AI – ��

Anthropic Launches Claude for Teachers to Influence America’s Classrooms

Lily Altavena — Wed, 15 Jul 2026 18:30:00 +0000

This article was originally published in

Anthropic, one of the world’s most prominent artificial intelligence companies, is launching a version of its AI-powered assistant Claude for teachers, entering a race by technology companies to infuse AI into education.

Anthropic boasts that its product can incorporate academic standards from all 50 states, and teachers can use it to help devise lesson plans, personalize instructional materials to students, and harness data to improve instruction, according to a company news release.

Claude for Teachers joins Google, OpenAI, and Khan Academy — among others — in marketing AI products specifically to K-12 educators. The new product launched Tuesday and is available for free for verified educators in the U.S. It will also be piloted in Detroit Public Schools Community District for a study on educator well-being and practice.

Claude’s formal arrival into the classroom comes during a complicated moment at the intersection of technology and education. The , the , and have all encouraged educators to adopt AI. But a is simultaneously gaining momentum, prompting some of the nation’s largest school districts to rethink how much time students spend in front of screens, as well as the contracts they’ve signed with huge players in ed tech.

Drew Bent, education lead for Anthropic, said that teachers using Claude’s educator product could, for example, pull in a student’s past assessment data and assignment data, along with past lesson plans, and ask Claude to build lesson plans for individual students based on that data — all while they’re sleeping.

In developing Claude for Teachers, Bent said Anthropic staff often heard that while teachers are already using AI to generate lesson plans, the plans generated were often detached from the content teachers actually needed to address. Anthropic’s tool will help teachers save time and toil less to improve student outcomes, he said.

“There’s a lot of evidence of what works well for teachers in terms of aligning with high-quality instructional materials, formative assessments, differentiated instruction,” he said. “But of course, if you have 30 students in your class, you’re not able to do all of that.”

Bent said Detroit was already using other Claude products, and in a “human-centric” way that impressed Anthropic, leading to the pilot program in the district that will start next school year. Anthropic will train teachers at a handful of schools in Claude for Teachers, and evaluate how the product may shape teaching practices in the district.

While AI companies have been eager to cement the technology’s status as a classroom staple, tech giants likely have a long way to go to quiet skeptics. Student-facing AI and so-called cognitive offloading, a reference to the reliance on AI to complete tasks instead of using critical thinking skills.

Bent emphasized that Anthropic is focusing largely on teachers, and that most K-12 students can’t access the company’s Claude assistant, due to an age restriction for anyone under 18.

Daniel Buck, a research fellow at the right-leaning think tank American Enterprise Institute, argued that if teachers outsource work to AI, “don’t be surprised when classroom community and academic outcomes rapidly deteriorate.”

Skeptics have also raised questions about student privacy in using AI, such as Claude.

Anthropic is working with the American Federation for Teachers on aligning the product’s privacy practices with what the labor union has said will become a “gold standard” in best practices around safety, according to the news release.

Among the privacy features in Claude for Teachers: It won’t use conversations between the AI assistant and teacher accounts to train its AI, student information will be protected in a manner built to comply with the federal law governing student privacy, and privacy terms of service are written without jargon so teachers can understand what they’re signing up to use.

“It’s important that Anthropic is committing to these principles in their new Claude for Teachers — a tool designed by and for educators to assist them instructionally and hopefully give them more time for the human relationships at the heart of learning,” wrote AFT President Randi Weingarten in the company’s press release.

Weingarten has been walking a tightrope when it comes to AI. She’s in elementary grades while promoting teacher training in the technology. Just a day before calling for the ban, she , an AI chatbot from Khan Academy, in action.

While AFT is working with OpenAI, Microsoft, and Anthropic on privacy standards and AI training for educators, it is notably not working with Google, .

Utah’s state education board recently made a deal with the tech giant , promising personalized instruction tools for educators.

It’s not yet clear whether one AI product reigns supreme in schools, but more teachers overall are using AI. Around 61% of teachers , compared with 32% in 2024.

Chalkbeat is a nonprofit news site covering educational change in public schools. This story was originally published by Chalkbeat. Sign up for their newsletters at .��

The End of Homework? Teachers Grapple With Cheating in the Age of AI

Greg Toppo — Tue, 14 Jul 2026 10:30:00 +0000

At the beginning of each new class, Al Rabanera lets his students know that he knows they’re using AI.

“I’m not going to pretend like you aren’t,” he tells his students. “I know it’s readily available for most of you, if not all of you.”

A math teacher at in Fullerton, California, where he works with students as old as 19 who are struggling to get enough credits to graduate, Rabanera has watched AI creep into homework assignments over the past few years as students use powerful tools like ChatGPT and Google’s Gemini to race through assignments.

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It has forced him to change his approach.

He has stopped sending home problems that can be lifted wholesale into an AI chatbot and pasted back into an assignment. Instead, he builds lessons around what students actually care about, creating, for instance, a unit on buying a car that weaves together calculating interest rates and monthly payments with learning about credit scores. He has replaced rote problem sets with one-of-a-kind poster projects and in-class design challenges.

When Rabanera assigns practice, he often has students devise their own word problems around personal interests to prove they understand the underlying concepts.

California math teacher Al Rabanera has replaced assigning rote problem sets with one-of-a-kind poster projects and in-class design challenges, among other assignments. (Courtesy of Al Rabanera)

He’s hardly the only one scrambling to try something new: Nationwide, teachers at all levels are rethinking, scaling back or, in some cases, abandoning homework altogether as evidence mounts that students who outsource their assignments to AI aren’t just submitting work that isn’t theirs. They’re surrendering the cognitive struggle that makes learning stick and makes homework, well, work.

New large-scale research suggests that teachers’ fears are valid. A led by Sina Rismanchian of the University of California, Irvine, analyzed 3.2 million student math problems on the digital platform over a decade and found that after ChatGPT’s release in late 2022, high school students spent 31% less time on word problems — the kind easily copy-pasted into an AI — compared with graph-based problems that required a hands-on interaction with the platform. College students showed a 27% decline.

When students were tested under proctored conditions with no access to AI, the copy-paste behavior vanished. And when researchers examined whether students had actually retained anything, they found that the odds of correctly answering AI-susceptible word problems fell by 25% in the post-ChatGPT years.

“Students are using AI a lot,” Rismanchian said in an interview. For those who do, “it’s coming at a cost for their learning outcomes.”

The College Board last fall that the percentage of high school students who said they use AI tools for schoolwork grew from 79% in January 2025 to 84% in May 2025.

In a , 37% of K-12 principals said students were using AI for homework help, slightly higher than the percentage who said students were using it to help draft essays.

John Singleton, an associate professor of economics at the University of Rochester and a co-author of the study, said the finding “certainly requires a rethinking of what the object of homework is.” For him, assigning short writing assignments to his college students is “insane these days, because you’re going to get back 25 AI-generated short essays, and so it’s really not gauging comprehension. It’s not even doing the work of forcing the student to engage with the material, because they can put it into the AI.”

Conscientious instructors are drafting AI policies that they post to class syllabi, he said, “but I think the temptation is there.”

Talking to colleagues, Singleton said, “Everyone feels sort of bewildered about whether they’re doing the right thing.” Moving toward presentations and oral exams make sense, but giving up traditional writing assignments as a way to assess student thinking, he said, “is too bad, in some ways.”

Everyone feels sort of bewildered about whether they're doing the right thing.
John Singleton, University of Rochester

The irony of this moment is that AI was supposed to offer students a , capable of explaining concepts, adapting to individual learners and helping them work through difficult material at their own pace. Instead, many educators say, for a significant share of students it has become the most efficient cheating device ever invented.

“There’s a zillion people that are trying to come up with these guided learning environments and Socratic tutors and stuff like that,” said Justin Reich, director of MIT’s Teaching Systems Lab and host of the AI-focused podcast “.” “And I’m just like, ‘Guys, you’re putting the “Carefully teach me this stuff” button directly next to the “Do everything for me” button.’ ”

Reich has spent years studying why students cheat. When they’re being honest, they typically tell researchers that the assignment didn’t seem worth their time, or that they ran out of time. They felt pressure to perform or, in many cases, they found themselves stuck on a problem with no other help in sight.

Guys, you're putting the ‘Carefully teach me this stuff’ button directly next to the ‘Do everything for me’ button.
Justin Reich, MIT

“What’s new now is that, with all the gen AI stuff, the cost of taking a shortcut is zero,” said Eric Cosyn, a researcher who co-founded the .

Ashley Kannan, who has taught eighth-grade U.S. history for 30 years in Oak Park, Illinois, said that if schools continue to go down the same path of assigning work and expecting students not to be tempted to take shortcuts, “the war is over — we’ve lost.” Classrooms, he said, will be left in “a race to see who can plagiarize and cheat the best, and who has the resources to do so,” a dynamic he calls a losing bet for everyone.

Start your homework in class

In interviews, many educators and researchers were quick to point out that AI didn’t invent academic dishonesty.

Denise Pope, a senior lecturer at Stanford’s Graduate School of Education and co-founder of , a research and school reform project, has been tracking student cheating behavior for two decades. Long before ChatGPT, she said, copying a classmate’s homework was consistently the most commonly admitted form of academic dishonesty.

The group’s latest academic integrity study, drawn from nearly 30,000 high school students, shows that this pattern still holds: 32.7% of students reported copying someone else’s homework at least once in the past month, a figure almost identical to the share who reported using AI as an unauthorized aid: 32.8%.

Students, Pope said, are simply swapping out one shortcut for another.

“This sort of hand-wringing that AI is changing homework like never before is a little bit off,” she said, “because there were high amounts of copying and cheating homework long before.”

Students are not having the productive struggle that they need to really learn the material.
Denise Pope, Stanford University

All the same, Pope’s team has surveyed more than 100,000 students since November 2022, and the results are unambiguous: They’re using AI to do homework. Many don’t frame it as cheating, making the case that consulting an AI is no different than asking a parent, calling a tutor or typing a question into Google. Echoing Reich’s findings, she noted, “Some of them are saying it’s another piece of technology that helps us when we’re stuck.”

Teachers, naturally, see it a bit differently. A Challenge Success survey of 678 faculty and staff members found that the most pervasive concern, raised by about 58% of respondents, wasn’t cheating itself but the erosion of critical thinking. Teachers complained that students aren’t developing the intellectual stamina that hard problems require. “If school is about skills and not content,” one teacher wrote, “ChatGPT takes away critical thinking skills at a time that we are supposed to be teaching those skills to the students.”

Pope said her group is hearing from teachers that they’re afraid to send homework home “because it’s even more clear that there’s this ‘Easy’ button, and students are not having the productive struggle that they need to really learn the material.”

She recommends rethinking homework, starting with what she calls a homework audit — a systematic review of assignments to ask whether they actually require a student to do the intellectual work required. Teachers should also be able to tell if the work was done by the student or by AI.

“Start your homework in class,” she advised. “You will get a really good picture of who understands what you’re asking and who doesn’t by looking around and seeing what happens in the first 10 minutes — one kid is done and one kid is still stuck.”

Kannan, the Illinois history teacher, has landed on a similar idea, built around conversations. He still assigns a version of the same paragraph students have long written to identify a historical figure and place them in context — but the process now unfolds through one-on-one conferences rather than solo writing. Because of the conferences, the writing looks different.

The goal, he said, is to locate the assignment “in the hearts and minds of a student” rather than in a generic prompt that AI can complete on command. “I think that there’s a way to personalize rigor,” he said. “When students, when young people feel that something is personal to them, they do come alive.”

Illinois history teacher Ashley Kannan says he now builds homework writing assignments around one-on-one conferences that help ground the writing “in the hearts and minds of a student” rather than in a generic prompt that AI can complete on command. (Courtesy of Ashley Kannan)

Kannan said this kind of individualization isn’t new — special education teachers and speech-language pathologists have practiced it for decades. “All I’m suggesting is that there are pathways that we know work. Why not bring it into the mainstream classroom for every student?”

‘How this could this stronger?’

Researchers are also grappling with the limits of what they can measure. Self-reported cheating data, as Rismanchian’s paper notes, is “the least reliable way to measure anything.” In his own earlier research on 70 undergraduates, more than half of students who were directly observed relying on AI denied using it. “We were actually observing this copy-pasting behavior,” he recalled.

A group of McGraw-Hill researchers co-authored the Rismanchian study, and Dylan Arena, the publisher’s chief of data science, said solving the AI cheating problem has two prongs: Better detection helps, but it’s insufficient without a cultural shift inside classrooms. Students who believe their sole obligation is to produce a completed assignment, he said, will always find the path of least resistance.

“There are kids who are here thinking, ‘I need to punch my ticket, I need to get this thing done,’ ” Arena said. “What’s the most expedient way to do that? Hand it over to this tool.”

But he suggested that something deeper is actually happening as AI colonizes students’ thinking: They’re losing their tolerance for “not knowing” something at any given moment, for what he calls the productive discomfort of sitting with a hard problem before the answer becomes clear. “It used to be that we would spend weeks not fully understanding something, and we would read, and we would write, and we would talk, and we would try, and we would write drafts — and they wouldn’t be quite right. But there is now an expectation that I either instantly know what I should do, or I should turn to a tool.”

A few teachers are experimenting with a more informal version of that transparency, built on relationships rather than documentation.

Kannan described pulling aside a student last year whom he suspected, based on months of conversation, of leaning on AI for most of his schoolwork. “I knew that because I was taking the time to talk to him,” Kannan said.

Rather than report the student, he asked him to run his own writing through the same chatbot he’d been using, in a bid to “reverse-engineer” and improve it. “Let’s actually ask questions to ChatGPT about how this could be stronger? How is this weak?”

Kannan has since built that move into his regular teaching: After students draft work with AI’s help, he sometimes has them ask the tool to critique the output, with students in effect “co-designing” assignments rather than simply handing them to a chatbot and turning in whatever pops out.

In the absence of such guidance, he said, students will do what they must to complete assignments. He recalled a student who’d been assigned an essay on the American dream by an English teacher. “There was really no instruction as to what that was, and she said, ‘I carried on a conversation with Gemini about what the American dream was, and that helped me understand it more.’ Given the demands of what the student was facing, she used AI as a partner, as an opportunity.”

, a longtime education researcher who has studied homework, noted that teachers have been assigning less homework for at least a decade — and that the rise of AI might reduce it further if teachers lose confidence that take‑home work is genuinely done by students. But it could also work the other way: If teachers believe that AI is a kind of all-purpose helper, they could actually assign more homework because they believe students “are going to be helped out and kind of semi-tutored,” he said.

Many school districts are trying to reframe the relationship between students and AI from the ground up. The Laguna Beach Unified School District in California, working with researchers at Stanford, found that AI policies had generated a culture of suspicion in which teachers spent their energy trying to catch cheaters — and students felt guilty for using AI.

Michael Morrison, the district’s former chief technology officer, called it “the absolute worst culture that I can think of.”

In response, the district developed an add-on tool for Google Docs called , which asks students to disclose exactly how much and in what ways they use AI on a given assignment — a kind of nutritional label for AI-assisted work. Surveys suggest students generally use it honestly because the tool gives them something they didn’t have before: a sanctioned way to tell the truth.

Michael Keller, Laguna’s director of social-emotional support, noted that the district is already rethinking homework altogether for high school students, since 70% are athletes who devote an hour or more each day to practice and training.

He sees the AI initiative as part of a broader commitment to treating students as full partners in their own learning. Monitoring how students are using AI, he said, is not the point. “We really view it as our moral obligation to make sure that we put trust and supportive relationships as the foundation to their learning experience,” he said.

For MIT’s Reich, transparency is necessary but not sufficient. The deeper challenge is motivational. “Kids are just natural boundary pushers,” he said.

And though they may not be able to articulate this, “the boundaries are what make them feel safe and loved and cared for.”

The problem, Reich argues, isn’t that students are weak-willed or morally deficient. It’s that the incentive structure of homework — grades for completion, not for thinking — has always been fragile. AI has simply exposed that fragility.

“It’s not like homework is perfect,” he said. “Any teacher will tell you that some of the assignments are dumb or don’t work. But across the tens of millions of minutes of stuff that we ask kids to do in the afternoons, some of it’s got to be useful. And if we turn that spigot off, there’s just going to be less learning.”

Finale: Takeaways from a Season of AI in Education

Michael B. Horn — Thu, 09 Jul 2026 16:30:00 +0000

Class Disrupted is an education podcast featuring author Michael Horn and Futre’s Diane Tavenner in conversation with educators, school leaders, students and other members of school communities as they investigate the challenges facing the education system in the aftermath of the pandemic — and where we should go from here. Find every episode by bookmarking our Class Disrupted page or subscribing on , or .

In the finale of Class Disrupted’s seventh season, Michael Horn and Diane Tavenner reflect on the season’s conversations about artificial intelligence — from how the tech is shaping school models to new tools. In a “no-holds-barred” conversation — they talked about how much of the education sector is still operating under the traditional model, the challenges of creating learning experiences outside of that model, and the tensions they see between innovation and entrenched systems. Throughout the conversation, they touch on issues like policy backlash against technology in schools, the value of outcome-based contracts, and whether microschools or low-cost private schools can actually drive large-scale change.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane.

Michael Horn: We’re in person.

Diane Tavenner: I know.

Michael Horn: Two episodes in a row.

Diane Tavenner: It’s amazing. For those who are only listening, we actually are sitting together here in Boston, which is super fun. It’s good to be on this coast with you. And for me, the best part of this is, I got to have family dinner last night at your home. And I woke up this morning thinking about it. I just felt really happy. It was joyful. I think you know that my favorite night of the week is family dinner night at my house.

Michael Horn: Yeah.

Diane Tavenner: And I hustle home, so I don’t miss it. And it was so fun to be with your girls and with Tracy. And I was thinking a lot about those few hours and, like, the learning that was happening at that table. I mean, we got to play this board game that your girls had, like, invented, and the dialogue and the conversation and the curiosity and that, you know, they were making their own lunches, which called back to, like, Rhett cooking.

Michael Horn: Yeah. But he was doing that from a young age. I remember…

Diane Tavenner: Yeah, yeah, yeah.

Michael Horn: Preparing meals and shopping and everything.

Diane Tavenner: Totally. And so that was really fun. And to be back in that sort of younger age, because mine are older. I should say, the thing I kept thinking is, like, this is what learning is, and what if this is what it felt like? I wish everyone could have this experience.

And yeah. So that was good.

Michael Horn: No, it’s really cool. I will say. We also then had a conversation this morning about … She was like, so what night is family dinner night in the Tavenner household? And should we think about creating a similar structure for our girls? So you being there also caused us … because I think it’s intentionality, also.

Diane Tavenner: It is.

Michael Horn: Right. And that intentionality we both brought to raising our kids and now thinking about reinventing schools. And I think it was interesting because in our previous episode, we were live as well.

Diane Tavenner: Right.

Michael Horn: We were at the ASU-GSV Summit with Reed Hastings. And it was interesting to hear Reed, I think, for the first time in my memory, and I think that’s what he talked about. We actually need to reinvent the classroom model, the school model itself. We can’t just be tinkering, if you will, toward utopia or layering over here and whatever else. That’s obviously the tune we’ve been singing for most of our professional careers.

Diane Tavenner: Right. But it was fun. It was exciting to hear him talk about that and reflect on the work that he’s done in different, you know, elements of education. And to really start to zero in. We have to work beyond just the classroom if we really want to transform the experience for young people. And so, I mean, I guess it always feels good to be confirmed and validated.

Michael Horn: Sure. We feel like geniuses now.

Reflecting on mid-season insights

Diane Tavenner: Yeah. So I think our plan for this today is a callback to mid season, where we did this in person also kind of mid season. Oh my gosh, we’re learning so much. Let’s get together and just reflect and process and talk about what we’re learning after that first half of the season where we were really talking to sort of thought leaders and people who are thinking big picture and who understand AI and in AI, and so we thought we’d do that again, where the back half of our big AI exploration season has really been with practitioners, I would say. And so what are they? What are we making? We had all these really fascinating conversations. This is our first time to really sit down and, like, sort through them and process them and understand what we heard. And then we’ve also been thinking about next season, and so we’ll share a little bit of that

Michael Horn: Tease a little bit. It’s been interesting, and I’m glad. Process is the word that I’ve been thinking about because I think when people appear on our podcast, this sounds silly, but it’s not an endorsement of their model or their tool or whatever it is by us. It’s a genuine learning opportunity for us. And obviously we were both interested in new school models.

Diane Tavenner: Right.

Michael Horn: Which was three episodes that we had. And then we were interested in the ed tech tools that are coming up and using AI in different ways. And we were trying to process. We had this framework that you introduced in the last time we were in person in Cambridge or in Boston about the sort of horizon one, horizon two, horizon three, schooling models, and the way I think we’ve sort of categorized it, there was H1 is sort of the industrial model that we know, and you had H3 as sort of this post industrial model. And H2 is sort of this hybrid that is elements of both, maybe, and so forth.

Diane Tavenner: I love that metaphor of the steamship that is outfitted with sails still because it has to be running both because steam is not reliable enough. You can’t really get all the functionality you need and trust it. So you have to still bring the H1, if you will, into it. I’m glad you brought up the framework because that really shaped how I saw the second half of the season and had all those conversations. I’ve actually been thinking a lot about that framework and how my understanding of it is sort of, I don’t know, maybe becoming a little bit more nuanced as we talk with people and think about these different models. And so that might be a place to start.

Michael Horn: Yeah, I think let’s dig into that because. And you can take it wherever you want. I think it’s a framework that I have found both useful, and I’ve been struggling with, like, what is this H3 really gonna look like? Where does it really depart from the grammar of schooling as we’ve known it? And do we really think we’re gonna actually get there? Or are there gonna always be this sort of H2 pulling us some of the elements. And then I guess I’ll say the last thing, which will probably come up as we talk about the school models that we had on the podcast. Where, you know, where are parents in all this? Like, where is their headspace? ‘Cause, like, what we want, where parents are, where schools are. And I, for those that can’t see my hand gestures, I actually think that’s the continuum at the moment. Like historically, I think parents were more conservative than the schools, I think that may have flipped.

Diane Tavenner: Well, and I think there’s some evidence of that with the growth in microschools and sort of alternatives like, people really sort of self bundling, if you will, you know the homeschooling.

Michael Horn: I was shocked by the way from the homeschooling I read this morning in ��. 10 million, I know. 10 million families. They reported from this RAND survey that Johns Hopkins had done. I think it was Angela had done there. And that’s a big number.

Diane Tavenner: It’s a big number. And I think it speaks to this idea of where are parents and I agree with you, they’ve historically been more conservative. I mean, I wrote prepared to try to, you know, I call it a love letter to parents, like to introduce them to some of the ideas of what’s possible. Because my, my feeling was like they want one thing for their kids, but they don’t know how to get it. And so they kind of double down on, you know, the existing model, if you will. I actually think we’re starting to see, at least for some, a break from that. And not just a small amount, but, you know, a growing.

Michael Horn: In some cases quite big. Yeah, in some cases there’s, there’s steps and I actually think that comes up. I don’t know if you want to stay on the H1, H3 or go into the model.

Diane Tavenner: I think what I would say is just a couple of reflections and observations, how I’ve been using that framework in real life. So, one, I find that it is helping me be engaged in conversations with educators and developers. All the people who are in, who care about this work and who are working in it, who are thinking about AI in that if I can just set the framework out up front and say, look, like my passion is developing the post industrial model and H3, and honestly I don’t think I’ve seen it yet, and I don’t think it exists yet, and that’s not a bad thing. That’s fine. But let’s not pretend that certain things are H3 or that they are a real new model. We might have that conversation in the models conversation when I don’t think they are. And you brought this up the last time we talked, like, we also need tons of kids are in H1, they’re going to be in H1 for a while.

How do we use AI to make those experiences better? So I think when I’m able to say to people which conversation are we having? Where are we working right now? And the key for me has been how do we not constrain or compromise work in developing the post industrial model or the H3 place by working on H1? And that is a real risk, I think, because, I mean, there’s so many, like, elements of H1 policy and norms and practices and tools that just codify and solidify that model and then therefore prevent unintentionally the invention of new models.

Michael Horn: And we talked about that some in the midseason in terms of you named assessment and special education in particular in those. I think there’s a lot more and frankly —

Diane Tavenner: I’m going to add a new one.

Michael Horn: Since we recorded, I think we have seen the growth, the fastest growth in my memory of a series of policies around screen time. Cell phones were already emerging but now into screen time in general that I don’t love but well intentioned in H1 that are like diametrically opposed to — I won’t call them H3, but I will say, like, these new models of schooling that are appearing by education entrepreneurs in sort of this H2, H3 nebulous area.

College and career readiness concerns

Diane Tavenner: Right. I couldn’t agree more. The other one that I would add is the place that I’m working every day right now. So let’s call it the sort of college and career counseling and readiness world, if you will. And it’s just so clear to me that, you know, the goals in that area by the vast majority of people are to improve college and career counseling and readiness in the H1 model. And I keep saying, like, if we’re successful at that, don’t we just codify this entire system? Don’t we reinforce it and bolster it and prevent ourselves from moving to what I believe is and certainly what we’re trying to do at Futre, which is life navigation, whole human development, like enabling young people to emerge into a life of flourishing, to launch into a fulfilling life, not just get accepted to college or you know, take that next sort of educational step if you will, which is what is happening right now, I think, in H1. And so just the, the — I don’t think I’ve convinced a lot of people of that.

Michael Horn: Yeah, so. So it’s interesting. I think it is a risk, one, but — so I agree with you, no surprise. But two, I’m going to take a theory that we use in my class and from the Clay Christensen playbook, if you will, value networks, and Tom Arnett has been writing a lot about this at the Christensen Institute. And basically the way I’ll say it is this.

Let’s use an analogy. When RCA and these large vacuum tube consumer electronics products, they got disrupted by Sony and these transistor LED products, it wasn’t just Sony replacing RCA, it was also all of the component suppliers of RCA got disrupted by a whole new batch of component suppliers because they were completely plug-in compatible with the old and vice versa. And then it wasn’t just that, it was also retail got completely disrupted. So RCA sold through appliance stores that made their money not by selling RCA products but by repairing.

Diane Tavenner: Of course.

Michael Horn: Right. Because these vacuum pipes would blow out. Sony got sold in discount retail. Target, Walmart, Kmart, all birthed in 1962. And so the shorthand that we used to say at Entangled, where I spent some time, was systems disrupt systems. And so in some ways, like, you’re building the new system now. I will tell you, the struggle I have when I teach in the class, which is we use a few school networks as like different, different versions of this. So we have Virtual Learning Academy, Charter School in New Hampshire, Big Picture Learning.

Like a few of these that Tom has written some case studies on, and every single one of them at some point pull back into the existing system and like, Tom’s answer to this is always, like, well yes, we inhabit Earth.

Diane Tavenner: Yeah, it’s the gravitational pull right?

Michael Horn: Like we are like, we are not completely free on Mars yet. Sorry, Elon. But like, so where is the limit of exchange of these systems and where are crosswalks not distorting these new things and where are they, to your point, actually codifying like grooves on a train track that are impossible to escape.

Diane Tavenner: So that’s an interesting segue into talking about the three models that we talked about, I think during the season. Because, you know, we specifically wanted to talk to Alpha School because like, everyone was talking about Alpha School. There was a lot of sort of PR and marketing out there about them. But we kind of wanted to really get under the hood and understand, you know, was this new. A new invention or not? And what was going on. We wanted to talk to John Danner, who’s the co-founder of Rocketship and now starting Flourish, literally, you know, AI Native designed as an AI Native model. So what does that mean?

Michael Horn: By the way, he is the only one that’s actually AI Native.

Diane Tavenner: Correct.

Michael Horn: Think about it. Because Alpha dates back to 2014 or whatever.

Diane Tavenner: Yeah.

Michael Horn: And then Summit, of course.

Diane Tavenner: And then we talked to Summit about, you know, their vision to create the next generation of model, which would be an AI Native. So I’m curious.

Michael Horn: Play, you. Go ahead, go ahead, ask your question. Ask your question.

Diane Tavenner: Well, what’s your impression of those three? I mean, how would, how did you come away from, you know, what were you believing that they were actually inventing Horizon 3, AI Native, you know what is your takeaway?

Discussing H3 framework development

Michael Horn: Yeah, it’s a good question. I think this is where I’m struggling with the framework because I still can’t completely imagine all the features of H3. And so, I think, let’s tease this for next year’s. I think we should like construct a little bit more clarity around what really is H3 and what are the distinguishing characteristics? Because I think, in my world, I sort of, like, I don’t know, Montessori feels like it has a bunch of these. And if I look at the Alpha model, I actually see some tiebacks to Montessori. I see some tiebacks, ironically, to big picture learning and things like that. Let’s put that aside for a moment. In some ways they feel like the closest, sort of like pulling from both sides of it in very clear ways.

Flourish to me felt like the closest to what I might imagine H3 in a middle school.

Diane Tavenner: Yeah, I think it has the potential.

Michael Horn: We don’t know yet, right? And I appreciated John’s honesty with where they are. And then I had this — I’ll tell you my take on Summit, let’s start there. Yeah. Because that’s your baby.

And you asked this question before we recorded. So no one knows you asked me this question, but you said, like, I’m wondering how you’re going to respond and react to this. Because in your mental model, Michael, to disrupt yourself, you need to have a completely separate, you know, et cetera, et cetera. And then, like, the old migrates out to the new, and that’s not what they’re doing. So I had this thought though, as I finished. ‘Cause I didn’t know what to expect. I came in with it a little bit, like, hackles up a little bit. And then when we finished it, my reflection was this, which is when there’s another way to survive.

Disruptive innovation that we don’t talk about that often, which is because I think it’s less proven. But the way is you are so clear on the job to be done that you do your mission, what you don’t do, your priorities, what you don’t prioritize, et cetera. That when new technologies come in or new features, you can swap them out very cleanly. Because everyone in the organization understands this is what we do.

Diane Tavenner: Interesting.

Michael Horn: So let me give you an example outside of an industry, and then you can — I’m going to let you fill in the blanks from it. So there’s this retail store that came in at the quote unquote “low end” that has never gone up market and never been disrupted by online retail. And it’s called IKEA. Probably know it.

Diane Tavenner: My gosh, my children love it.

Michael Horn: Right. And IKEA, we have become convinced they do one job to be done really well, which is, I need to furnish this apartment today.

Diane Tavenner: Yeah.

Michael Horn: Like I — yeah. And they’re not selling heirloom furniture. They’re not trying to go high end. Nor are they threatened, though, by discount furniture or Wayfair or any of these folks. Because the job to be done is so distinct and clear. And they lay out the entire store like you’re walking around the apartment, and you fuel up midway through Swedish meatballs, right?

Like. And so a new technology comes along and their simple question always is, does it help us get the job to be done?

Diane Tavenner: Yeah.

Michael Horn: Accomplish it better, or is it irrelevant to us? And if so — so take China, for example.

Diane Tavenner: Do they ever ask if that job still needs to be done?

Michael Horn: That’s probably a good question, but I think you probably have to.

Diane Tavenner: I think that they probably keep finding that job keeps needing to be done.

Michael Horn: I think it’s still relevant. And they probably say the market’s pretty big.

Diane Tavenner: Right.

Michael Horn: It’s a good question. But so in China, it’s interesting. Like, in the U.S., we drive large cars, so we go. We pick out the furniture with the stickers, and we go down to the bottom thing and we pick off these flat carts, and we put them on that cart and we check out and we drive off in our cars and we assemble them really nicely. In China, they don’t have big cars. And so IKEA will deliver it to your house same day.

Diane Tavenner: Which is key.

Michael Horn: Super integrated around the job, and they can make those sorts of decisions because, like, does it help us get the job to be done or not? Context changes fine.

Diane Tavenner: Right.

Michael Horn: We just know what our true north is.

Diane Tavenner: Such clarity.

Michael Horn: Such clarity, right? And so it prevents them from going up market and specificity. Right? And you can imagine all the ways that trickles down, I think. And so my read when we were listening to Summit was like, now, you could argue your mission changed, right? When you moved away from college. But I don’t say, not really.

I would say it was more. You actually were really clarifying what it meant to educate a prepared human.

Diane Tavenner: I agree. I agree. Sharpening, clarifying.

Michael Horn: Sharpening, clarifying the metrics

Diane Tavenner: And talking about it in a way that was more resident.

Michael Horn: Yeah, to the families you served

Diane Tavenner: Yeah.

Michael Horn: So I guess my point is, like, when I heard what y’ all are or what Summit is doing.

Diane Tavenner: Not me, the team at Summit, big fan.

Refining educational processes with AI

Michael Horn: Katie and Dan and the whole team at Summit, big fan. They, like, they see, you know, Summit learning got ripped out, and they’re like, okay, we can design this new technology with AI or partner or whatever it is to accomplish what is the school that Dan walked us through very helpfully, I thought, on that episode, and, like, they could reinvent expeditions. But again, it’s not changing the DNA and the mission and clarity of the place. It’s more like, how do we sharpen the processes and better get them done right now with the tools available. I think that’s very possible

Diane Tavenner: Without being disrupted and to be very relevant. And the adoption of that and that technology.

Michael Horn: Job to be done is still, like, the prepared thing has not changed.

Diane Tavenner: No, it has. If any, it’s maybe more so, more relevant. Oh, that’s such an interesting.

Michael Horn: Here’s where we get to say we didn’t tell each other our talking points.

Diane Tavenner: We literally wait to talk about these things. That also brings me probably back to Alpha, because using that lens, I’m trying to think if I feel like Alpha has that level of specificity and clarity in a job to be done. Or if they dont?

Michael Horn: I’m not sure they do.

Diane Tavenner: I don’t think they do? Right?

Michael Horn: I’m not sure.

Diane Tavenner: Yeah. Because their model feels a little bit more like this. Like you said, there’s a little piece from here and a little piece from there and a little piece from there, and they’re kind of stuck together. But it’s not clear what they’re adding up to, necessarily.

Michael Horn: I don’t disagree with that. To me, not just from our episode, but in subsequent conversations and things of that nature, what I’ve taken away from Alpha … Okay, let me say it differently. I think the thing that probably bothers you the most is the disconnection between the morning academics and then the life skills learned in the afternoon, is my guess.

Diane Tavenner: For sure. Just this sort of absence of intentionality. And, like, I just don’t understand how you have.

Michael Horn: How you have been separated.

Diane Tavenner: Yeah.

Michael Horn: And so my takeaway is I don’t disagree. For my kiddos, I would agree as well. And I also think relative to the current system …

Diane Tavenner: Yeah.

Michael Horn: It’s still significantly better than what most families would be getting on both dimensions, I think.

Diane Tavenner: Yeah. And then you have to talk about ROI. Like, is it $75,000 better?

Michael Horn: Well, so the the price point, let’s actually talk about that. ‘Cause I agree with that.

Diane Tavenner: Yeah.

Michael Horn: And it’s not what I would price it at, but I also cynically understand pricing at that high. This is a higher education thing that I hate. Right. I’d always. You know, Western Governor’s University is not seen as a prestigious university because they charge $6,000 or whatever, but they’re amazing at their job to be done. But the game is, like, if you come in at this high price point, in the absence of metrics that people universally really can understand and agree on, they equate price to quality.

Diane Tavenner: Yeah. It’s a luxury brand. It’s a luxury good. And so they burst onto the scene and sort of signal to people with lots of money they did break.

Michael Horn: I mean, we’ve been frustrated. We haven’t been able to break into the conversation. We said that up front in the two hours. I think that’s been a way for them actually to do it. And so I’ve sort of like, I’m living in this dual world where I’m like, it’s not how I would do it. It’s not. And I get it. Like, I get what it’s accomplished for them.

And there are many parents for whom like they’re really desperate for. They’re running toward it or maybe running away from something. And that’s more I think, the truth.

Parents exploring educational alternatives

Diane Tavenner: Those are the conversations I’ve had with parents, you know, when parents are running away from something to a variety of different choices depending what’s on the table for them. Which again, I always take as feedback as someone who wants to be in the system and serving the majority of young people. I’m like, huh, you gotta pay attention to that when people are running from what you’re offering and you know, where are we? Where are we not meeting their needs? And really, to me, this is what it means to co-design and work with communities to create this public good, if you will. The other thing that came up for me in the models — and this is on Flourish, but it’s come up a lot this year. And I’m curious what your thought is. You just talked about how the conventional wisdom is that, you know, to disrupt yourself you have to sort of, you know, wall off that new innovation, which was certainly the approach I took when I was leading Summit. I’m glad to know there’s another pathway. I think I talk to a lot of people right now who view microschools as that possibility.

I think that’s what they’re thinking. So by people I mean, you know, whether it be school districts who are thinking about starting microschools, where they are going to do their innovation there, charter school networks who are thinking about doing their innovation there. Certainly Dan. Or yeah, John. John Danner is rethinking about, you know, starting as a microschool. And he talked about how he thought that the scaling there would be easier and whatnot. I’m skeptical of the microschool as the place where you can actually design a new model.

And the reason that I’m skeptical and I’m curious what you, I actually don’t think at the micro level you work through so many of the elements and principles and systems of an actual model. Like, I just can’t imagine serving 50 million kids a year in microschools. Like, I just don’t think that adds up to a system of educating the number of young people we need to educate. But I could be totally wrong.

Michael Horn: Yeah.

Diane Tavenner: And if that’s the case, like, how do you make the — ? What do you actually learn and develop in a microschool that is transferable to a larger school or a larger system or a larger model? I feel like there’s a lot of gaps there.

Michael Horn: That’s interesting. A lot of thoughts, so we’ll go through them. So, one, and then push back where you — because I don’t — so on the district and charters using microschools, that I would say is promising, but I’m unconvinced. And the reason I’m not convinced is I still don’t see the mechanism.

Let me say it this way. Districts for years have had alternative schools to serve, people who dropped out of the system. Great area of non-consumption. I’ve written a lot about how I thought it could be a disruptive thing. But the thing that they don’t do is disruption, has to grow, and people have to migrate out to it. And no one migrates out to it.

Diane Tavenner: No.

Michael Horn: Right? And I think there’s a lot of reasons for that. But I think part of it is the business model. The charters are a little different, but the business model district is the district, it’s not the schools.

Diane Tavenner: Correct.

Michael Horn: And so schools take on the thing of pilots.

Diane Tavenner: That’s a bigger system. And I’m like, how does that microschool in any way help us understand and transform the system?

Michael Horn: Yeah — no. And so I’m — I don’t know that the dual — I am convinced that the dual transformation strategy works there, even though I’ve recommended districts do this because I don’t see another avenue for them. Let’s leave aside charters for a second. I guess I would push back on the — well, maybe I would relabel it from microschools.

I’m moving away from that phrase because I think “micro” implies small. And if I look at what I think the most promising disruptive innovations in this new schooling space are, I don’t think the feature is necessarily small.

Diane Tavenner: The size.

Michael Horn: Yeah. I think the feature is that it’s a low-cost private school. And so, like, Flourish may end. It’s small at the moment, but it may end up being several hundred students. I could imagine maybe John would say no.

Diane Tavenner: Yeah.

Michael Horn: Con Lab school was originally a microschool. It’s like 300 kids or something like that.

Diane Tavenner: Right.

Michael Horn: So it’s growing. You wouldn’t call that micro anymore.

Diane Tavenner: I’m not trying to call it low cost either.

Michael Horn: Well, that’s fair. That’s fair also. Right? So — but I guess the point being, that’s a good push, by the way. But the — I guess I am somewhat optimistic still that like we’ll have hundreds and thousands of these different schools, and some of them will be chains, and some of them will be sort of your local coffee shops. Sort of.

Challenges of educational system changes

Michael Horn: If you think about the mix, and I do think families are potentially going to move out of the district system into these new things. Now I have two thoughts on what you’re saying, which is, like, how do you do all these people like the size and complexity? And I guess I have a couple thoughts. One, I think we don’t. I don’t think we can know the answer to that until they start to grow in size and complexity and solve the next problem. And there’s a tendency of people in education to want to design the system top down.

Diane Tavenner: That’s so true.

Michael Horn: And I don’t think we’re going to. I don’t think that’s how it’s going to work. So that’s one. But the second one is, I think I can imagine a lot of families in more bespoke smaller communities like these 10. I mean, if it’s truly 10 million of 50, like that’s already 20% that are DIYing. And is the future system 100%? I doubt it. But it could be 70.

Diane Tavenner: What’s interesting to me about that is the DIYing. And this was the point of that article this morning, is literally people. It felt like summer to me when I was like organizing all these summer camps and these activities. And so these parents are like assembling and essentially duct taping together. The, you know, the microschool part tending to be more of like online digitally driven learning.

Michael Horn: Or it’s like there are two days a week there, real world experience. The set of three things.

Diane Tavenner: Yeah. And so in my mind I’m like, okay, that’s a parent sort of cobbling all those things together. But if you’re a microschool, that the parents are going to you because they’re expecting you as a school to put all those things together. How do you do that? If it really is about small, how do you have all those real world experiences that we’re imagining for a small number of kids? I just don’t understand how it works.

Michael Horn: No. And this is where I think we agree, which is, I personally think, the upmarket trajectory of the sector because disruption comes at the low end. It doesn’t serve most people and has to serve more complicated use cases, which is what I think you’re describing. And so I don’t know what that looks like. But my current thinking is maybe all these microschools plug into what’s a community center and it’s actually kind of permeable between them. I think your argument would be H3 is actually the operating system underlying how these all connect to each other, which we haven’t created yet.

And then the second thought I have, also, is frankly Odyssey and Class Wallet and all these things that are trying to direct education savings accounts. I think they have to reduce the complexity of stitching this together. And it’s almost like you used to say this when you were in COVID, operating Summit. You had, like, the different flavors or — right? You go up to this — the — the sub shop, and, like, the —

Diane Tavenner: The menu item.

Michael Horn: Yeah, exactly.

Diane Tavenner: The ordering, the sandwich.

Michael Horn: But I kind of think that’s what we see start to, like, take the coordination out of it.

Diane Tavenner: Right.

Michael Horn: Because my orders aren’t going to be —

Diane Tavenner: I can order the signature sandwich where there’s no substitutions. But I know that I’m getting like the Santa Cruz that has turkey and avocado.

Michael Horn: Right. And most families are not going to be able to do. And when they do, it’s not going to taste very good, to use your analogy.

Diane Tavenner: To make their own.

Michael Horn: Right. Like that’s the other part of the innovation we don’t, haven’t seen yet. And so sort of my evolution in thinking has been it’s the coordinating infrastructure. It’s like the different reassembled parts that coordinate. Like maybe it’s hop, skip, drive as part of this ecosystem. For those that don’t know, that’s a sort of disruptive to the bus or car driving

Diane Tavenner: Uber of busing, essentially.

Michael Horn: Exactly. Right. And — and like all these things, maybe this new system and future I would think would be part of it.

Diane Tavenner: Yeah.

Michael Horn: And anyway. But I almost think the way that happens is like, we’re gonna just have to solve each next problem.

Diane Tavenner: Yeah.

Michael Horn: To try to take more like more students and more families.

Rethinking microschool education

Diane Tavenner: And I think that comes back to this idea that I guess maybe you’re helping me realize that I’ve been sort of experiencing the microschool as like people who are like, oh, we’re gonna reinvent education in the classroom. Like, like individual classroom teachers are going to reinvent education. And so microschools were starting to feel like that to me. I’m like, no, we’re not going to reinvent this system. So what you’re helping me see is like, yeah, we have to like effectively scale though, if we want them to be micro, if it is about small, or we need to understand the principles and then figure out how they all kind of coordinate and work together to scale up the system. Because the system is the actual. And if you think about it, the industrial model, most people don’t go to their school and think this is like a factory because the factory is the underlying infrastructure of how everything runs.

Michael Horn: That is the operating system.

Diane Tavenner: That is the operating system. And I think what I want and what you want is a post-industrial operating system that enables and allows the experience we had at family dinner last night, like that type of learning. And it counts and it has all. And so, will some of the features of those schools look like the ones we see today? Of course. Of course they will, because there’s amazing things that happen today, and those of course get pulled into that. But it’s that underlying infrastructure that really transforms, I think, everything.

Michael Horn: I think that’s right. I sort of want to jump into the tools. We probably have a couple things we want to pull back onto the schools on. But we can go back for a long time. We can iterate. But the — so it’s interesting, I think Timely, interestingly enough. Right?

Diane Tavenner: So this was our conversation with Paymon.

Michael Horn: Paymon. Yeah. We should say this to remind people

Diane Tavenner: Who was working on how do you make a better master schedule? And I think what I agree with him completely as someone who was fanatical about the master schedule that embodies all of your values and your resources, your master schedule does. And yeah, so he’s really trying to make that better.

Michael Horn: Yeah. And he is clearly designing in the H1. But I think the takeaway I had from it is one thing AI can be really good at is simplifying a lot of these logistics and time and getting the right experience at the right time for the kids who maybe can’t. Their family or guardians or whatever it is can’t line that up for you. And like so when you think about this operating system of, of industrial model, we move from class to class. It’s all there in the building. It’s not outside of the building to something that is much more porous and flexible and so forth. My thought from the takeaway from that conversation was wow, AI could be incredibly helpful at this coordination logistics, pulling in resources truly from the community and each kid probably having different mixes of that.

Diane Tavenner: Yeah.

Michael Horn: Right. And I’ll stay with my community center model just idea for a second, like multiple microschools, if you will, or whatever we want to call them, plugging in is you can almost imagine a parent saying like, I want to opt out of parts of this totally because I will manage this. And another parent being like — or, you know, having a conversation where like, please help.

Diane Tavenner: Right.

Michael Horn: And you being like okay, we’re going to line up three amazing outside of school experiences for you and then you’re going to jump into this learning experience here. But then like, you’re going to do this project at this other one here because like we’re hearing what your goals are and where your struggles are and, like, the questions, and we’re by the way seeing you could use some exposure to this, Steve, and understand is this something you hate or like or et cetera.

Diane Tavenner: And I think what you’re doing right now is sort of starting to illustrate the difference between Industrial model Horizon one. So Paymon and Timely are working on a master schedule that’s still within the confines of a building. You know, a five day week schedule that’s kind of eight to three, that’s classrooms, you know, I call it the egg crates. It’s like 1 to 25, like all these, you know, so that’s what he’s doing. But now you’re talking about in my mind a new operating system that would greatly sort of expand beyond those boxes, those little egg crate boxes to all the possibilities that could be practically now assembled and personalized for families and kids. And so that kind of would be the H3 version, I think of what Timely would do, don’t you think?

Michael Horn: I’m learning here already. No, I think I’m 100% and I’m getting clarity now. Okay.

Diane Tavenner: And so I think this is a perfect example of like, yes, Paymon, go please. Like there’s, like another school year is coming and please can we get off the magnetic whiteboards where people are still like by hand trying to create master schedules which we know is not going to deliver and do use AI to do that significantly better, significantly faster in a significantly more personalized way. So I thought that was an exciting tool for that reason and it provides that clarity, we just said. I think that. Yeah, I’m curious.

Michael Horn: OK, where are you gonna go?

Diane Tavenner: I can’t decide where to go.

Michael Horn: That’s OK.

Diane Tavenner: I can’t decide because — well, let’s just remind people of the other folks we talk to. So we talked to Dicia Toll, who is working on CourseMojo, which is the ELA sort of in classroom experience. And I think I would kind of bucket her, that conversation with the one we had with Matt Pasternak who is, is at Once there. He’s working on reading younger kids and how they read, starting very sort of human based and then bringing in AI to support. And if we remember, he’s doing really interesting things of how you’re using all the people resources.

Michael Horn: Giving really cool experiential opportunities potentially for high school.

Defining a school’s basic elements

Diane Tavenner: Right, right. Which takes me back as you were talking a minute ago and I’m thinking back to Dan Efflin’s description of at the most stripped down basic level, what is a school? And I love this framing and I think about it all the time now, which is like, it’s literally a set of, it’s a group of students, it’s a group of young people. It is a set of objectives or outcomes that those young people are trying to reach. And it’s a bundle of resources we have to help them do that. At the end of the day, that’s the most stripped down version. And I think Once is super interesting in how they’re thinking about the potential resources, high school kids, you know, instructed to teach young kids in the virtuous cycle that’s sort of created there.

Michael Horn: I just had this thought I hadn’t had it before, but there’s like a. Both a very cool career potential. Right. Of teaching for those kids also, frankly though, if they don’t do that, because I can imagine some people being like, oh, this is like very self determinative or whatever. And I don’t know how I feel about that. I actually think it’s also a very cool, like, parenting 101 opportunity, if you will, for kids to get experience with younger kids and learn about the science of reading and, like, some of the motivation stuff that I’m sure Matt is baking in, things not to do and so forth.

Diane Tavenner: And I would add a third is the entrepreneurial nature of this. So I talked to him about how could high school kids create summer businesses where they’re using once to tutor kids in their neighborhood and set up like little entrepreneurial ventures which he’s super excited about and open to. And so that type of creative thinking about, you know, recognizing and seeing high school kids as resources to help anyone learn but other especially younger kids to learn such a, I just, I love that kind of thinking. I wish it were just across the system in so many different ways.

Michael Horn: Totally agree.

Diane Tavenner: Yeah. One thing that we have talked about that I think is worth bringing up here is we both noted with both of those conversations how these are not, these have required significant human expertise to create these products sort of in collaboration with AI, if you will.

Michael Horn: That’s a good way to put it.

Diane Tavenner: You know, and I think Dacia did a good job of really walking us through like the level of intentionality and intervention and human expertise that has gone into creating those really cool, important, rigorous experiences in the classroom. So I’m curious like what you think about that and how you process that.

Michael Horn: So I agree. I will tell, I’m just gonna be honest. I left the Dacia conversation glowing for like 10 hours afterwards. I was so excited. And I think a large part of it was because of what you just described, with the intentionality of what is great, not just like good, great practice looks like.

Diane Tavenner: Yes.

Michael Horn: And let’s recognize that like, like 80 plus percent of teachers don’t have the training or background or can’t time or whatever.

Diane Tavenner: Like, what she described happening in that classroom is humanly impossible to do. I don’t care how good you are.

Michael Horn: Totally agree, right? But like, even if you were one on one or one on two, that’s fair. Like, a lot of teachers still don’t have that background.

Diane Tavenner: True.

Michael Horn: Right. And she’s looking at the best practitioners to think about the next best question with the text. Like really deep in the text. And that’s something I think that often comes up also right is you can’t just do this in a generalized way. Like actually really digging in is important to create this instructional tool. So impressive.

I also really appreciated how she was like, look in your framework of H1 through, like she engaged in that framework and was like, I’m on the H1, maybe H2 part of this. I found that really helpful because, like, it immediately, it’s like, this is where I’m sitting. But by the way, I can easily imagine what she’s built being part of an H3 set of offerings as well.

Rethinking traditional classroom model

Diane Tavenner: I think that’s right. And so let’s go there. Because my current favorite provocation, I’m probably driving a lot of people crazy with this is what if? And what I asked them to do is pretend for a minute that we can never ever, ever again have classrooms with one teacher, 25 kids, five days a week, 50 minute periods. I don’t even care if you have a block schedule every other day for, you know, 90 minutes. We can never do that again. Imagine we can never ever do that again. How are kids going to learn? Can we like expand our imagination how they can learn? And the reason I’m provoking this is I loved everything Dacia said and agreed with you. I left sad because of the comment where she was like, yeah, they’re doing it a couple days a week.

Michael Horn: Oh, interesting.

Diane Tavenner: And I’m like, why? Just we’re going back to the 1 in 25 egg crate to do all these other things and we’re just doing that. Why? Why aren’t we doing that more? Why aren’t we doing that really strategically or what? Like, I don’t understand, you know, this. Yeah. How we’re thinking about that. And so that felt depressing to me.

Michael Horn: Yeah, yeah, I hear that. I think it speaks to something different that we were going to hold on, but I’m going to go there now because you’ve been forwarding me pieces around the adoption of AI in the workplace.

Diane Tavenner: Yes.

Michael Horn: And Reed used the analogy that we’ve both loved for some time that Ethan Mollick at Penn uses, which is like thinking about AI like electricity. And this sort of, there was the paradox. Right. Electricity got introduced. It didn’t actually increase productivity until you redesigned the business models and factory models, models around it so you could distribute, as opposed to have the central drive shaft. And I think the same thing is playing out right now in industry, which is like AI, more of a cost ad, maybe marginally product, you know, productivity, particularly for coders, but maybe not elsewhere. And large parts of enterprises are frankly not using it to its theoretical capacity.

Diane Tavenner: Right.

Michael Horn: And I think the same thing may be true in schools right now as well, with. Well, maybe this will get us into Magic School. Unless the tool fits the workflow exactly as you’ve currently designed. And like Dacia’s thing, it does. Like, you do need to rethink use of time and space. Right. And like, what. And activities and instructional plans and coherence.

And like, it raises all these questions that are problems in the current model.

Diane Tavenner: Correct. Correct. You’re right. I have been forwarding you lots of things because, you know, I love to do this. I love to go and look out into other industries because it helps me sort of understand the principles and kind of what’s going on. And then I feel like sometimes I’m too close to it in education. And so I need that sort of distance to then come back and look at what we’re doing through that lens.

Michael Horn: Yeah.

Diane Tavenner: And so one of the things that happened to me this season is I just engaged in a whole bunch of conversations with people who are in different industries, and I’ve been really trying to deep dive into how are you using AI and how are you bringing it into your company or your work or what you’re doing? How. How is it getting integrated and whatnot. And these articles I’ve been sending you this last week was profound to me. This line of inquiry has led me to believe one. And I’ll just say this again, AI is not changing education full stop. Like, the only thing that will change education is humans.

We can use AI as the tool to do it, but AI is not changing education. There’s zero evidence that it’s changing education. One in my view. And then two, it doesn’t appear to be changing business as fast as everyone’s at. And so like, we started this season with me feeling like this is like coming. It’s coming like a tidal wave and it’s gonna hit us and we have to get ready. And it’s going fast and I’m ending this season like, oh, no, this is electricity. This is a 70-year project.

Like, calm down, we actually have time because it’s just not coming that way. And the specific thing that came this week that crystallized it for me is learning that all of the hyperscalers, so OpenAI, Anthropic and Gemini in some form or fashion, different but similar, literally are paying a lot of money to, let’s call them an intermediary or third party to force companies to adopt AI and integrate it into their workflows because it’s not happening naturally. What appears to be happening naturally is like all these companies jumped on, okay, let’s do a pilot, let’s figure this out. They’ve been running those, they’re not working. And so they’re tossing AI. They’re like, no, we’re not going to do that.

Michael Horn: Because it’s expensive. The computer power, the tokens, right? It’s expensive and people aren’t using it.

Diane Tavenner: Like, it’s not happening the way they thought. And so, you know, in OpenAI apparently is going to give their private equity companies a huge amount of money to force their companies to integrate AI, OpenAI, you know.

Michael Horn: See if any of that money materializes the business model of — I mean, that’s another shaky thing in all this is like, which companies will still even be here at the end of this? Google probably will, but like, we don’t know.

Diane Tavenner: Right. And I think it was Anthropic who’s going more of the consulting route. So MacKenzie and those folks, and they’re going to use them to try to deploy it. And Google had a — what was their strategy? Like a combo of the two or something like that. Anyway, I’m like, if it’s not happening in business, where they actually put resources to these things and efficiency is their, you know, the way they’re competing, it is not happening in education. We don’t have the resources to do what they’re doing. We don’t have the incentives they have.

And so, it just gave me sort of like in some ways a good breath of like, oh, we have more time. And then two, oh, unless we actively do this, this is not gonna be the tool that helps us.

Michael Horn: Yeah. So stay on this. This is really interesting on a few fronts. You’re crystallizing something I had been thinking about but hadn’t fully dawned on me, which is I do think what’s true in the business world, and I think it’s very true in the education world, is individuals are using it in bespoke ways.

Diane Tavenner: Yes.

Michael Horn: And like, I’ve been thinking about some danger of recording and then this won’t be out for a few weeks. But I’ve been thinking about writing something around how, like, for years, education was like the last place to adopt technology. Right now it’s actually adopting technology very quickly. And that’s the problem. And the reason I say that is, I think teachers are pulling it into workflows that exist. This is the Magic School thing that we learned what they’re already doing, but more efficiently, perhaps with less thought, et cetera, et cetera.

But to your point, from an enterprise like reworking workflows. No.

Diane Tavenner: Right. Which goes back to that idea of the operating system, right?

Michael Horn: But I think — so the other thing I want to just say on this is the other reason I’ve been skeptical. All my friends that live out where you live in Silicon Valley tell me that, like, you don’t get — it’s exponential improvement, and therefore it’s going to like, flip a switch and the world’s going to change and blah, blah, blah, blah, blah. And Reed has this view a bit as well, right? That he said.

But I think what they discount is, like, they think through a very narrow lens of how work is done, and they discount all of the human friction that you just started to allude to and organizational friction and legacy systems and stuff like that. Not just in education, but, like, I think you’re right. It’s actually in almost every part of the economy.

Diane Tavenner: Right. And I’ll just say two funny things about that, because that’s where I live, as you know, that’s like the water that I swim in. And I do think a lot of that sentiment comes from. It is moving most quickly and most effectively in software development.

Michael Horn: I mean, that’s why you see the product development. I mean, that’s why I think you see and it’s important. Amazing, right, Claude code is amazing.

Diane Tavenner: Amazing.

Michael Horn: And it’s like zeroing in on an application in a part of the economy that they understand. And it’s a narrow set of — I’ll misuse the Howard Gardner multiple intelligences. It’s a narrow set of intelligences, like, you know, what my point is, though. Yes. I don’t mean to lift that up as a valid framework, but I more mean it as an analogy.

Diane Tavenner: Yes.

Michael Horn: Let me say it this way more specifically. Right. Which is an interesting thing about these large language models is they’re largely trained on language and images, and like we have as humans, have lots of other senses that are not going into these things.

Diane Tavenner: Right, right, right, right. And it turns out we have a lot of power and control over our systems and our lives and whatnot. At the end of the day. Well, that kind of leads us into the last two tools that we explored, OK?

Michael Horn: Kira and Magic School.

Diane Tavenner: Kira and Magic School. And I mean, just a funny aside, I don’t know if people could detect this, but I think we have to come clean and be honest. Like, both of us left one episode this season pretty, like, frustrated, angry. I mean, I had a very hard, long weekend after one episode because it really, like, shook me. They were different episodes.

Michael Horn: OK, yeah.

Diane Tavenner: And I don’t think either of us predict that we would have our own emotional reaction or that the other person would. So I will come clean and say, yeah, I mean, I had a really rough weekend after we recorded the Magic School episode on a Friday, and it shook me.

Michael Horn: Yeah.

Diane Tavenner: Yeah. And I don’t know how you would describe your feelings after the Kira —

Michael Horn: After the Kira — ? Yeah. I mean, could people hear the frustration? I don’t know.

Diane Tavenner: Yeah. You were frustrated.

Michael Horn: I was frustrated. I think before that, I will say on the Magic School, one little surprised me about the conversation. And so maybe that’s why I didn’t have quite the emotional reaction. I was not surprised by the direction it went. But we can break that down. The Kira, I’ll just say, rather than hide it. I was trying to understand in more detail, like, what are you, like, what does this actually look like when you say you have a Central American country, that it’s all moving over to a new. Like, that’s a big claim.

Diane Tavenner: Yeah.

Michael Horn: What is it? Like, what does that actually mean? Look, like, how are you changing workflows, what people are doing? And when you say, like, contrast with Dacia.

Diane Tavenner: Right.

Reflecting on content personalization

Michael Horn: Because Dacia’s episode we recorded, I think, afterwards, and that’s when I had this big breath of fresh air to me was like — she’s like — we went deep in the content as humans to really understand what it’s saying. And I felt like, so maybe Kira’s able to ingest anything of content and, like, magically make it awesome and personalized and — But it took me back to a lot of stuff in like the mid 2010s that talked about personalization and data without, like a clear view of like, how are we assessing mastery? How do we understand this thing? And like, if you don’t have a viewpoint on that, I’m not sure how you’re driving these behaviors. And so I’m — I’m not castigating the tool because I just, it was more like I wanted to understand more.

Diane Tavenner: Yeah. And that was what I mean. We hung up and you were like, I don’t — I’m so frustrated. I don’t understand what it does.

Michael Horn: Yeah. It was more limitations.

Diane Tavenner: Yeah. And to be fair, as you know, I’ve spent multiple hours now sort of getting under the hood.

Michael Horn: Yeah.

Diane Tavenner: I think that’s a really interesting response though. That, and what it made me reflect on is Kira is currently in the complexity. So I always love this, you know, quote that gets, you know, you start with simplicity, you move into complexity and the goal is to get to the other side of complexity, back to simplicity, so people can understand it. And I was, I realized that I have been sucked into Kira because it is in that complex, messy place.

Michael Horn: Yeah. So it’s hard to describe.

Diane Tavenner: So much potential there though, that I can see when I’m really geeking out and nerding out. And so I see the possibility there. But I’m doing like all that mental translation, I think, having dug in. So. But I don’t think it’s a simple story yet by any stretch of the imagination.

Michael Horn: Which is fine, right? Like a startup. It doesn’t have to be until product market fit is so clear.

Diane Tavenner: Right. And there’s a lot to be figured out there and understood. But I do think it’s the closest thing I’ve seen to the potential of being kind of this operating system. Post industrial operating system. And I will just say quickly that like, part of your skepticism probably comes from like, we get a lot of people calling, emailing us, stopping us, you know, at the ASU + GSV, telling us we’re doing exactly what those people said we’re doing. And then we try to dig in and we’re like, are you really. You know, and so. And you get more of it than I do.

Diane Tavenner: And so I bet.

Michael Horn: I think that’s certainly true. And I think I’m also part of my skepticism is I kind of think you have to build for this H2, H3.

Diane Tavenner: Yeah.

Michael Horn: And purposely say we’re not building for H1 to do, if you’re really going to be the operating system for this new model. I think anything you build in H1 that’s optimizing around that is going to do two things. I think it’s going to suck you out. Like I think there will be values that are in contradiction with each other and you can’t do both. And two, I think because the most of the kids are still in H1 and most of the money is still in H1, we’re going to see what every venture backed ed tech company has done which is like start out with this value proposition that sounds great. And then move back into the traditional system. Because that’s where the money is.

Diane Tavenner: And that is huge tension. It’s a huge tension and it’s a limitation in our sector quite frankly.

Michael Horn: Yeah. And I think venture does not help it because they have very rapid expectations of growth that are incompatible..

Diane Tavenner: Incompatible with that. And so how do we get someone to build for H3? It’s a huge question.

Michael Horn: Let’s go into it next year more because I think what you did at Summit, actually doing it interdependently with the school model is. And with the AI tools available now, maybe actually easier to do parts of that. I think that’s gonna have to be the answer, but let’s hold on.

Diane Tavenner: Let’s hold, let’s hold.

Michael Horn: So let’s talk about Magic School because you had a reaction but like this is a place where I’m gonna say it. They do have product market fit. It’s taken off like gangbusters.

Diane Tavenner: Like crazy. Which is — well, let me just say the parts that I really spent that weekend grappling with and struggling with. So the first is, I mean the clarity with, of the purpose of Magic School being to make teachers’ lives easier. And so first of all I just have sort of, that is not my goal or objective ever.

Michael Horn: Period.

Diane Tavenner: And it’s not because I don’t love teachers. It’s not because I don’t think educators are amazing. Look, I am one. I do that role, whatnot. But my goal is not to make a teacher’s life easier. My goal is all about the young person. And I view all of us, me included, who are not those young people as resources to that young person’s learning journey to get them to their outcome. And so I just think when you orient around making a teacher’s life easier, you by definition are no longer focused on the student.

Critique of current education standards

Diane Tavenner: And so I just think that fundamental premise is problematic to me. Second, it just as a teacher who is really committed to the craft of teaching and learning and education. I just feel like it sets this incredibly low bar. And you know, we had a dialogue about, you know, IEP creation. And as you know, I spent a lot of time in Magic School and I was really disturbed by this, like popped out generated IEP for a variety of reasons that was terrible. And you know, the idea that that was somehow better than what is existing in school that had the kids wrong kid’s name on it is like good enough or we should be celebrating or like that’s our bar.

I just can’t. That’s just not who I am as an educator. You know, my — every morning I wake up and say like, is this a school I would want to go to as a student, teach in, send my child to. And I don’t want, that does not meet my bar in any way, shape or form. So I’d rather not be doing education than doing it at that kind of standard. And then the third thing was I’ve just seen this movie before. So first it was, you know, teachers, paid teachers where we just throw these random activities up and, and you know, you’re just doing this activity driven thing.

And then it was Pinterest where teachers are just grabbing stuff off of Pinterest. And now it is Magic School, and it just really diminishes. It makes me so sad. It’s like, really, that’s who we are?

Michael Horn: Yeah, I think I wasn’t surprised by any of that, which is why I will say I appreciated, when you talked about learning styles and stuff like that. I was. We were both pretty horrified by that given the research clarity around that learning styles is a myth Yeah, so full stop. I thought his answer on the IEP, I could appreciate it a little bit more because I do think that the baseline is not what we would hope across.

And so I get it. And I think my overriding thing is like, it illustrates to me the problems with H1, which is, and I, you know, I sent this thing to you that I did with Rick Hess. I think because existing school models struggle with, with coherence, rigor, you know, setting clear expectations for students and focusing on the student frankly, any model, any tool you sell into that model that gets immediate uptake without significant process and priority changes, as frictionless as that has, and the other tools that you named have, it’s actually amplifying those things rather than solving them.

Diane Tavenner: Yeah, I completely agree. Completely agree. And then of course the business model of we’re going to get all these teachers to use it. And then we’re going to take the data to the school or the school district and say, did you know all your teachers are using it? You should buy an enterprise license. That was deeply disturbing to me. That’s the seat that I sat in most recently. And I’m like, really? Am I going to be like, that’s how I’m making my decisions about what tools we’re adopting?

Michael Horn: Well, I’m going to say independent, though. Right. Of the. That this is my bigger point right now. I think of like, the individuals in the system are just doing stuff. And that’s where I think education actually is. I again — I think, the moniker. We’re tech backwards. Rip Van Winkle wakes up and recognizes the classroom. I don’t think that’s true anymore in this era because they are using a lot of stuff. In fact, districts are using —

Diane Tavenner: Yeah.

Michael Horn: Have licenses to 3,000 tools on it.

Diane Tavenner: That stat, you tell me that. You’ve said that like 20 times.

Michael Horn: Mind blowing, right? It’s mind blowing, and it’s a problem.

Diane Tavenner: I know.

Michael Horn: Most of them we know don’t get used.

Diane Tavenner: Correct.

Michael Horn: We know for the effective ones that it’s like a 5% problem of only, you know, students on a certain part of the curve even using them.

Diane Tavenner: Yeah.

Michael Horn: To me, though, these are all model problems at the end of the day. And that’s where I think I can get my head around. Okay, are we working on H1? Are we working on reinventing completely? I can get my head around that there’s importance and value in both. Personally, I really struggle on the first one because I just think the model is so injurious at this point.

Diane Tavenner: Well, and I think that one of the things that happened in the middle of the season was this backlash to this technology, which we haven’t even unpacked yet. Literally. We’re mid season and suddenly almost out of nowhere and overnight, I mean, this massive backlash. And you alluded to it earlier, look, we were already on this trajectory of banning phones in schools. You know, we’ve talked about that over the years. I think we’ve both sort of settled in like, you know? OK.

Michael Horn: Yeah. I mean, I’m willing to live with it. I don’t love it.

Diane Tavenner: I don’t love it either. I will, but I don’t want to live in the space of like, I wish that we could have a culture in the school where kids could learn to use them responsibly

Michael Horn: No, I agree. Yeah, I agree with that. I guess I’m more intrigued with some of these other tools that sharply limit and let the educators have control. But look, yeah, I’m not gonna do.

Diane Tavenner: That’s interesting. So that one fine we had sort of. But then suddenly overnight we are banning Chromebooks. We are putting limits on the amount of minutes that can be technology can be used in the school in a school day. We are saying that, that no one in a school can use AI, including the teacher in any way, shape or form. Which is the most baffling, mind boggling. Like how do you police that? What does that even mean? And these policies are popping up like crazy. My take on this is that there’s such a lack of understanding and such a conflation going on.

So there’s all these cases and the, the heightened sensitivity around social media and the negative impacts on kids that is mostly sitting on their phones but certainly it’s online and some kids are, you know, districts are not using the filters and whatnot. And so they’re maybe getting on their Chromebooks. So that’s getting conflated with actual useful learning tools, useful learning technologies, useful operational system technologies. And we’re just suddenly going to just ban everything.

Michael Horn: Yeah.

Diane Tavenner: Seriously.

Michael Horn: Yeah. And this is so this is the thing, right? Let’s like we live in the nuance. We like the third way. That’s our trademark, for better or worse, let’s like spell it out. We are simultaneously not thrilled with large parts of the ed tech market that is going into traditional schools. And so I understand why the backlash is emerging. I would agree that for the most part they have not been useful in some cases counterproductive.

Diane Tavenner: Correct.

Michael Horn: And these policies are limiting. So we’re not going to let someone use CourseMojo for, like what?

Diane Tavenner: That was the first example that we came to. Or we’re going to say like well if you use it and you. We’re going to count the timer, you use that for 20 minutes. So you only have 40 more minutes today for the whole day to use any sort of technology.

Michael Horn: Like what are we doing?

Diane Tavenner: What are we doing?

Michael Horn: Yeah. And so — or like Amira Learning, which Dacia also talked about, which very well studied, lots of RCTs. We’re not going to like let them use that? And then there’s been this whole meme that has gone on about a particular company i-Ready on the internet over the last few weeks about how there’s no studies behind that them. Fine. I’m not going to defend them right here except to say like that’s true of every freaking textbook and material we have ever had in school.

So let’s, like, have an honest conversation.

Diane Tavenner: Well, yes, and, and all the — the — the negatives that you just said about ed tech are 100% true of all of those industries, if you will. The other thing, like, let’s put Once in this. There’s a bunch of places banning technology use with kids under certain age, period, full stop. They can’t use Once to teach their kids to read, even though there’s human component to it. Like, this is crazy.

Michael Horn: This is crazy.

Diane Tavenner: This is literally taking a sledgehammer to —

Discussing educational model challenges

Michael Horn: What is a real problem. And I’ve been thinking a lot about it, like, what’s the role, you know, blended and all these things that we’ve sort of movements that we were part of. And I was looking back and I was like, I actually think we were pretty clear. Like a station rotation, which is all I said in elementary school, from a traditional district should even attempt. I’m partial to the flex model that I, in our typology, but, like, I didn’t think most districts had the cultures or routines or processes to do it. A station rotation model imagines a kid on a computer for like 30, 45 —

But to your point, these blunt acts policies —

Diane Tavenner: They don’t give any discretion or flexibility or professionalism to the people in the school who are having to make these choices and decisions. And the tension there, of course, is like, wow, did we lose that? Did we deservedly lose that trust?

Michael Horn: That trust, yeah.

Diane Tavenner: Because we are making some bad choices about what we’re bringing in. We are wasting money on things we’re not using. We are not thoughtfully integrating, you know

Michael Horn: So, two thoughts here, because I think you’re right. And so we’ve sort of made our. Our point of view known. I hadn’t thought about this, but I loved that Dacia said we’re doing outcomes based on contract.

Diane Tavenner: Yes, I do too.

Michael Horn: I’ve been saying this since 2009. Like districts. And districts would look at me like I was crazy. There’s now, you know, through the Southern Education Group.

Diane Tavenner: There’s a whole group.

Michael Horn: There’s a way, there’s a boilerplate language. I think every ed tech company worth its salt ought to do a contract.

Diane Tavenner: Well, let me just amend that because I’m in that space right now. That’s the exact place I wanted to go because I’ve sat on the other side, and I was like, this is brilliant. I love this, and quite frankly it’s good for me too because it doesn’t help me if someone buys me my product and never uses it. That’s actually bad for all of us.

Michael Horn: Yeah, yeah. Zombie revenue, etc.

Diane Tavenner: But the problem is even the outcomes based contracting people will tell you, oh, we only really are ready to do this with very limited number of products where there are clear measures. I think we’ve got to push on that. I’m hoping to be one of the people who will push on that because the space I’m in, they’re like, oh, we have no idea how to tackle that space.

Michael Horn: But this would be amazing. Amazing. I’m gonna go into one of your other bugaboos. Yeah, this would be amazing. Like RCTs is one way to look at the world.

Diane Tavenner: One way.

Michael Horn: If you have outcome based contracting with assessments and measures that we trust. Yes, it solves itself totally. And so, like, this is a way I think to actually develop incentives.

Diane Tavenner: I agree.

Michael Horn: To get those more grassroots driven. What’s the outcome we’re trying to drive? What’s the right assessment or measure for it? And let’s count it like conversations.

Diane Tavenner: I agree with you. And like in my case there are things I specifically know that our technology is designed to do. And so maybe those aren’t yet, you know, fully industry standard or something. But. But when you’re buying it, don’t you want to know what it’s supposed to do and then figure out if it’s —

Michael Horn: And then hold it to account for that?

Diane Tavenner: Yeah. And I think that’s good for everyone. So I agree with you. I think more there really interesting and —

Michael Horn: I think that would be a good get instead of the bands. That would be a great place for that. I will say I think it also. We may pull back into school models here for a second. I think it’s one of the reasons Alpha is attractive also to families is because they feel like they’re making this big leap, OK? And so actually I heard someone, one of my students said they profiled seven different microschools using AI in different ways. And one of their statements was like families feel like, you know, two hour uninterrupted block of time or whatever feels radical.

Is it really more radical than like seven 45 minute periods? No, but like no one ever thinks that way. But it feels like radical to do, you know, mastery based progression and then like all this time for life skill development and with what you said still true, like it’s decoupled, and that may have problems for Far transfer.

Diane Tavenner: And I’m not sure they’re doing mastery based, but keep going.

Understanding norm-referenced assessments

Michael Horn: Okay, so maybe we’ll come back to that also. But like, I guess my point being like families are opting for it, and I think one reason that they are is not just the price point signaling, but also the old metrics signaling. We might not love the old metrics. NWEA map. I think it’s like a very bizarre usage of it. And I’m just going to spell it out for those that. Because I think people still are confused around what a norm referenced assessment often shows, which is like imagine a Y axis of different score levels or achievement levels and like, okay, I’m at the 90th percentile entering in, relative to the people in my band. There’s a curve and like, you know, am I falling below or above right them.

And so when they say 2x, it’s like 2x the person in the middle at my curve is what that’s showing.

Diane Tavenner: Yeah.

Michael Horn: And in some ways it would be shocking if they weren’t hitting that because the traditional school is capping how fast you can move. We heard Reed talk about this on Dreambox and so if you’re now uncapped it, I would hope that a student would move at least that much faster.

Diane Tavenner: Well, this is why I question if they’re doing mastery based. Because that has nothing to do with mastery.

Michael Horn: Say more on that.

Diane Tavenner: I mean where you’re just comparing against the kid. That is not mastery based. Norm reference is not mastery based.

Michael Horn: Oh, fair enough. Fair enough. OK, so this is where I think they are though is because they also have a bunch of criterion references also going on that they didn’t talk about.

Diane Tavenner: Yeah. Yeah. And you know, I’m skeptical because they’re like jumping between adaptive learning programs.

Michael Horn: I think that’s a big question. Yeah, yeah. I don’t disagree. And I’m wondering where the feedback loop truly is in that some friends of ours in the industry who’ve spent a lot more time with the model tell me that there’s a lot more underneath it. Underneath. And I don’t know. I don’t know. I will say simply raising the bar from 60% to 90% was a nice start.

Diane Tavenner: Yeah. Yeah.

Michael Horn: Because it’s crazy.

Diane Tavenner: Well, and we did that as well at Summit. You know.

Michael Horn: But it’s crazy to me that they didn’t like before. But it reflects also on H1. But I guess my point being more I think that’s why they have these very traditional measures because it gives, like as a parent, it takes away anxiety.

Diane Tavenner: Yeah.

Michael Horn: I’m making this big jump. What do you mean? They’re going to be doing a Tough Mudder and a press conference with athletes.

Diane Tavenner: And like, the familiar measures that give me comfort.

Michael Horn: So I think it’s almost taking the world as it is, not as maybe we would like it to be. And I think that’s part of, like, it’s like, it’s why. I also think you’ll hear the bragging about the SAT score or the acceptance to Stanford or the, you know, and. Yeah, so I guess I’m just explaining it a little bit, but. And I think that crosswalk may be part of this process as more families gain maturity around. Oh, is that possible through this? And so I’m curious.

Diane Tavenner: Yeah. So what I think you’re speaking to is this whole, whole societal shifting sentiments. And so, and we don’t really talk about this, but this would be an interesting thing to explore next year. Like, is there a way to understand. Well, we know people who look at these things, so there is a way to understand this.

Is the public sort of sentiment moving or shifting or changing? And what is moving it and shifting and changing it? And so, you know, are we in this sort of chaotic period where we’re like, people are holding onto things of the past, but they’re like, exploring and looking at the future and they’re kind of like, you know, there just feels —

Michael Horn: Well, and here’s the relationship, right to the tech conversation also, which is like, okay, my kid’s going to be on this two hours of screen time with AI, it sounds creepy. It’s a vision model that’s looking at me, tracking my attention. But there’s a real measure that shows outcomes.

Diane Tavenner: Right.

Michael Horn: And so I think, think like now

Diane Tavenner: And this cool cocktail party thing where I just say that my kids, you know, running a Tough Mudder.

Michael Horn: Yeah, yeah, yeah, Right, right, right. Of running a Tough Mudder. And so like, but here the legislation would ban these movements to like, like this stuff as well. That’s not. I don’t think that’s what we want either.

Diane Tavenner: I mean, we’ve never liked legislation, like the type of legislation we’re seeing popping up overnight. I hope by the time next season starts, it’s kind of calmed down a little bit and people have sort of gathered their wits. I hope that educators are going and making logical cases as to why with real tangible examples like we’ve just given about why these are not useful policies. And can we actually put something more thoughtful in place? I hope the media doesn’t, you know, sort of fan those, you know, fears and instead sort of brings a little bit of a rationality to it. That’s of lot of hope. You gotta have it.

Michael Horn: But yeah, I mean, I think, I think my hope on that front continues to be the families that are opting for these new models and that it’s more grassroots driven because and I think, like, I think that’s my other piece and we’ve had this conversation offline. Your question a little bit is it really going to be a portfolio of models in the future? Is it really one?

Diane Tavenner: I’m so curious about this.

Michael HornL and I guess the reason I think it might be a. I think it depends how you define model.

Diane Tavenner: It does.

Michael Horn: And I think it also depends on like, I think it’s very hard to imagine all families lining up. I’ll say it up front, like a billion people in Alpha school. I would be shocked. Shocked. Because I don’t think that’s where we

Diane Tavenner: Just count me as a complete skeptical. There’s no way.

Michael Horn: But I think that’s the point is like some families are going to say no screen time.

Diane Tavenner: Right.

Michael Horn: Great. Some families are gonna want more than what Alpha provides OK. But like, let’s, let’s take the air out of the balloon and let a little more choice with some measures that give feedback loops to the families so that they know they’re making progress for their kids.

Diane Tavenner: We can’t help ourselves. We’re starting to foreshadow all the things we’re thinking about and what we’re starting to plan for next season. So I guess maybe it’s obvious at this point but, people might not realize that literally at the end of every season we get together and we’d reflect and we make an active choice of whether or not we’re gonna do another season and this season. I don’t even think there was a question.

Michael Horn: No. This was the most clear cut we’ve ever been I think.

Diane Tavenner: Clear cut.

Michael Horn: Clear time.

Exploring AI’s impact on education

Diane Tavenner: I feel like we are so clear. So much more to explore the impacts of AI. So I think we’re going right back in on that front. Two, I think we have more questions than we had even at the beginning of this season. More people that we want to talk to than we had at the start of this season. So like more, more, of, you know, how what is happening with AI in education? What are the tools? What are the models, you know, what’s happening on that front? So I guess this is a good time to say, like, if you guys have ideas, people you want to hear from, you know, things you think we’re not exploring.

We would love to hear from you over the, you know, this will be effectively the summer, if you will. Yeah, I know. It’s like the one sort of traditional thing we do is, like, have summer break but —

Michael Horn: We’re a little traditional.

Diane Tavenner: We’re a little bit traditional on that front. So we want to hear from people on that front. And then we’ve got these other two kind of funny passion project ideas.

Michael Horn: Are you going to talk about it? Yeah, go for it. No? OK, all right. We got a couple other ideas, which are going to be our curiosity driving it and our desire to explore. But it won’t be with guests or at least maybe. Yeah, but not.

Diane Tavenner: I don’t know what form they’re going to take. I would. Yeah, they’re interesting. They’re like things that keep coming up for us, and so I think they deserve a little bit of exploration in maybe not the exact way that we do the rest of the podcast. So we’ll see how they —

Michael Horn: We’ll see how those take shape. Probably not like season one, where we script every.

Diane Tavenner: No, no, no, no. So. So we hope folks will be excited to join us again for another season.

Michael Horn: I think that’s right. And send us your curiosity questions and, like, things that we’ve said that don’t make sense to you. And we’ve both had a lot of hot takes today and left a few dangling.

Diane Tavenner: Kind of a rambling conversation

Michael Horn: I was about to say. Did I give you room for everything you wanted to say?

Diane Tavenner: No, definitely. I mean, this is just like an ongoing dialogue for us.

Michael Horn: So people pick in where the things that we maybe had incomplete sentences or thoughts or you want to hear more.

Diane Tavenner: Definitely.

Michael Horn: We’re going to scratch those itches next season.

Diane Tavenner: Definitely. I’m very excited for it. But before we do that, should we

Michael Horn: Do what we’ve been reading or watching or what we’re going to do?

Diane Tavenner: We should. And I — I think I have to —

Michael Horn: Pull out my phone so I know what I’m reading.

Diane Tavenner: You have to know what you’ve been reading. AndI’ve got. I decided since this is the last episode of the season, I have three things, so I’m gonna go crazy and share more than one.

Michael Horn: All right, Go for it. Go for it.

Diane Tavenner: You want me to start?

Michael Horn: Yeah, you. You start.

Diane Tavenner: So this one probably won’t surprise people, I have just recently finished reading “How Countries Go Broke, the Big Cycle” by Ray Dalio. And people have heard me who’ve listened to the podcast before talk about his previous book, “The Principles for Dealing with a Changing World Order,” which was really profound for me in terms of these deep analytical approach to identifying these historical cycles that help me make sense of the world we’re living in and what sort of feels chaotic. This new one, newer one, about how countries go broke, is a companion to the first one. Obviously it’s speaking to something that I think about and don’t know a lot about, which is our debt and what it means if we’re no longer the reserve currency, et cetera. What I will say about it is it’s sort of classic Ray Dalio, if you like that kind of thing. It’s actually comforting to me in a way. So I really —

Michael Horn: It’s a critical topic.

Diane Tavenner: It is critical as like a citizen, I think, and a person in the world. So I highly recommend it. I find it fascinating. So that’s one. The second one is a podcast, you know, that’s become one of my favorite podcasts. It’s fascinating. Interesting times with Ross. I never say his last name

Michael Horn: Ross Douthat.

Diane Tavenner: Yeah.

Michael Horn: Yeah. He was my year from college, so, you know.

Diane Tavenner: Oh, I didn’t realize that.

Michael Horn: Different college, but it’s —

Diane Tavenner: Yeah, OK. And I listened to this one at the same time. I was reading the Countries Going Broke book and they go together in a really interesting way. It’s titled “A Bitcoin Evangelist Tries to Convert Me.”

Michael Horn: Oh, interesting.

Diane Tavenner: And it is like Bitcoin 101, which again, important topic. Highly related to the “Countries Going Broke.”

Michael Horn: Well, I was going to say highly related to the notion of a reserve currency.

Diane Tavenner: Exactly, exactly. So I found those two things a really interesting pair. And then the last one is, you know how much I love the “Last Invention” podcast series. I thought it’s, it’s just incredibly well done. They do continue to add these fascinating, you know, episodes here and there, but that comes out of a group called the “Reflector” podcast who actually does all of these other topics. And I just listened to a three-part series titled “Strange Bedfellows When LGB Meet T.”

Michael Horn: Oh, interesting.

Diane Tavenner: And so good. It’s this historical look at the movement. It’s incredibly well done. The style design that I love and for me, very personal. Lots of people I love are LGBTQ and living it and it just gave me a lot of incredible insights and history and understanding and provocations and yeah, really fun.

Michael Horn: Super interesting.

Diane Tavenner: Yeah.

Michael Horn: I’ll add that then to summer walk, dog walks, I guess.

What do I have? So I’m finishing up “Jump,” which is by Larry Miller. It’s the same. The story of — so he was on the Jordan Brand and Nike, and it’s his memoirs, effectively.

Diane Tavenner: OK.

Michael Horn: But he was someone who was incarcerated. He did some, you know, pretty awful things as a young man growing up in Philadelphia. Spent a couple times in jail, got his education while he was in jail, got a degree, and then ultimately an accounting degree from Temple, and then went on to have this incredible career. And it’s these reflections and frankly, also how he was dealing with this, like, ghosts in his past that no one knew about and he couldn’t talk about. And sort of. It’s a really interesting, like, reflection on human potential and how do we think about, you know, this in people.

Diane Tavenner: You were telling me about it last night, and I’m very curious.

Michael Horn: Yeah. And it’s a great — it’s a great audiobook.

Diane Tavenner: OK.

Michael Horn: So it’s very — it’s very lively.

Diane Tavenner: On my list.

Michael Horn: On your list. I’m doing a weird — so I was trying to read books out loud to my kids, because why not?

Diane Tavenner: Because they’re adorable, and it’s so fun.

Michael Horn: Yeah, well. But you don’t get the — I mean, because they’re just so racing ahead all the time, right? I don’t have a lot of opportunities. I was trying to do a couple Mark Twain books with them because I remember loving “A Connecticut Yankee in King Arthur’s Court.” Here’s the takeaway: Really hard to read it out loud.

It’s really sort of funky.

Diane Tavenner: I was like, oh, interesting choice.

Michael Horn: But as a result, I’m reading all the Mark Twain books now, so to myself. And so, like, I know you’re gonna laugh. I don’t love fiction generally.

Diane Tavenner: No, you don’t.

Michael Horn: Nope. But I’m enjoying it. So we’ll see where it goes if I keep going through it. But I had this idea that I would culminate with the Ron Chernow biography of Mark Twain.

Diane Tavenner: Oh, my gosh. You be careful before you do that, Scott read it. Do you read how long that book? It’s long.

Michael Horn: Yeah.

Diane Tavenner: He was in that book forever. Yeah.

Michael Horn: The Ulysses S. Grant book was a long one that I read it by him as well.

Diane Tavenner: I mean, the level of detail about one person’s life, it’s extraordinary.

Michael Horn: OK, well, we’ll see if I do it or not, but maybe it’ll slow down my pace in books and I’ll have to watch more TV.

Diane Tavenner: You’ll come back next season and be like, I read one book.

Michael Horn: I read one book this summer. Yeah, no, it’s entirely possible, but those are the things at the moment on my list.

Diane Tavenner: That’s awesome. Yeah, I’m not adding that one to my list.

Michael Horn: Fair enough. Fair enough. I’ll commiserate with Scott afterwards. Huge gratitude, Diane. This has been a lot of fun this season. In some ways we’ve had a lot of fun throughout, but in some ways the most fun that I think I’ve had.

Diane Tavenner: So I agree. This is. We both do kind of a lot of sort of side gigs, volunteer, you know, middle of the night, passion project. But this definitely is very special.

Michael Horn: Huge thanks for all and huge thanks to our audience again because it does really fuel our curiosity and our desire to do this. And feedback keeps us going. Reminder. Subscribe Rate us.

Diane Tavenner: Yes.

Michael Horn: We forgot to say that up front.

Diane Tavenner: I know. Because we always forget to say it. We’ve never said it, but it turns out it does matter a little. And so if you can even take just 60 seconds to put five stars there and give us some of the feedback that you give us verbally or in writing, it helps a lot.

Michael Horn: And let’s also say thank yous to �� for continuing to distribute. Let’s say thank you to LearnerStudio for sponsoring the team Danny and Lindsay, who make this come to life at the moment. Really appreciate both of them. Both last names Curtis, but not related. And just huge thanks to everyone that makes this work.

Diane Tavenner: Yeah. Awesome. And with that …

Michael Horn: … and with that, we’ll see you next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

Dana Suskind on How To Protect Childhood in the Age of AI

Mark Swartz — Thu, 09 Jul 2026 14:30:00 +0000

The last time I interviewed Dr. Dana Suskind, we discussed the three T’s strategy outlined in her book “”: Tune in. Talk more. Take turns. “It doesn’t require fancy gadgets,” she told me, “or a specialized degree.”

Though it was only a few years ago, the fancy gadgets have gotten a lot more advanced since then, and now Suskind is back with her next (and, she promises, her last) book, addressing the promise and perils of the technological revolution coming for every element of society, including young children.

In “,” which will be released on July 14, Suskind acknowledges that technology is swiftly mastering endeavors that were once considered uniquely human. “Artificial intelligence is eroding our supposed superiority in each area one by one. … It generates language with fluency that surpasses most humans. Al turns out art, music, poetry, software. It uses existing tools and builds new ones.”

But she zeroes in on a distinction that matters: Unlike humans, “AI does not care.”

Suskind’s new book guides parents, caregivers and educators through navigating how to raise children in the age of AI by offering the HOPE framework, which offers four principles: human connection, owning imperfections, protecting the early years and enhancing adult-child interaction. She called it HOPE, she said, because “Having a child is an act of hope for the future, and the future is not predetermined, as much as it feels like it is.”

A pediatric surgeon and the founder and co-director of , Suskind has been a prominent voice in early learning since the publication of her 2015 book, “” which popularized the concept of linguistic “serve and return” between babies and their caregivers.

In the conversation below, Suskind describes her nuanced stance on AI and shares her thoughts on how AI shapes child development as well as what parents and early educators should consider when deciding how and when to use technology with young children.

This interview has been edited for length and clarity.

How does “Human Raised” fit in with your other books?

My whole journey has been about the power of talk and interaction and relationships … to allow children to not just learn, but to become human. A couple years ago, when AI started building momentum, I was like, “Oh my gosh, wait a second.” Suddenly we have technology that can mimic the human interaction that builds a child’s brain, which has been the focus of my entire research. I was like, “Oh, we really need to be thinking about this in a really deep and thorough way.” Because not only was it going to come so fast and furious, but parents have no guidelines or guidance. There are no policy guardrails. And so I felt I needed to write one more book — I swear this is my last one! I wrote it to think through this whole thorny issue myself.

What particular insights do you bring as a surgeon?

I’m a cochlear implant surgeon. The way I got into this field was [by examining] differences in early language environments among children who are deaf and hard of hearing and got cochlear implants, [versus] typically developing children. I saw interaction and language … as modalities for building cognitive skills, language and literacy.

I’ve always been cognizant that and nurturing interactions are important for socioemotional development, but so much of my work has been focused on those hard skills. And in writing this book, [I see] that human connection is a way to become human, to build the social brain, to build our ability to connect with other humans and navigate the human world.

How can parents and educators tell when a technology is enhancing connection versus replacing it?

The frictionless experience is what makes it so seductive, and so different than what we’ve met before. I mean, we’ve had technologic innovation throughout all of human history, but it’s always been sort of a one-way street. Even social media and the engagement economy has been about sucking our engagement, but it hasn’t been building an intimate relationship and that is what generative AI [does].

AI is not a monolith. … The generative AI and that intimacy building aspect of it is what is seductive for adults, and for young children and very young children. The younger you are, the more likely you are to both anthropomorphize this technology, to project thoughts and feelings onto these entities. So they’re more at risk.

At the same time, I am not anti-technology. … I believe in the power of technology that allows human flourishing. And I do believe that if we use it in the right way, it could do the things that we want. Allow opportunity gaps to close, allow people to have more presence. So let’s use it to enhance the human condition, not to replace human connection.

I love tech, but I don’t love tech to replace the powerful role that parents and caregivers play in building children.

In your book, you talk about the importance of owning our imperfections. How does that make us more human?

One really amazing experience in writing this book was reflecting back on the imperfections of humans and our relationships. We’ve always looked at them as bugs. How can we be better and more perfect parents? But the truth is that “good enough parenting” is an evolutionary gift that actually teaches kids how to be human. … Those missteps and ruptures and repairs help us learn how to be good partners with other humans.

In your new book, you wrote, “AI has the keys to unlock the social gate,” which you define as “the biological filter built into the infant brain that evolved to allow a particular type of teacher: a human one.” What are the ramifications of this breach?

We may not know everything about the technologies that are being built, but we know a whole heck of a lot about how children develop and how they can best develop to their full potential. In some ways, this book was about understanding the interaction between technology and children and adults, but also understanding how the human brain is built.

In some ways, evolution gave us a mechanism to ensure that baby’s brains develop through human connection. Kids don’t learn from TV. It affects their language development and social development, but it doesn’t actually teach them. The social gate has made sure that babies only learn from human interaction. It opens it up and allows the learning to happen. And now that AI can mimic that human interaction … it has the passcode to the social gate, and whatever flows through from that technology is actively wiring that child’s brain.

Patricia Kuhl [professor at the University of Washington and co-director of the university’s ] is a goddess of early brain development. Her research … is probably one of the most important things [for understanding] how to navigate AI in the early learning space. The basic science of human development is incredibly important not just for parents to understand, but policymakers and the people who are building this tech. They need to understand how we develop, so that they build tools that don’t inadvertently lead humanity in the wrong direction.

And how much faith do you have in the people building these AI tools?

I’m not going to answer that question, other than to say that I wrote this book primarily for parents and caregivers — for anyone who loves children. But I want desperately for those who are building technology … to read and understand it, so that they can more intentionally design with developmental science in mind.

How could you see AI technologies supporting the early years?

Number one, don’t displace that human connection that supports the parents or teachers in the children’s lives. We know about the administrative burden and the invisible labor that makes caring for young children so hard, so let’s build tools to make it easier for parents and teachers so that they can be more present. Let’s build tools that allow us to understand better how children are developing when [they] are exhibiting delays, so that we can more quickly ameliorate those issues. And there are scientifically driven tools that support the early learning process, but I don’t think that they should be used as replacements for teachers.

What are some promising areas of research?

Brian Scassellati, who’s a computer scientist at Yale [and director of the ], showed that social robots can help teach children with autism spectrum disorder to learn social cues and become more connected with other humans. In the same vein, [that when] children read to social robots versus humans, they were less anxious. Using the science to help guide us in understanding the best ways to support children’s learning is great, but [we should] never forget that it must not replace human connection, it must only support it.

I’m a physician, I come from the world of medicine. When we create new biologics, let’s say a vaccination, it’s not like we say, “Okay, we’ve created it. Let’s see how it does out in the real world and then go back and tweak and fix it.” No, we do really rigorous studies to make sure it’s safe for the population. And right now everything is being put on parents like, “Oh, you decide …” It’d be like saying, “Here’s your car seat … let us know if it’s safe and we can go back and tweak.” These are really powerful technologies. We need a lot more science, not to stifle innovation, but to make sure our humans remain safe.

We can’t wait for the research to happen to get guardrails in place so that no harm is done. We need longitudinal data in understanding children who are growing up with generative AI as a significant presence, in understanding the impacts on language, socioemotional development and attachment. We don’t have these. I want research on protective factors. What are the conditions in which AI tools generally support human connection rather than displacing it? It’s not just an academic question, it’s a design question that will shape the field.

What are the skills that children are going to need to succeed in the future?

Now that AI can do all the things that we were trying to optimize in our kids — like be the best at math and science — and all these hard skills now can be done by AI a million-fold better than humans, it’s those distinctly human edge skills that matter. The critical thinking, the social connection, the curiosity, creativity, resilience — those are going to be the skills that allow children to thrive in the age of AI.

Opinion: The Final Piece of the Ed-Tech Backlash Has Finally Arrived

Jeremy Adams — Wed, 08 Jul 2026 12:30:00 +0000

I have been a high school teacher for almost three decades, spending almost all that time teaching seniors about American civics. My teaching tenure has overlapped with the rise of the very trends now engulfing our educational system: I have watched my students embrace smartphones, social media, online learning and now artificial intelligence.

But recently I have noticed something I never expected.

Many of my more thoughtful and honest students are becoming critics of the very technologies that shaped them. They are tired of the slop. Tired of content created by code. They say they are repelled by the prospect of an AI friend or romantic partner.

When it comes to the classroom, they willingly admit they prefer a lively class discussion to an online activity. They resent teachers who use AI to grade their papers. And most powerfully of all: They admit their use of technology is a hurdle to becoming the educated Americans they know they should become.

They speak almost like addicts.

They don’t want to be on their phones eight or nine hours a day. They don’t want to use AI to complete their assignments and short-circuit their ability to learn and grow. They know their attention span is stunted.

But in so many circumstances, they simply can’t resist. These observations may sound anecdotal. Let me assure you, they are not.

Over the past year, a series of highly publicized incidents have suggested the emergence of something larger: the first widespread cultural backlash by young Americans against the digital world that shaped them.

Multiple public speakers recently referenced the AI revolution now upon us—a at the University of Central Florida, a at Middle Tennessee State University, former at the University of Arizona. In every instance, young Americans either passionately booed or, in Schmidt’s case, the remarks.

A community college in Arizona to read the names of graduates, but the system quickly malfunctioned. When the college announced what had happened, the backlash was both raw and immediate.

These incidents may appear isolated or trivial.

They are not.

Together they suggest the emergence of something larger: the first widespread cultural backlash by young Americans against the digital world that shaped them. The generation that grew up on iPhones, spending much of every waking hour online, now seems to be awakening to the perils of a digital world neither they nor their parents fully understood.

And yet these young people are increasingly lending their voices to a growing chorus of educators, parents and policymakers who have begun to realize a painful truth: There is no quick technological fix for the crises consuming the modern American classroom.

, , emotional distress and a generation-long erosion in students’ ability to concentrate all demand a fundamental reassessment of the role screens now play in the educational lives of our children.

Every other day a prominent newspaper or publication now gives voice to this fundamental truth.

Whether it’s The New York Times explaining or prominent Substack columnists offering a American educators should take advantage of this moment by defending traditional instruction rooted in foundational human relationships.

A generation ago, young Americans had access to a diverse chorus of influential adult voices that tethered them to the mighty responsibilities and possibilities of adult life. Children lived with two parents, numerous siblings and often spent considerable time with grandparents. Life exposed young minds to a faith tradition with pastors and priests on Sundays, sports activities with coaches after school and maybe Boy Scout or Girl Scout leaders as well.

Many of these voices from ages past are silent today.

But that doesn’t mean our children don’t hear voices. They do, and they often come from people and digital spaces their parents never would have chosen for them.

This is why — especially in this era — the humanity of teachers and the personal vitality of our classrooms are essential. The voices of instruction our children hear should be the voices of teachers who know and care about their students — not Alexa, not Siri, not some anonymous digital interface designed to maximize engagement rather than human flourishing.

Eye contact. Conversation. Personal relationships. No code or product required.

The kids know this. The kids want this.

Shame on us if we fail to give it to them.

‘Rehumaning’ Education: Banning Screens Is Only Part of the Solution

Greg Toppo — Tue, 07 Jul 2026 10:30:00 +0000

Educators are having right now, with schools across the U.S. banning cellphones and parents fighting what many view as excessive classroom screen time.

But educator and author Stephanie Malia Krauss says ditching devices isn’t enough. If we want to improve young people’s academic results and well-being, we must focus on how schools can actually meet their needs. Removing devices without addressing stress and safety, among other issues, will be an empty gesture.

An expert on , Krauss began her career as a Teach For America teacher in Arizona and founded a competency-based high school in St. Louis. She has since led national youth readiness initiatives and consults widely. In her new book, “,” Krauss argues that schools have let addictive technology, stress and chronic busyness strip kids of basic human needs like sleep, play, wonder and connection.��

She has coined the term to describe what schools can do to protect these needs. ��’s Greg Toppo talked to Krauss about how the current tech backlash offers the opening educators need to focus on a version of school with students at its core.��

Their conversation is edited for length and clarity.

You begin the book by observing that wherever you go, you ask people how they’re doing. The answer is always the same: “Everyone’s overwhelmed, stressed and exhausted.” Is this just a COVID hangover, or is something else happening?

I started asking the questions, “How are your kids?” “How are your families?” and “How are you?” as a way to understand the impacts of COVID. I was doing a book tour in my basement, and I couldn’t fly to be with the schools and youth programs I was speaking to. And at that point, the overwhelm, overload, stress and exhaustion that people were reporting made sense. I felt it. We were all experiencing the existential dread of a global pandemic.

But I decided to keep asking the questions, and six years later it’s hard to still consider COVID as the consequence for what’s driving the overwhelm in our lives. Having surveyed tens of thousands of adults caring for kids personally and professionally across an incredible variation of communities and contexts, the results have shown that people are more overwhelmed and overloaded and stressed and tired than they were six years ago. There’s only been one exception, in one community. And that was when I asked a group of hundreds of early childcare educators how the kids were. And the answers were: “Full of joy, curiosity, excitement, enthusiasm, unending energy.”

Something is changing between the early childhood years and the start of school.

Well, maybe we should be talking to them! Are they doing something right, or is something happening with four-year-olds that we need to focus on?

What is interesting to me is that at the bookends of our life, the early years and the elder years, the responses are often the same. Young kids start out as curious, joyful, full of energy, contributors. They want to help. They’re playful, creative. And when researchers study centenarians, people who live to be 100 or older, you often hear the same thing: These are people who have found ways to contribute, stay playful, keep moving, be curious. And in my mind, that’s a reflection that the overwhelm we are feeling today is not a personal failure, it’s an environmental one.

Let’s talk about classroom technology. You’ve said that digital apps and algorithms “exploit students’ developmental vulnerabilities, and that schools need concrete strategies to prioritize human essentials.” It seems like schools are starting to get this message — see recent phone bans and the nascent anti-screen movement. But you think this isn’t going far enough?

Toxic tech — tech that hooks and harms our kids — is what I call one of the “dangerous weather conditions” of modern life. Addictive, manipulative tech is often designed in ways that restrict some of the best parts of being human. So young people go on addictive platforms, for example, and they’re looking to have their normal developmental needs met. They’re curious, they want to connect with somebody, they’re bored and looking for entertainment. A teacher or adult told them to, and then they end up on a platform that’s designed to keep them coming back.

I think about this as “ultra-processed content,” designed to be hyper-palatable. But it’s fake, and kids are less likely then to want to socialize, play or experience recreation outside of something designed to be available 24/7 and feel much more fun and connecting. In the classroom, computers can sometimes be brought in to address capacity issues, cost issues and burnout issues — and they are, like ultra-processed food, a convenience and a cost saver. It’s still a level of ultra-processed content.

Any time that we have technology that is keeping kids from the very things they need to be healthy and happy, and also to learn and develop, I consider that toxic tech. And any time we have technology that actually assists or amplifies the ability to tap into the essentials that keep us healthy and happy, that’s humane tech. It’s technology that should stay. Right now, many schools are moving in the direction of seeing the antidote to addictive tech being the absence of technology. I would say the antidote to addictive tech is the abundance of healthy developmental opportunities that promote human essentials like play and creativity. We don’t have to remove all tech, we need to remove toxic tech.

So, what would be an example of tech that is not just non-toxic, but humane?

I interviewed boys at a private middle school in Richmond not too long ago, and they told me how technology is used for research, for projects, for really exploring things that young people are interested in or curious about. Kids can explore their capacities for wonder and creativity and or focus. They can learn something new, they can think about their personal interests, explore their identity — and then the technology goes away, and they’re reading books and talking about books and doing things outside. Technology is an enabler and encourager of essentials, rather than prohibiting the essentials in the first place.

How do you read the current anti-tech moment we’re in?

My worry is that the removal of devices will address symptoms that we’re seeing that sometimes relate to the consequences of harmful toxic tech, but also have roots in other places. When I was writing the book, the first question I had was, “Why are we all overwhelmed at such an intense level, and why is it getting worse? Hard lives are harder, and times of stability are still stressful.” And what I found was that there were four universal forces at play, toxic tech being only one of them. If we go into schools and remove computers and other digital devices without attending to the other “dangerous weather conditions” that I talk about — being overtapped, overworked, and overwrought, really afraid for our lives, safety issues that students feel — we’re going to see the persistence of the problems that right now are sometimes being exclusively attributed to technology use.

The idea that people are overwhelmed more broadly is not something that I hear discussed in this context. The only thing people are saying is, “My kids are overwhelmed by screens, so we need to remove the screens.”

Yes.

You’ve been on this listening tour, and I gather that kids are really interested in talking to adults about cellphone bans. What do they want us to know?

I’m wrapping up a statewide listening tour of Virginia middle schoolers on behalf of the , which is a statewide partnership of youth development organizations, school districts and education groups. And in every conversation with middle schoolers in Virginia — a state that did statewide phone bans — they have wanted to talk about it. And the answer is almost always the same, which is nuanced, and we have to give kids credit for the nuance they bring into conversations about devices, computers and AI.

What I’ve heard over and over is, “I like not having my phone when I’m learning. It’s easier to focus, it’s easier to pay attention. I’m not as distracted.” And then, from Appalachia to Alexandria, I have heard, “I don’t feel safe.” And what I have come to understand is that for kids, phones aren’t only communication devices, they’re comfort objects. If I have my phone and something terrible happens — a school shooting, a disaster, something scary — that is my way to get in contact with my family and to be safe. And when phones were removed from classrooms, schools either did too little or did not attend at all to the safety needs that crept in.

Aside from students saying, “OK, you took my comfort object,” what is the upshot? Are there behavioral consequences? Are there bigger mental health consequences?

I think so. In conversations with kids, it is clear that every day they come to school worried that something bad and dangerous can happen to them. Without addressing that safety concern, kids will be more dysregulated in the classroom. And we often confuse dysregulation with discipline. The signs of dysregulation look nearly identical to how we would characterize a misbehaving student or a lazy, disengaged student. Teachers receive little to no training on the science of dysregulation, but it’s possible that some of the problem behaviors that they are seeing are really dysregulation and not discipline.

You write about the behaviors that get kids into trouble at school — to your point about dysregulation — and you say, “These kids don’t need detention, they need a nap.” Are we missing a key success factor here? Do high school kids need a nap?

The is absolute that tweens and teens should not do school or anything before 8:30 in the morning, because their body clocks shift the moment they hit puberty. They are wired to want to go to sleep later. It has ancestral origins of young people staying awake later at night to protect their families, which means we wake them up hours before they’re ready and deprive them of the types of sleep they need for learning, memory, emotional regulation, and a whole bunch of other really vital things for learning behavior and future success. As the mom of a high schooler who starts school too early and who is also learning to drive, I will say personally this feels very consequential and even dangerous. We would be amazed at the improvements in adolescent mental health, adolescent learning and motivation if kids simply felt more rested, in the same ways that we feel profound impact as adults when we feel well-rested.

You write about the importance of play and movement, even suggesting that older kids need recess too. I’m guessing without their devices?

Yes. Whether we’re talking about sleep and regulation, or we’re talking about play and movement, this is all a part of a broader move to say it’s really time to “rehuman” schools, to have a version of human-centered schools and education that really protect and prioritize our natural human capacities to learn and develop, but also to endure and enjoy life, to thrive. Across millennia, humans have relied on play, for example, to prepare, to heal and to learn. So, when we look at studies of hunter-gatherer communities, we see that young people up until the time they transitioned into adulthood spent about a third of their time playing. So when humans are kind of left to their own devices, play becomes a really primal need that we share with every other social species. And groups like the National Institute for Play have shown that we need play at every age and stage of life, from the early years through elderhood. So adults in schools also should be asking, “How can my work be more playful? How can my engagement with students be more playful?” And when play is prioritized, there are physical and psychological benefits, in addition to quality-of-learning and work benefits. Movement? Same thing: In the studies that I looked at, for “How We Thrive,” they call sitting the new smoking, saying that if you sit for six hours or longer in a day, the health harms are equivalent to a pack of cigarettes a day. And yet we tell students that a good student sits in their seat, doesn’t get up, stays seated, and then moves quickly through the hallways in a pretty controlled way. But by injecting small moments of movement or small moments of play, we improve not only the culture of our classrooms, but also, ironically, the performance and quality of work that students can do.

Speaking of play, it strikes me that what happens after school, after the bell rings, is worth paying attention to.

In doing the statewide listening tour of middle schoolers, I heard from young people every time about the vital importance of their afterschool and summer programs. And there were instances of schools that bring that kind of youth development programming into the regular school day, and in those cases that was what I heard the most about. These are activities and experiences that lead with young people’s abilities to engage socially, to engage in play, to engage creatively, to celebrate each other. In this moment where we’re talking about screens and devices and what to take away, I want us to talk about what to give back. Too many kids can’t afford or access these afterschool and summer programs, and they matter more than ever.

To bring us to the beginning of the conversation, the antidote to addictive, toxic tech is not the absence of devices, it’s the abundance of healthy, positive developmental experiences, which are often found in afterschool and summer programs and experiential project-based learning in the classroom.

Opinion: While Washington Debates Screen Time, Many Students Lack Access Altogether

Waymond Jackson — Thu, 02 Jul 2026 16:30:00 +0000

Earlier this year, to grill experts on how social media, smartphones and other technologies are affecting children’s mental health and learning. That conversation has since helped fuel a new wave of legislative action, with nearly a dozen states now considering screen-time restrictions for students. It’s an important debate.

But from where I sit in Birmingham, Alabama, the focus in Washington and in many statehouses misses a crisis just as deep and consequential as the one over whether kids spend too much time on TikTok.

The more urgent issue for millions of families is that too many children and adults lack the digital skills employers now require. That gap is not driven by overexposure to technology but by uneven access to it. Alabama is far from the only state facing this challenge. Roughly one-third of U.S. workers , even as 92% of jobs now require them.

Concerns about technology overuse deserve attention, but they shouldn’t crowd out the work of building stronger pathways into the digital economy. That means ensuring students and adults have the tools they need, along with access to the instruction and hands-on training that lead to employment. Policymakers and state leaders should be just as focused on helping communities build the workforce pipelines the economy depends on as they are on mitigating screen time.

As a parent, I understand concerns around screen time on a personal level. My wife and I think about it constantly with our 13-year-old son. Like many families, we have set limits. We decided early not to give him a smartphone until he turns 14. We would rather he spend time outdoors, read real books and experience the world away from a screen.

At the same time, I lead a nonprofit organization whose mission is to prepare young people for the future of work. From that vantage point, I know something else is true: My son’s comfort with technology will play a major role in the opportunities available to him as an adult. Those two realities must coexist, but only one seems to be driving magazine cover stories and congressional hearings.

For many students, school is the only place where they can reliably access a laptop, high-speed internet, or guidance from someone who understands how these tools actually work. In Alabama, lack adequate internet access at home. Nationwide, lack access. Unfortunately, even inside schools, opportunities to develop meaningful technology skills remain uneven. Only of U.S. high schools offer computer science courses at all, and of elementary students are enrolled in computer science learning experiences. As a result, many students never get the chance to learn how technology actually works.

The stakes around this gap are rising quickly. According to the World Economic Forum’s , technological skills are expected to grow in importance faster than any other skill category in the next five years. Artificial intelligence and big data top the list, followed by networks and cybersecurity, and technological literacy more broadly.

Students cannot easily gain these skills from worksheets. They cannot learn to code on paper alone. Educators cannot prepare students for careers in cybersecurity, robotics or digital design without placing technology directly in their hands. And students cannot meaningfully understand artificial intelligence without interacting with it. Yet many policy conversations treat technology in schools primarily as a distraction to be managed rather than a skill set to be developed.

Every school should have dedicated learning spaces where students can experiment with coding, explore AI and develop creative skills. These spaces should be guided by educators who can teach not only the technical skills, but also the ethics and responsibility required to use these tools wisely. Across the country, some schools are showing what that hands-on approach can look like.

At outside Birmingham, for example, a newly built learning lab provides students with access to a podcast studio, music production equipment and video editing tools. Students use the space to produce original music and record podcasts. Meanwhile, at Robert C. Hatch High School in Perry County, a tech-forward space — which was developed through a partnership with the State of Alabama and Ed Farm — combines in-person and remote instruction to expand learning opportunities for students in a rural district.

These schools remain the exception, not the norm, with many districts lacking funding, infrastructure and training. State and federal policymakers should treat that gap as an urgent priority. Digital fluency is now as foundational as reading, writing and math.

Parents, educators and policymakers all play a role in setting healthy boundaries around technology use. Students should not spend every hour of the school day staring at a screen, and devices should not replace teachers or human connection. But schools that lack meaningful access to technology leave students just as unprepared. If the national conversation continues to focus only on keeping technology out of students’ hands rather than putting it within their reach, too many young people will be locked out of the opportunities that define the modern workforce.

Opinion: As AI Advances, Student Voice Must Keep Pace��

Hadley — Tue, 30 Jun 2026 12:30:00 +0000

As I climbed the steps to the stage on the morning of my junior high graduation, I felt my heart racing. Just a few feet away stood a microphone and hundreds of eyes waiting for me to begin. As the commencement speaker, I had rehearsed my speech countless times, yet I had no idea this would mark the beginning of a passion for student voice.

That experience stayed with me long after graduation. It taught me something that is becoming increasingly vital in today’s digital world: Confidence is not developed by having the perfect answer but by believing in authentic ideas.

Many young students are losing confidence in their own ideas. It is undeniable my generation grew up behind screens, with videos and phones embedded in everyday life. As a 16-year-old high school student, I don’t consider artificial intelligence a futuristic invention but rather a simple extension of the world we already live in. That is why I believe many conversations about AI in schools are missing the bigger issue entirely. The real concern is not technology itself but what happens when student voices become overshadowed.

Technology has not only changed the way students learn but also the way we communicate on a daily basis. Many young people have become highly skilled at digital tools, with FaceTime and Zoom becoming frequent parts of everyday life. It is a common joke that if our generation does not put their phones down, they will forget how to talk to someone on a date!��There is immense truth behind that. As we become more accustomed to immediate responses, we are slowly losing the patience to sit with a thought.��

About 54% of students already use AI for schoolwork, a number continuing to rise. AI can certainly help students organize their thoughts. I even used it during the brainstorming process of this piece, but the important aspect is that my writing remained a reflection of my voice. With this expanding access to knowledge, an important question remains: Where does student voice fit in? If students begin relying on AI from the moment their education begins, they risk losing the discomfort that comes from developing confidence in their own ideas. That uncomfortable part matters.

I have personally experienced how being in an uncomfortable position can lead the mind to function in ways AI cannot replace. It was the day of a long-awaited DECA business conference, and I put on my dress and blazer, a stark contrast to the comfortable clothes I wear at school. I had recited my speech until I knew it by heart, yet during the presentation, I felt the pressure set in and my thoughts begin to blur.��

For a moment it felt as though all my preparation disappeared, but after taking a moment to slow down, I looked back at my notes and continued. It was not the polished performance I had imagined, but by the end I had conveyed what I wanted to say. More importantly, the experience taught me something AI never could, which was how to recover in real time.

Through these experiences, I have seen how much students can grow when they are asked to use their own voices. Recognizing this, I am proud to lead a summer program in the Chicagoland area called First Voice Academy.

Here, middle school students learn and practice public speaking in a low-stress, immersive environment. Designed to promote interpersonal connection, the program walks participants through the importance of communication and concludes with their delivering a speech on their own. Taught directly by high schoolers, the program gives younger students the opportunity to learn from peers who have faced many of the same obstacles.

I can envision a student struggling to present during a speech, but that is the key: The words are theirs. The goal is not to ignore AI, but to ensure younger generations have opportunities to develop confidence in their own ideas.��

Artificial intelligence can polish language perfectly and respond to a question in under a second, but what it cannot do is replace a student’s voice. Confidence is gained when students believe their own thoughts are worth sharing, even when they come out wrong the first time. Students who feel they have a voice at school are seven times more motivated to learn.

A program like First Voice Academy allows for real world experience while in a learning environment. If similar programs can be expanded into schools, more students would build interpersonal skills at a crucial point in their lives.��

I think back to the moment I stepped up to give my graduation speech and how scared I was. All it took was to say the first word, and suddenly I felt connected with the audience. Student voice offers a perspective unlike any other, and simply needs the opportunity to be heard. Technology may help develop ideas, but true opportunity comes from nurturing one’s own voice.

Reed Hastings on What It Will Take for AI to Be Different From Other Ed Tech

Michael B. Horn and Diane Tavenner — Thu, 25 Jun 2026 17:30:00 +0000

In this episode of Class Disrupted, Netflix founder Reed Hastings joins Michael Horn and Diane Tavenner to discuss his decades-long journey through various chapters of education reform and how it’s shaped his view around artificial intelligence shaping the space. Reflecting on the slow progress and setbacks of past education initiatives, the episode dives into the potential of and urgency for harnessing AI for accelerated, mastery-based learning and global impact. Reed shared what he believes reinventing traditional classrooms means for edtech entrepreneurs.

Listen to the episode below. A full transcript follows.

Michael Horn: Remember how much fun it was to have Reed Hastings join us on Class Disrupted in the beginning of the season to dish on AI and education? So much fun that the ASU + GSV Summit said, why don’t you invite him back and let’s do it again. So that’s what we did. Six months later, Diane Tavenner and I welcomed Reed Hastings live on stage at the ASU + GSV Summit to talk AI, his different chapters in education, lessons he’s learned, and where he thinks the puck is going. Enjoy all of that on this episode of Class Disrupted live from the ASU + GSV Summit and sponsored by the Learner Studio.

Michael Horn: Welcome everyone to the Class Disrupted podcast. This is our seventh season doing it. Normally we are disembodied voices on a screen talking with each other and a guest, but tonight we’ve got a live audience and we want to thank the ASU + GSV Summit and all of the amazing staff that has put this on. Huge thanks for all of them. Please. And you are all here to make a lot of noise and make this fun, right? Diane?

Diane Tavenner: Indeed. That’s what we want, is a spirited conversation.

Michael Horn: So who do we’ve got on tap?

Diane Tavenner: Well, tonight, Michael, we have an incredible guest, someone we’ve talked to before, but it is time to do it again. We have Reed Hastings with us. And most people know Reed from Netflix. A lot of people know that he spent time in education. What you might not know is that for the last year, Reed’s been on the board of Anthropic. He’s done a deep, deep dive in AI, the recent version, and 40 years ago earned a master’s in AI.

Michael Horn: So Reed, you’ve also had a long set of experiences with education over the years. You were a Peace Corps member, teaching maths 40 some-odd years ago, I think. And 20 some-odd years ago, you were the chair of the California State Board of Education. A testing period, No Child Left Behind. You had this guy named Roy Romer in Los Angeles as the superintendent for some seven years. What did you learn from that period of time working in education?

Leadership changes in education systems

Reed Hastings: Well, that was a time of great hope. We had No Child Left Behind, Reading First, high school exit exams. We had an accountability system and I was really an administrator of that on the State Board of Education. And we worked hard on all the technical details and there was some real progress. And as you mentioned, Roy Romer was very successful as superintendent. He made it seven years in LA Unified as superintendent, set a record for that and put in a lot of great programs that really raised scores and achievement, learning. And the tragic thing was the next five years after that, I watched it all get dismantled, independent of its results. It sort of, you know, politically wasn’t in favor.

New administrations elected, they are like, get rid of the old guy stuff and let’s put in different stuff. And so that was true at the school district level and that was true at the state level. And it really woke me up to the hero syndrome we have. And whether it’s Tom Pazant’s great work in Boston now getting dismantled or Houston, Mike Miles is the hero today. And you know, watch what will happen in five or 10 years from now, because that’s where Rod Page was so great 40 years ago. Of course there was Joel Klein in New York that so many people worked hard on. So we see this cycle of rise and fall. And I have to say, for all the work that I did and all that state board, there’s very little to show for it.

Diane Tavenner: So in another chapter where we met, was in the charter world, and you’ve been on the board of KIPP national for 20 years now. You have supported countless of us who have been in the work throughout that time, you know, the City Fund. And this is a long-term strategy for you. What are you learning from charters?

Reed Hastings: Well, I would say charters haven’t failed, but they haven’t succeeded at driving up NAEP scores in the high charter states, say like Arizona, Texas, Florida. And of course unions have fought us to a draw in deep blue states and then in red states we’re able to grow and we’re investing in, again, Florida, Texas, Arizona, Georgia, so lots of states. But even, you know, after 20 years, we have a good success at the city level. So at the city level, it’s actually the only thing that’s driven citywide improvement for all kids is high charter share. So if you look, PPI did the graph, the scatter plot showing that cities with low charter share, Portland, Seattle, have had no improvement in closing the gap of achievement between poor kids and all kids over the last 25 years. And then you start walking up the cities that have 10% charter share, more improvement, 20, 30, 40, 50, like Newark, Camden. And then you get to New Orleans, which has the highest gap closure in the nation over the last 25 years. And of course that’s 100% charter.

So charter is still promising, but like grindingly hard and slow. Think trench warfare, but it hasn’t been reversed. OK, so a lot of positivity and I continue to be a huge donor in that space and continue to believe in it on maybe a half dozen boards of charter networks.

Michael Horn: So the third chapter that you then went in on an education is when I met you, 2010, the very first ASU GSV summit, you were there and you were getting involved in education technology, EdTech and DreamBox Learning, of course. And there’s been a whole wave, sort of cresting, if you will, with EdTech. What’s your take on that chapter?

Reed Hastings: Yeah, well, Rocketship was using DreamBox Learning, and I knew it through there, and I thought, OK, here’s a great opportunity to take this amazing software. And obviously computers transform everything. And so if we could just get some investment in DreamBox and get it bigger, it would surely transform both district schools and charters. And again, grindingly slow. Turns out that selling to school districts is really hard. The only thing harder is selling to charters because they’re small, so the money’s on the district side, but grindingly so. And then, you know, DreamBox was one of the early adaptive learning, you know, let kids go at their own pay systems.

But school districts kept telling us to turn that off, please, because they wanted to catch kids up to grade level, but they definitely didn’t want to get kids ahead because if the kid gets ahead, then they’re disruptive and bored in the class. So catching kids up to make the machine work better, very much valued. Letting kids get ahead, which sort of threw sand in the machine, not valued. And so it was an early lesson in sort of the depth and strength of the grammar of schooling that we have.

Diane Tavenner: So if I sum up these three chapters, state policy, district work, pop of success gets wiped away. Charters making progress haven’t failed, grindingly slow ed tech, no real discernible change yet. I know you’re not trying to depress us. I know you’re trying to help us know that you’re learning and still in the game, which we know you are. So let’s get back to AI. When’s it going to cure cancer? When is it going to figure out fusion so energy is free? When is it going to autonomously drive us all over the place so we don’t have to deal with parking lots anymore? When is it going to make our lives better?

Rapid AI advancements predictions

Reed Hastings: By the end of the summer? Predicting AI is tricky because it’s growing so fast in quality. You know, it was three years ago when ChatGPT came out and it could barely do third grade math. And now all of the major AI systems are very impressive and they’ll continue to improve. And what’s happening is we’re on one of these curves where it’s, let’s call it doubling every year in quality. So it will be twice as good as it is today a year from now, and then twice as good, and then twice as good and then twice as good. So whatever challenge you think AI is not up to, just wait a year. OK? And so that’s the amazing thing. And there’s no guarantee that the exponential will continue forever, but it has been the last several years and you know, it’s getting very, very impressive at many scenarios like the ones you talked about and many others.

So the amount of change that we’re going to see in our society, mostly positive, but there’ll be some negative, from AI getting better and better is hard to grasp because of this doubling, doubling, doubling. You know, just when you think we’ve got it like situated like how’s it going to work with society? Then it gets even better again. And so we’re in for the ride of our lives, both on the positive side. So curing cancer, energy, you know, abundance, these kinds of things and on the stress side of everything is different than it was when we grew up.

Michael Horn: Well, that’s the question I want to ask you because not only is there this anxiety and stress, as you know, people are also worried, will people get hurt as it gets better? And you know, you can imagine a myriad of ways that could play out. What’s your take on how do we prevent people from getting hurt?

Reed Hastings: Yeah, and I mean, again, that’s happened with some tragic cases of, you know, teens and suicide already. And look at the societal level, we make certain choices, sometimes explicitly, sometimes implicitly. And we tend to accept the choices that are already made for us and be scared, scared about new ones. But for example, you know, we lose 40,000 people a year to car accidents in the U.S. and about half a million globally. And if we just ban cars, you know, we wouldn’t have those deaths. OK, but we’re not willing to pay the price. So implicitly we’re making a trade off of 40,000 U.S. deaths a year. So I look at it and say, you know, is it as powerful as a car? And if it is, then I’m like, I know where society is in making those trade offs.

So I don’t want to pay that price. I don’t want to see 400,000 or 40,000 a year deaths. But I think when we get all excited about four deaths, we’re sort of losing perspective about the size of the prize and the other trade offs that we have and continue to make in society. So, you know, AI, I think will reduce deaths, and in particular with self-driving cars, that should be able to eliminate 90% of those 40,000 US deaths through self driving if we can get that adopted. OK, but then you see the story of the one Tesla death that happens. And again, that death’s tragic. I’m not trying to take away from it, of course, but in comparison to all the lives that self driving is already saving, it’s quite small.

Diane Tavenner: So let’s take that into education now, because one of the things that I love about you is that you keep learning and you stay in the work when a lot of people leave, and I know that there is a fourth chapter that is going to be written in your work and it’s going to involve AI. And so what does education look like in the age of AI? What does school look like in the age of AI? What does learning look like in the age of AI?

Improving education over the years

Reed Hastings: Yeah, but in my first 25 years, I’ve spent the time trying to do the better classroom, whether that’s from the state board level and testing and assessment, how do we make schools and classrooms better, whether that’s using ed tech like DreamBox Learning to make the classroom better. Charter schools, which have had some progress in making the classroom better. But it reminds me of the story about steam powered factories in the 1800s. So in the 1800s, all of our factories had a big steam plant that burned coal and rotated an engine. And then throughout the factory we had a rotating rod which carried power through the plant. And then we had belts and pulleys and wheels that then spun the individual looms or other machines. And these were highly developed, mechanized, and lots of belts and pulleys throughout the factory. You know, lot of productivity.

Then electricity comes and we replace the big steam engine with a big electric engine. And that saves some money. But real productivity of the factories didn’t change. And this puzzled economists for a long time. And then people started saying, hey, the power distribution system, all those pulleys and rods spinning, that’s the problem. And if we get rid of that and then go to individualized electric motors, so each loom has its own motor, then it can be designed sideways because the power is not all in one direction. Then it’s variable speed. You can turn off some motors and turn on other ones and all of these subtle effects.

Then we had a huge increase in factory productivity from basically using electricity the way it should be used in lots of small, relevant motors, rather than replace the one big motor. And I remember hearing that story and thinking, oh my gosh, that’s what’s happening in education. We’re putting tech into the classroom and the classroom, the sage on a stage, is the power distribution system. The sage on a stage is holding back technology from its natural effects and its ability to teach children directly. And we have to be brave enough to try to do school without sage on a stage at all. OK? To have all of school be learning individually, your daily lesson plan from the system executing.

Experimenting with individualized tutoring

Reed Hastings: We want to maintain the social development so the person in the classroom really becomes a social worker. They’re specializing in learning and emotional maturity and doing valor-type circles and these kinds of things. But the quote “education learning” stuff all becomes individualized where it’s mastery based learning. And the question is, how much more would kids learn? So one experimental way to get at this is to think about Bloom 40 years ago, and Bloom said two sigma improvement from individual tutoring, but it hasn’t been revalidated in a large scale way in a while. And, and so one of the projects we’re doing is funding that and you know, take 50 random kids, median kids in a median school, and give them a full year of the whole school day individual tutoring and try to figure out, OK, how much more do they learn? And so Ben Rosen, who’s here at the conference, runs Recess.gg, he’s running this project and recruiting tutors. And so let’s see, for second graders in the ideal condition, how much can they learn? What is the rate of learning of typical human 7 year olds? And I think we’re going to see it’s a whole lot faster than one grade level in one year, when again, completely individualized tutor, they can do everything moral and legal. They want to help the kid learn more in that year. All kinds of motivational things, all kinds of different teaching techniques.

But again, it’s one on one, dedicated. And you might say, well look, you know, that’s so expensive, $100,000 per kid per year. It’s ridiculous. And I would say that’s what it is now. But with AI, it gives all the AI developers a target of what they’re trying to do and how much more learning. And what we want the world to understand is, no, there really is twice as much learning that could be happening per day, per hour than today, because I suspect that we’ll find that it is twice as much, which roughly means by the time you get to eighth grade, you know as much as a typical high schooler today or by the time you get to 11th grade, you know, as much as the typical college student today. OK? Because of the time compression and the learning and the stimulation.

And that would lead to, you know, not just lifting the bottom, which of course it does, but just a tremendous revolution in the possibilities of the human brain. And there’s a positive example of this. So about 25 years ago, Deep Blue beat Garry Kasparov in chess. And from then on, AI chess has been better than human chess. And so you might think, well, everyone stopped playing chess and it’s kind of gotten irrelevant. But in fact, chess has grown. And now the typical 10 year old on Chess.com is scoring way higher than the 10 year olds of 20 years ago on a stable, vertically scored system. And what’s happening is the 10 year olds are getting tutored by AI and the 12 year olds and 14 year olds.

And so we’re seeing this rise in chess talent because they’re individually tutored by AI. And so that’s true for chess today and could be true for biology and history tomorrow.

Diane Tavenner: I know Michael has a lot of questions, but before we just move, hold, hold. Because I don’t want this to get lost. And I think people often get confused when we talk about the power of individual tutoring. And they think kids are going to be learning by themselves. And that is not what you’re saying here. I know that’s not what you’re saying.

Reed Hastings: A dark room, nothing there, locked in. We can reuse containers. No, you want all the social development that we have today. So it’s real.

Diane Tavenner: Because those chess kids are playing chess with other kids.

Reed Hastings: That’s right. And if you just take the chess, if you just take the school day and say the time that’s direct instruction, sage on the stage now becomes individualized tutoring. And all the play time and all the time that’s do a project together stays as that. And in fact you can be. The teachers can then focus on that aspect of the day. And again, social, emotional learning, we all know is important. But imagine if the teacher’s an expert in it and focuses on that because understanding and doing well on the stuff that’s tested is done by the software.

Diane Tavenner: And by the way, sage on the stage is a very lonely experience anyway, so let’s not pretend.

Michael Horn: Speaking from experience. Well, I was gonna say you’re gonna finally disrupt class, which I’m thrilled by, but yes. But I’m curious because I talk to a lot of ed tech entrepreneurs at this conference and elsewhere. What’s your advice to them? Because they do a lot of times what DreamBox did, right, which is sell to the existing system, the districts, the schools, the sage on the stage. What’s your advice to them?

Reed Hastings: Yeah, it’s a great point. The short term is if you want to make money selling to school districts, make teachers’ lives easier. OK, don’t worry about learning too much. But if you make teachers’ lives easier, you’ll sell well. If you want to change the world, focus on the homeschoolers. Focus on people who are able to go at their own pace and build systems that are individualized. And as the benefits of that are more and more clear, not meaning 5%, but meaning twice as much learning, school districts will move towards that.

Self-learning education technology

Reed Hastings: And so if you build that now, you’re skating to where the puck is going, which is this individualized education. And so think of it as trying to do the pure play where you don’t need a teacher. OK? It is the self driving car where most of the market is like the map in the car to help the human. OK? That’s where most of our ed tech is. And instead we need to build the self-driving car in terms of innovation, which is the self learning, self teaching. And again, the AI is getting better and better at the emotional motivation.

So when you, you know, the vast majority of people seeking therapy today are getting therapy from chat, not from waiting a week and going and seeing someone at 80 bucks an hour. It vastly expanded the market. And you can say, well, it’s uncertified and that’s all true, but it is satisfying to people and it’s not perfect in any way. It is getting better and better rapidly back to that doubling. OK? And so the understanding, the emotional nuance of humans is something that actually the software is, is quite good at and getting better.

Diane Tavenner: And we could talk for days and days about how this leads to agency and self direction and entrepreneurial spirit and when they’re getting what they need.

Reed Hastings: Yeah, once you learn how to learn from software and from the interaction, the world’s your oyster because then you go off and you want to do physics or you want to do history again, a lot of it is there.

Diane Tavenner: So before. Yeah, let’s take it to the world. So what does this mean to the world you are working globally? CJ is here in the audience with us. Tell us about your work in Africa.

Sharing AI education globally

Reed Hastings: Yeah, it’s one of the most exciting secondary effects of this AI revolution is it’s very shareable when we figure out good teaching practices like Success Academy or KIPP, it’s very hard to export that to a Brazilian or African context. But when you figure out tech, it’s very easy to share. So, you know, if you think of Kibera outside Nairobi, people live in, you know, hundred or thousand dollar homes, you know, a piece of corrugated tin compared to our, you know, half-million, million dollar homes. So it’s wildly different, right? But if you think of their phones, it runs basically the same operating system that we run, it’s the same apps. It’s like barely any different. And so if we can figure out software based AI teaching that really does all the work, we can share that with the entire world. And so the project that CJ’s leading is trying to figure out one tablet per child in Rwanda, which is a great test lab. If that works as we hope, we’ll do the hardware and operating system level, and various application developers in the U.S. will do amazing work there.

We’ll put those together, and we’ll see Rwanda rise to be the most successful education state, first in Africa, maybe in the world. And that will then prove at that point, which is the formula is really one tablet per child around the world.

Diane Tavenner: No pressure, CJ, no pressure. Number one in the world.

Michael Horn: We’re going to get all these people you’re working with, lots of attention out of this and so that we can multiply these efforts. Live from the ASU GSV Summit. Thank you, Reed, for joining us on Class Disrupted.

Disclosure: Reed Hastings was a founding board member of The City Fund, which provides financial support to ��.

This episode is sponsored by LearnerStudio.

Study: Giving Kids Access to AI Tutors Doesn’t Mean They’ll Use Them

Greg Toppo — Wed, 17 Jun 2026 04:01:00 +0000

Ed tech companies routinely pitch AI tutoring platforms as a way to deliver personalized instruction at a scale that no human teacher can match. But when researchers from Stanford University looked at how much students actually used one major AI platform, something startling happened: Students didn’t use it that much at all.

In the study, , two unnamed school districts carved out dedicated time for hundreds of elementary school students to work with a well-known AI reading tutor, either during class time or after school. Researchers followed about 350 students across two randomized controlled trials. All of the students were expected to log on for at least two 30-minute sessions a week.

They found that of the students assigned to work independently with the AI, just over 60% in the first district and 53% in the second ever logged on to the platform — at all.

Among all students, average weekly usage came to just over two minutes in District A and just over five minutes in District B.

Those who did log on averaged 13.2 minutes a week in District A and 25.8 minutes in District B, using the tutoring for just four to five weeks on average in an “intervention window” that ran from 14 to 31 weeks.

For Carly Robinson, the paper’s lead author and research director for the , the gap between access and use isn’t a shock. “As we’re talking about bringing AI tools into the classroom, the challenge isn’t just building good AI tools,” she said. “It’s getting students to use them and engage with them effectively.”

That’s going to take “intentional design” that appeals to both students and their teachers, who must choose whether to offer access.

“Having these tools available, even if they’re really good, doesn’t necessarily mean they’re going to get used if they’re not being embedded into kids’ learning experiences,” Robinson said in an interview.

Carly Robinson

But she was careful to note that the study didn’t draw conclusions about AI’s effectiveness, or the degree to which students were interested or uninterested in the bot, saying many factors could be at play. “This is not necessarily the students not engaging,” she said. In the two districts, the AI platform “was likely one of many tools available to teachers.”

For the study, researchers randomly assigned a group of students to work on the platform alongside a few classmates and a human tutor whose job was to support their engagement and motivation and to troubleshoot any problems students might encounter. In District B, the tutors were actually middle-school students who “had a free intervention block in their school day.” A typical session included a short check-in, 15 minutes on the platform and a few minutes of reflection.

Pairing students with a tutor worked, Robinson said — to a point. Usage increased by roughly one minute a week in District A and 4.4 minutes in District B. The number of stories students completed each week jumped 71% in District A and 80% in District B.

What the human pairing didn’t do was move the needle on reading scores: Neither district saw a statistically significant improvement in end-of-year reading achievement. But Robinson said the study wasn’t primarily focused on that. Rather it was looking at the overall impact of adding a human into the equation, someone who provides “accountability, motivation and relationship building.”

Wednesday’s findings mirror recent ones from Khan Academy founder Sal Khan, who that the rollout of his in 2023 was “a non-event” for many students. “They just didn’t use it much.”

Khan said AI tutoring doesn’t necessarily make students motivated to learn, or to fill in gaps in their knowledge needed to ask questions.

The new data also raise an uncomfortable question for educators: Among students who used the platform on their own, those who logged on tended to be higher-achieving and less likely to receive special education services. So the students who stood to benefit most from extra reading practice were among the least likely to get it.

Robinson said she sees that as a red flag for anyone considering AI tutoring as a quick fix for underserved students: “I think it should give us pause about treating AI tutoring as an equity solution.”

Alex Sarlin

Alex Sarlin, founder of the newsletter and a veteran industry watcher, said the new study “shines a light on several of the most persistent challenges in ed tech implementation: low usage rates that don’t meet dosage recommendations, differential technology usage based on prior student achievement, leading to lower usage among the neediest students, and a faulty assumption that students will jump into new tools without structured guidance.”��

The researchers’ approach showcases a promising direction, he said, “as it is increasingly clear that providing access to tooling is not nearly enough to drive usage, let alone outcomes.”

Amanda Bickerstaff, co-founder and CEO of , which provides AI literacy training to teachers, said results like these aren’t all that surprising, given what we know about these tools.

Amanda Bickerstaff

All GenAI chatbots, she said, can make mistakes, lack important context about students and how they learn best, and can provide biased outputs. Her group has recommended keeping these tools out of the hands of students through second grade, “and only with significant human oversight and AI literacy training” for students in grades three through five.

“At this stage, there has been little evidence that GenAI chatbot tutors meaningfully impact learning outcomes for students,” she said, “or that they are developmentally appropriate for students in elementary schools.”

Robinson, the study’s lead author, said she sees the usage findings as part of a larger pattern playing out as schools adopt AI tools more broadly. Schools, she said, should consider offering students “different iterations of these things based on what they actually need — and that’s probably a more likely pathway to scale than just saying, ‘Let’s give everyone an AI tutor.’ ”

Historically, personalized instruction has depended almost entirely on human teachers, with the teacher-student relationship central to the experience. But advances in technology — most recently in AI — have changed this dynamic, Robinson and her colleagues write. Now, personalized instruction exists on what they term “a spectrum of relational intensity,” from a consistent one-on-one human tutor to a computer platform that students navigate alone.

AI tutors may approximate human interactions, Robinson said, but students may still benefit from the care and companionship that humans provide. Logging on and sticking with something that might prove to be difficult, she said, is easier with a human in the mix. “There is just this component of accountability that a human can provide, where it’s so easy to look away or check out of something when it gets hard when you’re dealing with a screen.”

Survey: Young People Turn to AI to Be ‘Their Real, Unfiltered Selves’

Greg Toppo — Mon, 15 Jun 2026 10:30:00 +0000

Alison Lee still remembers the conversation that helped her see why young people turn to the safety of artificial intelligence for companionship and belonging. She was talking to a high school student and the girl told her, “Nobody dances at prom anymore.”

A researcher at , a nonprofit focused on human connection in the age of AI, Lee asked: Why not?

In a word, the girl said: Instagram.

“If you try to dance at prom, you’re going to look stupid at some point,” Lee recalled her saying. Eventually someone will pull out a phone and you’ll end up on someone’s feed, seen by “the entire school” with mortifying results. Better just to play it safe.

“Everybody just goes to prom to look cute,” the girl explained, “take a picture for the ‘gram, eat and leave.”

Alison Lee

For Lee, who has spent years studying human belonging, that exchange unlocked an important, if unspoken, part of why AI holds such appeal. “We’ve created this set of conditions where young people don’t feel like they have permission to be their real, unfiltered selves,” she said in an interview. So they turn to AI, which is programmed to affirm them at every step.

from Lee and her colleagues offer this insight among others, painting a detailed portrait of how young people use AI and why. They surveyed 2,383 people ages 13 to 24 across the U.S. and found that for nearly half of them, AI has already reshaped their relationships in ways that are largely flying under the radar of parents, teachers and policymakers.

Among the findings:

Just 15% of young people are in relationships with “personified AI” characters — but for about 45%, AI is already reshaping their real-life relationships;
53% of young people say they set clear boundaries with AI, using it alongside — not instead of — human support;
61% say parents rarely or never talk to them about AI, and 53% say the same about teachers;
Youth from low-income households are three times less likely as others to engage with AI, but they report greater feeling: 21% feel lonely often or all the time, compared to 6% of high-income youth; 57% feel like a burden to others, compared to 42%; and only 34% feel a strong sense of belonging at school, compared to 62%.

For the study, researchers sorted respondents into four broad clusters. About 28% rarely or never use AI, often out of ethical reasons or just disinterest. The largest group, 39%, uses AI primarily as a practical tool. They turn to chatbots such as Claude, ChatGPT and Google’s Gemini for homework and research, while keeping clear boundaries between AI and their emotional lives.

Another 18% use AI for personal and relational support, such as venting about a tough day, seeking relationship advice and processing emotions. And 15% engage with AI characters and personas in more intimate, companion-like ways.

Within the four groups, researchers found nine variations that challenge the conventional wisdom around AI use. For instance, among those who use AI for emotional support were two very different groups. Rithm calls them “Social Processors” and “Private Processors.” While they may look similar from the outside — both say they have lots of friends and use AI to work through their emotions — surveys found that the Social Processors use AI as just one tool among many. The Private Processors, by contrast, use it as a substitute for real human interactions because they feel they can’t bring problems to those around them.

“I started using it once, I guess, I realized people got tired of me complaining about the same thing over and over again. And I didn’t want to keep burdening people about the same issue.”
24-year-old male participant of The Rithm Project’s study

That data point could hold the key to understanding problematic AI use, Lee and her colleagues said, challenging the idea that lonely teens with small social circles are most at risk of unhealthy AI dependence. The data suggest something else altogether, said Kashyap Rajesh, a rising junior at Cornell University who consulted on the report.

“The driver of risky AI use is not necessarily isolation,” he said. “It’s feeling like a burden [to others] — and that came through in the research.”

The number of friends a young person has, the size of their social circle, how busy they are, whether they’ve got family nearby and even their feelings of loneliness barely predict whether they’ll fall into dependent AI use, he said. “What actually predicts it is specific feelings: Feeling like a burden to others, feeling like you can’t be your real self, feeling like there’s no one to turn to.”

Julia Freeland Fisher

Julia Freeland Fisher, a researcher at the Clayton Christensen Institute who advised on the study, said that finding should help start a different kind of conversation around AI. “Burdening one another is building reciprocity, which is how we maintain the social contract, how we maintain social cohesion,” she said. That young people are increasingly bypassing this step should be alarming, she said.

“AI companions wouldn’t be nearly so disruptive to human connection if we had a sturdier social fabric,” said Fisher. “It’s the weakness of our social fabric that makes these [findings] so worrisome, not necessarily the technology itself.”

‘It just keeps feeling easier than the alternative’

For Lee, the finding on being a burden reframes so much of our understanding about young people’s relationship to AI. Virtually every survey respondent reported a specific “relational rupture” or crisis that made them turn to the technology.

One young woman’s first question to a chatbot was, “I didn’t get asked to Homecoming — am I unlovable?” Another: “I got into a huge fight with my best friend, and I don’t want to tell anybody else because I don’t want them to take sides, so I needed to ask AI.”

“Story after story after story,” Lee recalled, “of a very singular, acute, discrete moment when they really had a moment of need and needed somewhere to put it.”

Rajesh, the Cornell student, said the data reveal a steady shift in which perhaps millions of young people are quietly moving from letting AI help with homework to asking it to mediate their emotional lives.

“They start off using it to help them write an essay, or help them prepare for their interview, or to study for an exam,” he said. “And they’re like, ‘OK, damn, this is really good, this is really helpful.’ And eventually their interactions escalate.”

Kashyap Rajesh

The drift happens gradually, he said. AI helps draft an email or respond to a text. Next it’s helping to navigate a social situation. Before long it’s processing a breakup.

Rajesh, who’s studying information science and AI policy, said his own AI use crept up on him: He went from studying with Claude to creating personalized AI study guides to wondering if even attending class mattered.

“I found that how many times I go to class and how actively I’m paying attention in class is actually not the biggest indicator of my understanding of the content or exam performance,” he said. “It’s actually just how much time I spend with Claude dissecting the lecture slides and building study guides that work for me.”

The report notes that because even productivity-focused platforms like ChatGPT, Gemini and Claude are engineered to interact with warmth and reassurance, what starts out as homework help or playful experimentation can evolve into a substitute for human interaction.

“Nobody wakes up and decides they want AI to be their emotional support system. It just keeps feeling easier than the alternative. And so by the time you notice it, the habit is already there.”
Kashyap Rajesh

What adults get wrong

Alongside the findings on AI use, researchers found that how adults talk about AI is also potentially problematic: Their conversations are almost always about academic integrity — cheating, plagiarism, source citation — and rarely about relationships.

Rajesh said adults should be asking directly whether young people are using AI to process emotions, to rehearse hard conversations and to get support when they’re struggling. “Those are questions that signal to a young person that the adult knows this dimension exists and isn’t going to freak out about it — which is, I think, the prerequisite for any honest conversation happening at all.”

Michelle Culver, the Rithm Project’s founder and a co-author of the report, said young people tell researchers that when the topic is AI use, they’re “navigating it alone.” She suggested that adults approach the topic with “curiosity” rather than “judgment or shaming.” That could help both sides gain insight into each others’ struggles in the face of a technology that’s constantly challenging their reality.

Michelle Culver

In the same way that educators are worried that young people aren’t engaging in the “productive struggle” of learning academic content, Culver said, “We similarly worry that young people might offload the relational work to AI and become ill-equipped to handle the very messy human friction of real relationships.”

As AI Use in Schools Grows, Lawmakers and Districts Scramble to Set Up Guardrails

Robbie Sequeira — Fri, 12 Jun 2026 16:30:00 +0000

This article was originally published in

With many students and educators already using widely available artificial intelligence tools, state lawmakers and school districts are playing catch-up on AI policies.

In Maryland, for example, AI usage policies for K-12 schools are “all over the map,” Democratic state Sen. Katie Fry Hester said.

In some school districts, she said, AI use is encouraged, while in others it is restricted, or —��a worst-case scenario for Hester — there is little to no policy guidance at all.

“What we heard repeatedly is that the teachers were feeling like they had to navigate artificial intelligence entirely on their own,” Hester said.

Hester said square one for lawmakers is AI literacy, which was the aim of new legislation that she sponsored and that was signed into law in May. It requires an AI coordinator in each school system, a statewide AI professional development for teachers and AI literacy to be a component of career readiness and computer science standards for K-12 students. It also requires the state Department of Education to provide certain guidance on AI.

Many other states have also been trying to create AI policies for schools. Lawmakers filed more than 134 bills across 31 states this year related to AI in education, focusing on data privacy, usage restriction in the classroom, literacy and training, according to MultiState, a government relations firm.

A survey by the Center for Democracy & Technology showed that (85%) reported using AI in their classroom during the 2024-25 school year, while 86% of students said they’d used AI for either personal or school-related reasons. But only about half of teachers and students reported that they received some training or information about AI from someone at their school, and few received training or information on risks of AI use.

A turning point for schools came with the rollout of ChatGPT in 2022, said Noelle Ellerson Ng, chief advocacy and governance officer for the School Superintendents Association. “AI was something that could not be gatekept,” said Ellerson Ng. “It was in the classroom the minute students were able to access it.”

Her association does not take positions on state AI bills or policies. But she said districts are trying to avoid knee-jerk, reactive policies such as New York City’s brief 2022 ban of ChatGPT because of fears about cheating.

Some states have made progress in laying the groundwork for AI policy in K-12.

Ohio has set a July 1 deadline for every school district, community school and STEM school to adopt an AI use policy. The state’s model policy recommends that districts address student and staff uses, privacy, ethical use, teacher-specific uses, vendor agreements, third-party AI tools and student assessments.

A new signed in March requires local school districts and charter schools to devise local policies for AI usage in K-12 schools, requires state standards for AI literacy and education training and ensures that no AI “replaces or eliminates a human teacher.”

enacted last month requires AI tools to be age-appropriate and requires teachers to review anything AI produces before using it in the classroom. It also allows parents to opt their children out of using AI tools. The law also directs the state education department to develop AI guidance and requires local school boards to set policies before the 2027-28 school year.

Yet even as schools are being sold on AI products by numerous vendors, there’s a growing skepticism about AI in classrooms. It follows a similar backlash about social media and digital technology’s academic and mental health effects on students, which has led to more states and districts putting in place bans and rethinking their reliance on laptops.

In the Center for Democracy & Technology survey, half of students said using AI in class made them feel less connected to their teachers, and 70% of teachers said they were concerned that students’ use of AI was preventing them from learning important skills.

Schools need to weigh the benefits of adopting AI tools in the classroom against their effect on student privacy, mental health and social skills, said Sue Thotz, director of outreach for Common Sense Media, a nonprofit advocacy group focused on technology and its effect on children and families.

Schools, Thotz said, may be the “only mandated safe space” where students can learn to use and access emerging technology. But she and other education experts believe districts need to increase scrutiny of products.

Globally, the market for AI products in K-12 schools was worth around $391.2 million in 2024, and could rise to more than $9 billion by 2034, , a market research company. That includes AI products for tutoring, personalized learning, automated grading, lesson planning and administrative tasks.

“When I talk about AI literacy, it’s not how to use AI. It’s understanding how AI is built,” said Thotz. “Why is it being created? Who’s profiting off of this?”

‘Giving a tool to children’

New York Assemblymember Robert Carroll said he uses artificial intelligence in his own work and sees its value. As someone who struggled with dyslexia as a child, he also thinks technology can help students with disabilities.

But he also wants to keep AI out of most K-8 classroom instruction. Students should learn basic subject matter first — in conjunction with critical thinking — and then later use the tools that can assist them, he said.

Carroll, a Democrat, has that would prohibit the use of most AI in K-8 classrooms, with exceptions for diagnostic testing and support for students with disabilities.

“It is imperative that all children gain strong foundational skills, especially in literacy and numeracy, and it seems that AI is uniquely positioned to possibly undermine that,” he said. “There’s a difference between giving a tool to adults and giving a tool to children who have yet to master skills.”

Rather than full bans, most bills seeking to restrict AI have opted to focus on age restrictions, parental opt-outs, oversight and bans on using AI to replace teachers.

This year, Florida’s would have included a statewide restriction on student access to AI instructional tools before sixth grade, with exceptions for use supervised by school personnel, English-learner translation support and disability accommodations. It overwhelmingly passed the Senate 37-1, but died in the House.

A adds computer science to the required public school curriculum, including AI and emerging technologies. Connecticut lawmakers in 2025 failed to pass aiming to stop AI from “replacing” public school educators.

Sophia Romee, the general manager of the GenAI Studio, an initiative studying how students and educators use generative AI at the College Board, the nonprofit that administers the Advanced Placement curriculum and SAT tests for high schools, said she is concerned that only that allow students to use generative AI have a formal policy governing its use.

The College Board’s research, Romee said, shows many students are worried about becoming too reliant on AI, and that adults need to give clearer guidance about where using AI tools for brainstorming, revising and tutoring crosses the ethical line into cheating.

“Students are far more self-aware about AI’s risks than headlines suggest.”

Like aviation in 1905

Jason Coley, director of the Center for Academic Innovation at Maria College in Albany, New York, said the policy debate needs to move beyond whether schools are “for” or “against” the use of AI.

“The better question is what kinds of AI use are supervised, age appropriate, transparent, and tied to real learning,” Coley said. Schools need guardrails around privacy, student data, bias, teacher training and equity of access, he said, but also permission to “experiment responsibly.”

Ellerson Ng, of the School Superintendents Association, said superintendents see AI as part of a larger umbrella of disruptive technologies in schools that has evolved from calculators to laptops to cellphones. The lesson, she said, is that overreactive policy rarely works. She also said schools should not cover AI in a separate policy, but as part of a broader technology policy.

“I don’t have a calculator policy. Why would I have an AI policy?” she said, describing how some district leaders think about the issue. “I have a technology policy.”

With past technologies such as cellphones and laptops, adults could often control when students had access, Ellerson Ng said. With AI apps and platforms, many students accessed the tools before teachers, principals or state officials were even aware of them.

That makes bans difficult, she said. Schools can block tools on school-owned devices and networks, but “you’re only one personal device away from social media and AI being in your schools.”

Justin Reich, an associate professor of digital media at MIT, said that uncertainty around AI should make policymakers cautious about declaring best practices too soon.

Reich said states are trying to regulate classroom AI at a moment when the field is still so unstable that “writing a guide for AI in 2026 is like writing a guide for aviation in 1905” before airlines, airports or even commercial flight.

“If you were to take any of the AI literacy documents, AI readiness documents, even the moratorium documents, and put them against a checklist,” said Reich, “there would be a lot of boxes in the ‘we’re making this up’ column and not a lot in the ‘we have evidence’ column.”

State lawmakers and school districts should be honest that they don’t know what they’re doing, are relying on limited expert information and that policy is subject to change with new information, Reich said.

“Lawmakers will need to be honest that what they propose now could be completely outdated in two years.”

is part of States Newsroom, a nonprofit news network supported by grants and a coalition of donors as a 501c(3) public charity. Stateline maintains editorial independence. Contact Editor Scott S. Greenberger for questions: info@stateline.org.

Opinion: From Tutoring to Translation Help, Crowdfunding Shows Ways Teachers Use AI

Alix Guerrier — Tue, 09 Jun 2026 16:30:00 +0000

Thousands of teachers are demonstrating each school day how to get artificial intelligence in education right. Policymakers, school system leaders and supporters of K-12 education should pay attention.

I have an unusual window into what’s happening in classrooms as CEO of DonorsChoose, which provides resources in 90% of U.S. public schools. Each year, 200,000 teachers post requests on our site.

Since the 2022–23 school year, requests for AI-related tools have surged more than 200%. But what’s interesting isn’t the growth. It’s the purpose.

Teachers are asking, overwhelmingly, for AI-enabled tools to reach students who have been left behind for decades: kids with disabilities as well as those learning English. In fact, 86% of requests are aimed at meeting the needs of students who have historically been underserved. In other words, teachers are turning to AI not only to save themselves time (although it can do that); nearly 9 in 10 are using it to get essential tools to the students who need them most.

For example, a middle school teacher near Atlanta requested AI-powered translation pens. With a simple scan, students can hear text read aloud or translated into more than 100 languages. For children who are learning English, or who struggle with reading comprehension, a $90 pen transforms their school day from frustrating to fulfilling. DonorsChoose has provided hundreds of these pens to teachers, along with more than 1,500 translation devices of other types.

In Chicago, an elementary school STEM teacher looked to AI to modify classroom materials when a child isn’t understanding a lesson.

In Miami, a middle school math teacher requested software that responds to students’ answers with immediate feedback that builds confidence rather than deflating it. Meanwhile, at another Miami middle school, a computer science teacher helps students get under the hood of machine learning by training robots to recognize and react to images. The project opens up discussions about ethics, real-world applications and how AI depends on what humans feed it.

In Detroit, high school educator Carrie Russell uses AI tools to effectively give every student a personalized tutor, expanding her capacity to teach each learner. She’s also mentoring other teachers about how to ethically and confidently incorporate AI tools into student learning.

These teachers aren’t asking for anti-cheating software or ways to monitor screen time, which is where much of the public debate is focused. They are experimenting and adapting tools that work for themselves and their students, without waiting for top-down guidance.

It shouldn’t be surprising that teachers are forging ahead and deploying AI in practical ways without directives from their schools and districts. Teachers have always been first responders to children’s needs.

In 2011, when American education underwent a seismic shift with states’ introduction of new academic standards, classroom teachers sounded the alarm on poor curriculum quality and misalignment to the new standards. Instead of waiting for the market or policy to catch up, they created materials that met the higher bar — and shared them with peers.

More recently, on DonorsChoose, educators flagged the COVID pandemic’s effects on student mental health long before they became a national concern. We saw teachers request food for hungry students when SNAP benefits were disrupted last fall. And we routinely see teachers mobilize following natural disasters to replace what’s suddenly gone from their classrooms and restore some normalcy in their communities.

AI is the latest disrupter in education. It’s an opportunity to move toward a future when technology expands human potential rather than replaces it, where fairness is built into the design and where every student can experience moments of joy, discovery and magic. Teachers are showing what that can look like — one classroom at a time.

The AI App Seeking to Scale ‘Magic’ in the Classroom

Michael B. Horn and Diane Tavenner — Fri, 22 May 2026 14:30:00 +0000

MagicSchool has emerged as a breakout artificial intelligence tool in education, with millions of teachers rapidly adopting it. But what’s behind that growth? How exactly does it use AI? And what does its adoption mean for teaching and learning? Its founder, Adeel Khan, joined Class Disrupted hosts Michael Horn and Diane Tavenner to go beyond the hype. Tavenner and Horn pushed for answers on the MagicSchool’s quality, privacy, and whether tools like this actually improve outcomes for students.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane. It is good to see you. As always, it’s been fun because we’ve had this arc of really digging deep on AI with school models. And then we’ve been moving into the edtech tools that are starting to define a lot of current models, the new models we might see and so forth. And we’ve got another big one. Dare I say, for today, this is going to be a good conversation.

Diane Tavenner: I am very much looking forward to it and looking forward to digging in. But before we do that, we have a quick ask for all of our listeners. Will you all rate or review Class Disrupted, wherever you’re listening to it? And of course, please subscribe. We’ve never asked anyone to do this in seven seasons. And you know, this is — it turns out, this matters. So we’d really appreciate it if you would do that.

Michael Horn: Yes, we would appreciate it. And we know we get a lot of feedback from listeners. We certainly get a lot of emails, and texts and things of that nature. So we know y’all are listening, but we need to see it in the ratings, reviews and subscriptions as well. It helps other people find the information, too. As folks know, this is a passion project for us, and we want it to actually be influencing the conversation more broadly. So, look, we know we got a lot of folks tuning in from our great partners with ��, Substack, Apple, Spotify, YouTube, you name it, we know you’re out there.

But subscribe, rate us, leave us a comment, and we’ll just be appreciative for that.

Diane Tavenner: We will. And we will continue to be grateful for the direct feedback as well, because we use that. We take that in. And in fact, this conversation today is something folks have asked for. So we will not waste any more time.

Michael Horn: Yeah, no, it’s true. Well, I’m thrilled because we get to welcome Adeel Khan to the show, known obviously for MagicSchool. But before that — we’ll talk about MagicSchool in a moment — but Adeel worked as a teacher, assistant principal, founding principal for DSST, the Conservatory Green High School in Denver in 2017, and then left a few years — I think it was like three or four years in or something like that — to coach school heads. And my sense of — Adeel can correct me if I’m wrong on this — but my sense is, this wasn’t the calling.

A year later, I think it was, ChatGPT burst on the scene into people’s consciousness in November of 2022. And Adeel says, there’s something here. This can revolutionize the teaching experience and you know, quickly sees like, hey, teachers aren’t going to figure out how to use this productively. Let’s help them do that. Builds MagicSchool and it takes off like a rock rocket. No exaggeration. I think over 7 million teachers now use it. That number’s probably out of date.

As I say. It partnered with more than 10,000 schools and districts, I believe over 160 countries. So we’ve got a lot to dig in here. The use cases are broad. But Adeel first, welcome. It is great to see you again.

Adeel Khan: Thanks Michael. It’s great to see you too. And Diane, pumped to be on the podcast today, and thanks for the kind introduction. Mostly accurate.

Michael Horn: What did I miss? Yeah, what did I miss?

Adeel Khan: You got, you got it mostly there. I think certainly when I got to coach principals, I didn’t, it wasn’t my favorite thing in the world. Not because it wasn’t a great opportunity. I worked on a great team, and principals need support. But rather, I think, when I was founding my school and building it, I thought, you know, if you’d have asked me if I had the most important job in the world, I would have answered yes. And I just missed that feeling. I didn’t have that kind of builder-chasing, really ambitious feeling when I was in that new role. And it’s sometimes good to know that about yourself, right.

I think that like sometimes trying something and knowing that you know where you are because, you know, in some sense at the end of my principal career and seeing the graduates in the first class of seniors, as I kind of built the full school out, I was exhausted. Right. And sometimes you think about, you know, what’s the other side like, you know, and you know, being an advisor sounds less intense than being in a school building every day and you know, chase this big dream and, and then, you know, knowing what the other side was like, I was like, oh no, some people are wired for this. Right? Being in the builder seat and being… I learned that about myself. And even when things get hard today, and they do, we have a lot of challenges. I know that this is what I want to be doing and, and I would be, you know, I wouldn’t be as… there’s no grasses greener thoughts anymore. Even when those seep in, I’m like, nope. I know this is the thing.

Even when it is, I’m in my most trustful habit.

Michael Horn: So I’m really, I love it because you’re willing to make the trade offs for the thing you really want. And obviously we hope all our young people start to grow that same muscle, right, in their lives.

Diane Tavenner: That’s the work I’m doing right now. So it’s an inspiring story.

Michael Horn: Well, I was going to say. Right, so like we have two, you know, Diane, Adeel, you know, founding school heads become founding ed tech company CEOs. I think the story of the founding story of MagicSchool is somewhat well known, but maybe what’s less known, Adeel, is how you think about the problem that you’re actually solving both originally and today because you all have expanded your scope quite a bit. But let’s start at the outset itself because I think your framing tells us a lot about why you grew so quickly. So, you know, how do you describe what MagicSchool first solved for teachers and why that approach was so important at the time it came out?

Challenges using general AI tools in education

Adeel Khan: Yeah, so I think that there’s a part where there’s maybe a little revision on the way you describe the problem that I found, which was not that I didn’t think teachers would be capable of figuring out how to use this and use it productively, it was that the tool itself was a general purpose tool. It wasn’t built specifically for the domain and therefore it was clunky to use. And you know, at that time even really good prompting didn’t yield a really good result for teachers. It feels like, you know, ancient history that you had to have massive prompts for like an LLM to understand what you really meant in the prompt. But at that time it really was, so if you were prompting ChatGPT, it was like you could get something quality, but your prompt had to be a page long for it to really understand your context, really understand the format of your output, just all those different things. And if teachers were going to use it right away, we had to do some of that work to get them to a magic moment with the technology. A lot of teachers would report to me when I was training them on ChatGPT, which is kind of the way the product idea started was, you know, I was prompting it so much, it wasn’t saving me time, it was costing me time.

So there was like kind of a really acute prompt of, you know, promise that this has an opportunity to assist you, save you time and also make you more efficient, augment your abilities. But it’s not working for you right now or the vast majority of teachers in that moment. But I think of it as, you know, when I started thinking about building the product, it started with I actually thought I might be training teachers on ChatGPT, and that just didn’t work. I like, experimented with it. I went to my school and teachers didn’t really adhere to the product. So I was like, there’s got to be a better answer to this. I was savvy enough to kind of be looking around to see, like, you know, products that were coming out that were generative AI products. And they were like this first set of products that were hitting the market.

There was like Harvey, which was like AI for lawyers. I remember that really vividly. And I don’t think they even had a live product, but it was like announced as a product, and they’d gotten some funding, and there were news stories about it. And I was like, oh yeah, there’s going to be an AI that is made for each kind of profession. And that makes sense because each profession has its own quirks. It has its own domain knowledge, it has its own workflows, it has all these things that are specific to that industry. And a generalized alternative language model isn’t going to be the answer for every single one. So that was kind of the clear vision from the start, is we make a really domain-specific, intimate product for teachers.

And the products evolved a ton since then, but that core of it was like, it’s going to be really domain specific. Even in those early days, I thought to myself, we’re going to make some tools in this platform that if you’ve never worked in a K12 school, you would have no idea what that even means. Like, that was kind of my bar for myself is like, I want it to be so intimate, so clearly made for teachers that they scroll through that initial dashboard of tools and they see a couple things and they say, you know what? Somebody who’s worked in schools like, built this. I still hear that today. And it’s one of the things that brings me the most pride. Teachers don’t necessarily get built products that are so clearly intimate for them and know their work. And that’s what we hope to do and continue to do with the platform.

Exploring MagicSchool’s AI features

Diane Tavenner: This is such a good place to jump in because one of the things Michael and I are doing this season is trying to help people see how AI is truly being used in schools like today. And so building off that origin story and where you started and where it is today, I mean, let’s get into like how MagicSchool actually works and what, where the AI is in it. And, you know, this is the moment we get to kind of nerd out, which is fun. It sounds like you’re, you’re in that space too. Like, just to give people some context: When you enter as a teacher, it seems like there’s basically three big categories of support, teacher tools, writing feedback, and Raina the chat bot. And so, and then when I look at the list of teacher tools, you’re right, I open that up, I’m like, oh my gosh, there’s like 75ish tools. And we’re getting everything from like a worksheet generator to report card comments, unit plan generator, standards unpacker, IEP generator, quote of the day, classroom management plans and even tongue twister, you know, so like help us unpack.

Like literally what’s going on here in this tools section? How’s AI? What is the role of AI and what, how are teachers using this?

Adeel Khan: So one thing that I think MagicSchool does exceptionally well as a product is, while there’s a breadth of tools and sometimes we get the feedback that it’s overwhelming, it’s like this tension we feel between it being overwhelming and also really familiar for teachers. Like, I’ve gotten that feedback since the very start and I always hesitate to like change the UI or make some meaningful shifts there because it has kind of become our calling card that we’re this like list of teacher terms almost. Right. The UI itself has been replicated by 20 other products at this point because of work. Right. It’s like, you know when you say AI to maybe a skeptical teacher or a teacher who’s like, technology’s not actually been a meaningful part of their story in the classroom, or a teacher who’s maybe been sold on technology being a really big opportunity for them, and they’ve been burned. And there’s all kinds of reasons why teachers and technology have not gotten along particularly well.

I will be clear. I was not somebody who like, as a principal, saw incredible value in technology. My school didn’t subscribe to any boxed curriculum-type technology tools or platforms. We built all of our own, internal. We built all of our own curriculum. We didn’t subscribe to a single ed tech product outside of the LMS that we used. And that was a kind of a requirement. And the reason was mostly because I just didn’t know that they were going to drive outcomes for our kids. And it was an outcomes-oriented principle.

I wanted to make sure our kids’ literacy and MAD scores and their SAT scores were like, that was, kind of our focus was preparing them for college. And I wasn’t sure that any of the tools out there would make a difference. And I might have been just wrong. Like, you know, maybe they just weren’t in my world. And there’s some great tools out there, I’ve learned since joining the ed tech world. But nonetheless, I think there is that skepticism amongst folks, and there are tools that have come to schools and have done just about nothing. I mean, everyone who’s worked in a school district or building will tell you, like, remember two years ago, that initiative we had? What happened?

That is just like a common story, like a new curriculum that’s going to revolutionize the way that you teach and can completely, all those kids that are behind your class, they’re going to catch up in a year. Or here’s a new tech tool that is promising to do this. So there’s all this baggage around, like products that come into schools. And the baggage often is, I invested so much time and energy in implementing this thing, and I don’t know how much value I actually got out of it, and then the district gave it up in two years. Like, you know, like, there’s like frustration here. So if you think about MagicSchool coming in, like really novice, you know, founder — me, I was just like, let’s get people to value really quickly, right? Like, let’s just get them into what this thing can do so it’s believable.

Using generative AI in education

Adeel Khan: So you jump into MagicSchool, you see the rubric generator, you click on it, it asks you some pretty simple like form fill up type questions like, you know, what we’re going to do attach the document maybe of the assignment you’re going to be assessing with this rubric. All very simple, super easy to use. And then you click the generate button, and you get a rubric that looks like a rubric. It’s what you expect it to be. It works the way you want it to work. It meets your expectations. Whereas like at the, at the, you know, in early days, ChatGPT and even still today, like it’s even the simple friction of if you went into a regular chatbot and you tried to build a rubric, of course you could, but like, you’d have to type in like four sentences.

I want a rubric. It’s gotta be six columns. It’s gotta be, you know, like making sure a boxed format. And then like, you know, you do it and then it, then the LLM will respond something like, oh, did you, you forgot this detail? Can you give me this detail? Wait, wait, what? Like I just gave you, like, it’s a frustrating experience for somebody who was told this is gonna save them time. And I have to know funky things about prompting nonetheless, like, that’s delight. A rubric generator that gives you a rubric is like a zero time-to-value, incredible experience for the actual. I see how this is going to be really helpful to me in my daily work. And the flywheel it creates is also a secondary value because if generative AI can do this for me, then what else can it do for me? And we have a chatbot on the platform Raina that is just like a ChatGPT but built for education with a couple, you know, bells and whistles and education-focused context. And if you use that, like, if you know that, you know, MagicSchool could create a rubric for you.

No, we’re not hiding the ball here. Like, you know, what’s happening is there’s a prompt, there’s inputs, it’s coming out with something. You know, you can probably prompt Reina and do something completely original or new, but you need to be able to see the value first for you to be really sucked into it. And like, I think an amazing job of getting people in the door with something really low stakes that should like, turns out to be really high quality, saves you a bunch of time and makes you want to use the platform more. It makes you want to think about, okay, what are the other tools? If it did this thing for me, what else can it do for me? And I think that’s the way that we see the flywheel start. We think about the user.

They start with these really simple tools. They might graduate into their own free form prompts and Raina and trying things. And then they might decide to use an agentic workflow like our class writing feedback tool, connect their Google Drive and have writing feedback dropped on each of their documents. Then they might want to think about, well, if I’m using this in my work and it’s really valuable, it’s helping be a thought partner to me. I wonder if it would be helpful for my students typically. So then we —

Diane Tavenner: Yeah, super helpful to understand your thinking and your flow, and it seems like that is working. Let’s go back and nerd out a little bit on the rubric generator. So where…? What happens when they type that simple thing in? Like, how are you using AI literally? What’s going on behind the scenes there? And as a person who’s written a lot of rubrics in my life, used a lot of rubrics in my life, like, how do I know that’s a good rubric and, like, what’s going on behind the scenes and under the hood?

Adeel Khan: Yeah. So great question. When MagicSchool started, it was just a prompt. It was like me with a prompt in the background. I had created rubrics as an English teacher in my life, and the best instructions I could give, and the judge was me. Like I was whether or not the quality was high enough or, you know, would meet my standards. And then it was a group of users who were doing the same things upon using it.

Diane Tavenner: And you’re prompting one of the big LLMs, essentially.

Improving model quality and selection

Adeel Khan: That’s two and a half years ago. So just know that’s like, it’s radically, radically different. So today we have a trust, safety and quality team, running our own evaluations on the platform. We have pedagogical experts on the team who are reviewing poor quality. So there’s a human part of it. Then there’s a thing called LLMs as a judge. Some of our evaluations are using models like Claude to judge the quality of the rubric. I was actually on a panel yesterday with one of the, a teammate, one of the teammates at Anthropic, his name is Nirob, and he was, he was naming that, you know, Claude itself at this point is probably the level of like a PhD in domain.

So, you know, you can have Claude, a PhD in a domain, almost, nearly, and getting better every day, judge the quality of the rubric too, and then iterate on the prompt model we’re using and change those things out. So we actually have, we have a wildly more complex version of judging for quality internally now. And it’s a combination of like LLM as a judge, human in the loop, user feedback on the platform. And then when new models come out, we are, on a regular basis, once every week or two, we’re saying, is this new model better performing against our criteria? And so each one of those essentially roll up to a score, and we have a score that determines whether or not this model can exist in the platform. And sometimes, you know, if it’s a 94 from one model and a 95 from one model, but one model is like one tenth the cost, we will choose the one that’s a little bit cheaper because we have to keep our platform affordable for schools. The vast majority of them, we’re able to say the absolute best output is the one that we go with, but they’re judged across a lot of different quality markers now to ensure that the output is something that we can stand behind.

Diane Tavenner: So just so I make sure I’m understanding. When I go in there and I pick a teacher tool, like, I think it’s report card feedback or something like that, you all have kind of established what good report card — the elements of good report card feedback. And then you’ve created prompts that are prompting the LLMs to do that. And then you’ve gone through and tested those responses to make sure that their quality. Because literally the humans aren’t testing while I’m the teacher here asking for that. You know, it comes back like that. Right? So.

So you just have confidence in those responses I’m getting?

Adeel Khan: Yeah. So, yeah, we’re essentially running out, like, hundreds of queries against them and then judging queries and then orienting around what the best kind of set of all of those variables. What’s the best prompt? What’s the best model? What’s the best output?

Diane Tavenner: Okay, got it. Now help me understand, like, that rubric. So now, like, I’m a teacher in my classroom, it’s often been standard that the teacher, sort of in their classroom, doing their thing, using their own rubric. How do you think about it from, like, your principal seat, like, the whole school? So, I mean, you know, for example, when I was leading schools, we built a longitudinal rubric that, you know, over time, the kids were — and so one of the things, as I play with MagicSchool, I’m like, oh, what’s the bigger picture here? You know, what’s the — what’s the high school arc? Or what’s the whole arc? And is this kind of more of an activity in my classroom base? Or is it the full big picture, the backward planned approach? How do you think about those things?

Adeel Khan: Yeah, it’s a really great question right now. I think that MagicSchool’s teacher side is best judged as kind of an assistant that helps you in the moment that you need it in your daily classroom activities. You can use Reina as a thought partner when you’re struggling with something or a Chatbot, you could, you know, really build out, like, a full, like, unit, then the subsets of lessons and like a generative thread, all the tools are threaded. So, for example, if you started with the unit plane generator, you could then say, build me the first lesson in this unit. It keeps context of that entire thread, and you could give it, like, input about, hey, my students performed this way on it. Can you generate the next lesson with that context? And you could do it that way.

But transparently, few people do. Right? Like, it’s really like, they come in, they kind of get what they need, and they keep going, we want to move toward more of what you’re describing, which is like, hey, the entire cycle is brought through in the platform. You give the platform your intent, and you keep these really rich threads with memory, context and knowledge of your classroom. So, we’re moving toward where you can kind of have the entire context of the classroom experience built in the platform. One of the key components of that is assessment.

Building personalized learning assessments

Adeel Khan: Like if the platform understands how your students did on their assessment, say for example today you could build an assignment in MagicSchool and you could, you could actually, you could build an assessment in MagicSchool. It could be taken by your students in MagicSchool. The results of that assessment could produce some really interesting insights about how they performed against the standard. And you could then create a material based on that assessment. So that’s where we think it’s going, is that hey, you’re going to build things based on the assessment results that take into context your students areas of strength and areas of growth based on the insights that are in aggregate across those folks who took the assessment. And you can imagine that there’s even more you can do, right? Like there’s on the student side, on an individual level, if you had assessment data about a child and you also had them taking assessments in the platform, you had them doing activities in the platform, you could trace a student’s personalized learning profile that understood kind of their academic needs. It could be continually updated with memory around the way that they interact with a platform. Not just from their academic strengths and how they’re growing, but also their stylistic preferences, their post secondary desires.

And you could really make these incredible persistent learning experiences for students that they always can tap into that’s associated with a really rich profile to serve them right in their zoning proximal development and in a style and in a way that they will engage and learn more.

Michael Horn: I’m curious off that just because that, that like becomes a very big vision, right, that shapes around the student how starting from like a teacher workflow, right to like the student life cycle almost, if you will, how much do you need to know and collect about individual students and how much do you need to know about the specific? Like take the assessment question, right, like, so I’m going to give feedback on you’re grappling with a particular poem or a book or something like that. How much does the model then need to get trained not just on the teacher rubric, but actually on the content itself as opposed to just the standard, which is probably a higher-level statement of ability to do X. But ability to do X in one context might be very different from another. So like, just help us think through the scope of that, of getting to that vision you just painted.

Adeel Khan: Yeah, it’s a really good question. I mean, we are at the early stages of understanding how this will work without student data. But as we build so one of the things that we are doing or in the planning process of getting some of these ideas off the ground, there is a really interesting process where you can kind of use LLM agents as sample students and then you can also assess the quality of their interactions with the platform based on specific profiles that you create for the AI agents. And then you can kind of get a really good data set on what you’re describing, Michael, is like, is it working? Is it actually meeting your needs? Or is that student who’s an agent who has the stylistic preference around visuals for their needs. That’s the way that they learn best. Or audio is the way they learn best. Their reading level or their reading test score diagnostic was this.

And this is what that means. You can kind of create all these profiles individually and then you can run them against the interactions they have with the platform and then you can judge how high quality the interaction is on the platform with like an external observer. So, like, almost similar to where I described that LLM as a judge, the judge can actually judge the experience.

So you can think of it as almost like a principal judge. Right? There’s a principal who’s an agent who’s watching the AI interacting with the student and determining is that a high quality interaction or not based on the child’s profile, based on what I know about what high quality instruction looks like. So you can start building these kinds of recursive loops and running them hundreds of times and get to some pretty, pretty impressive verifiable outcomes pretty quickly. And of course, you know, AI is not kids. And they’re not going to be perfect.

Michael Horn: AI is not going to act out for —

Adeel Khan: Yeah, yeah, yeah. So I don’t want to, you know, kind of anthropomorphize that this actually is the answer, that we can simulate everything through generative AI. But it’s a great way to like in a, in a basic sense feel like, okay, it works in a simulation. And then when we bring it to students and we work through the pilots and we see schools and districts who embrace this, then we can see it in action. We can combine those insights with the insights we got from our simulated experiences and make something that we think is really powerful. Some of those generative experiences, we’re going to be starting this summer in summer school with some partner districts to see how it works in practice and is it actually the needs of the kids. And we’re going to use that data as data to inform the product as it gets into more live cadence.

Diane Tavenner: You just said so many interesting things in there. Like, I am of like five minds right now. Where what thread do I want to pull? But let me pull on the one, because you started by saying, like, we’re playing with what we can do without student data in there. And best I can tell, based on my experience in MagicSchool you’re not connected to an SIS or anything like that. So you’re not pulling in any data as the teacher around your students. That said, I did create, I did use the IEP generator and I generated an IEP and I, you know, I, look, I didn’t do it on a real student, obviously. I’ve got like 25 years of experience. So I sort of built a proposed persona in my head and like input the information.

Discussing AI-generated IEPs and privacy

Diane Tavenner: And I will say I was like, kind of blown away that a fully formed, detailed, and dare I say, very confident IEP like, came out. You know, and at first I was like, oh my gosh. And it looked like an IEP that you would kind of read in a school sort of thing as I was skimming it. Then I started like really digging it, and I was like, oh, as someone with 25 years of experience, like, this is not the IEP I would have written necessarily because I, in my mind, you know, the kids I’m thinking about, like, I have intimate details and I’m like, wait, that feels a little, it felt a little AI-ish, right? Like sometimes AI gives you really, like, seems like compelling results, but they’re not very specific or personalized or whatnot. And so that was one thing I was curious about. Like, how do you think about some of the tools that are like that and, and how they get used? And like, I was thinking I’m a first-year teacher, and I use that, and I don’t have 25 years of experience. Like, can I? How do I do that? And then the second piece is you’re out with teachers, like, do they just pour a lot of information about kids into the platform? And I’m sure you get a lot of questions about privacy and security and, like, what’s going on there?

So I’m curious about those two angles.

Adeel Khan: Yeah. So I mean, we do a lot of professional development. I’ll start with the second one. We do a lot of professional development around making sure that teachers do not share information that could be sensitive into the platform. So there’s upfront training in that tool you used, actually, you’d see that like it actually actively reminds you in the tool itself to not, because that’s a tool that’s more susceptible for you to maybe submit accidentally that data. Of course, if any data is brought to our attention that was submitted to the platform, those PII, we remove it immediately.

And we have incredible data handling practices, and safe things are all available on our website. So we’ve done a lot of things to make sure that schools and districts can trust us. And so that’s certainly something that we consider. And we just think training and enabling teachers is the best way to prevent that stuff. Because even if MagicSchool has really great data handling practices and is kept safe, like they might bring it to another platform and you know, we want them to know how to use our AI, but also any generative AI and keeping student data safe is incredibly important when you’re using these tools.

Diane Tavenner: Yeah.

Adeel Khan: Second part, Diane, I think is really interesting. So I was a special education teacher too. So for me, I can think back to when I was a novice teacher, and MagicSchool would have been a godsend for me. The IEP would have prevented me from being anxious. I probably would have helped me save a lot of late nights, and it would have made me feel like I had real strategies to support kids because, you know, it’s a good point. So, so your question, I think I might challenge this. Like you presented it almost as like a fear that they’re not 25 years, so maybe they’re just taking this robotic IEP that’s not as good as the high-quality, 25-year IEP. I want you to know that like I’ve been a principal reviewing IEPs and I have seen teacher wonderful, hard-working teachers Submit IDPs with the wrong names in them because they were just copying goals and pasting goals from student to another because they were just trying to get it in by the deadline.

Right? Wonderful, hard-working, incredible teachers. So do I believe the world has gotten better because of MagicSchool’s IEP generator? Yes, it has. The floor has been raised because there are actual, because now the barrier is you understanding the student and getting some, some high-quality things and you know, even a novice teacher will see the goals and at least they have, they know how to write goals now.

I didn’t know how to write right, like, and I didn’t necessarily have someone to go to to help me write those goals. So I would say net MagicSchools raised the board dramatically for the way an IEP is written. I think a family would be much happier to see a MagicSchool-written IEP than the ones that I was editing, if I’m being totally honest. And it’s again, not because teachers aren’t wildly hardworking and talented. It’s because there is no time. And so I think that’s kind of the reality now. In an ideal circumstance, you know, they have a really great draft and they have an instructional coach like you, who they can go to and say, what do you think about these things? And like, you know, they can question and challenge them and push them and make them even higher quality.

But I do think that, like, sometimes we miss the reality of what happens in schools when we were critical of the tools that teachers are using to better their practice. And sometimes we just need to trust teachers. Like, in that case, I’m like, actually, I even trust the first- and second-year teacher to use this appropriately. And especially if they’re educated and they’re told, like, the way to use it, not to submit appropriately private information, things like that. But I always challenge people is like, yes, you know, people, we have schools and districts that hide the IEP generator because they’re so scared of it. And you know, we respect anyone’s decision on what they want to do and they’re allowed to do that and like, you know, our enterprise product and then we support them in doing that. But I, on a personal level, I always challenge them. I say, like, look, like, ask your assistant principals who are reviewing IEPs.

Yeah, they see, are they, are they higher quality because you took this tool away or are they higher quality when MagicSchool is in the loop? I think that’s like the question to ask is what’s the before and the after rather than like, what’s the ideal? Right. You want the ideal. But I think that there is like a, this go between. I think it is super strong. And I think that the tools are getting even better over time. And I think that the better the context that you share with it, the better it’ll do of course.

Diane Tavenner: Yeah, that makes sense. And I think I understand the perspective that you’re coming from and certainly your lived experience. And I do think sometimes people, you know, have an idea of what is happening in school versus a reality of what is happening in school. And so I appreciate that. I was also really tuned into you saying that as a school leader, you all developed your own curriculum. That was my reality too. You know, we ended up building curriculum, we ended up building Summit Learning, which was like a whole massive curriculum. And so I’m wondering how.

But a lot of schools have adopted curriculum, as you know, how do you recommend that teachers sort of deal with the world of like, I have an HQIM that I’m given by my school and I want to use MagicSchool. How do those two things play together? Or do they, or what does that look like?

Adeel Khan: Yeah, I think that, you know, again, lived reality of an HQIM in a district. I’ve lived in that reality as well. We didn’t box any curriculum, but sometimes we would get for a certain subject we would have like, you know, free prep lesson plans or whatever it might be. And I think the lived reality of those things are teachers are always modifying, changing, supplementing, making those things work for their classes. And that’s good. So I think that that’s what we’ve encouraged them to do is like, yeah, keep the spirit of what your school wants you to do. And obviously there’s a research base behind the curriculum that you’re using, hopefully, and we want to be a great supplement to that. We want to make sure that you’re able to build the supplementary materials that you need to make sure your class functions and works.

Integrating curriculum with MagicSchool

Adeel Khan: And MagicSchool could be used alongside those things. There’s a world where we build knowledge into our platform so the schools and districts can upload things like standards, curriculum they’ve built internally that are not like, you know, copyright by the publisher. And there’s a world where we partner with publishers too, and we bring those knowledge bases into our platform and call them as well. And as you’d imagine, those publishers aren’t super excited to work with large language models because this is like their proprietary IP. And nonetheless, I think that like, you know, we think that there’s a future where there’s kind of a win-win for both of us in a world where teachers are finding that they’re starting their day with MagicSchool, and it’d be really convenient if they can pull in some of that information and make those curricula more flexible with generative language models. But yeah, I support that. I’m also, I think a lot about curriculum and I make spicy posts on curriculum on my LinkedIn, if you haven’t seen them. There are a couple curriculums that are incredible and obviously they’ve driven meaningful outcomes for kids.

And I don’t think they’re all that way. To be clear. I don’t think they’re all super research based. I think that a lot of them are not particularly valuable and I think, I don’t think. I know a lot of teachers hate being put in a box. They hate having to be told that you can’t be autonomous in your classroom. You must follow the script because the script is better than anything you would create.

Which is like the, not the intended message, but the unintended message that a teacher might receive when receiving certain curriculum. And of course they’re the teacher’s love. Right? Like they tell you, no, this thing’s amazing. It’s changed my classroom. So not painting with a broad brush. There’s also like really amazing ones. I will say that my lived experience has shared that like if you trust teachers to go find the right resources and give them the tools to do that and know their kids and coach them really well, you can get really extraordinary outcomes. Mind you, most of my experience is secondary, but we have incredible results for our kids.

We served a highness population and had dramatic growth. And so that’s my own experience. So I don’t know. I think that there’s a little bit of like, I would not call myself as a personal. Like on a personal level. I do not ascribe to the Church of Box curriculum. That is not my ministry as we used to call it in Atlanta.

So do I think there’s really good stuff out there? Absolutely. It’s not my ministry. I just teach it.

Diane Tavenner: That makes sense. Clearly you are outcomes driven. I know that based on the school that you started and all the language you’re using here. How do you think about — how do you now at MagicSchool think about what outcomes your — how do you hold — what’s your bar? What are your goals? Like what are your outcomes that you want MagicSchool to drive to and how do you measure them?

Adeel Khan: So there are a couple ways that we’re thinking about this. So outcomes are at the heart of our mission. We have named goals in our company about how we are going to drive student outcomes in classrooms. Right now, the way that looks in our platform is the amount of what we would call feedback delivered to students. So generally what we’ve seen in the first two years of the product is that like the things that we, that teachers have said have driven growth, the experiments we’ve set up or have not set up we’ve just been reported are that when students get things like feedback on their writing that they’ve done on their own aligned with rubric in the platform, that is powerful. That is something that drives an alchemy classroom.

Measuring feedback and student outcomes

Adeel Khan: We have actual classrooms that have shown state exam scores change over those things. We have enough data to say that like when MagicSchool gives student feedback that teachers is controlling and aligning to a rubric or the way that they’ll assess students, that’s a great thing. So we measure right now how many instances of feedback is MagicSchool giving to a child under the supervision of their teacher through either our assessment platform in the product which gives students just in time feedback after they take an assessment, or in their feedback portion of the platform where they tune in. So those to us are pretty hard. Like we don’t kind of look at it as like hey, you talk to a chatbot, so you learned. Like, that’s not enough for us to kind of count and like our something that we think is definitely going to drive an outcome. It certainly might drive AI literacy and we certainly think about that measure as well. But in terms of outcomes, that’s the way we think about it now.

Well, where we want to go is we want to be able to probably say that students have learned and judge it in the platform itself. So one of the ways we might do that is through having a diagnostic assessment students taking the beginning of the year and then at the end of the year or tracking timing platform as we have more persistent student profiles in the platform and simply asking the district to themselves compare their users data which students spent the most time in platform and how much did they grow and then just give us a report back. There’s ways that we can kind of say it’s not a perfect correlation, but if students are spending more time in the platform and they’re getting better results at the end of the year, they’re growing more than their peers. That’s usually a pretty good indicator to us. There’s an experiment that was run unbeknownst to us in our first year. I actually shared this on a panel yesterday. I was at South by Southwest.

But I think it’s a powerful example and I think what a lot of organizations are doing around generative AI around the world and I actually think this is fun because a school district was ahead of enterprises, they’re ahead of companies in the way that they’re thinking about generative AI and our first year that we had a real enterprise product was 2024 to 2025 or 2023. Our first partner, which you imagine our very first partner at MagicSchool would be pretty innovative. And they certainly were as Aurora Public Schools, one of our very first ones. At the end of their first year using MagicSchool, they got a printout of their MGP scores at the district level. MGP in Colorado is basically like a growth score that is associated with each teacher in the district based on their state exam scores. Their grade has a high stakes exam. So the district quite literally gets a list of all the teachers who had the highest growth in order of like this calculated growth score. So they kind of have like which teachers are having the best results in their classroom.

It’s pretty sophisticated calculation. It tracks like based what was their expected growth based on their prior year’s performance to this year’s performance. It’s a pretty solid number. Like at the end of the day it’s like the kids actually grew and, and it’s based on some pretty hard metrics. What they did was that they liked the way the story is told, is the academic, one of the assistant superintendents called the technology director and said, I have all the teachers who have the highest scores or the highest MGP scores in the district. Can you pull up MagicSchools our user dashboard which shows which teachers are using it the most? And they said like one for one. It was like best growth scores were in the top 20 users, best growth scores, best users.

Using AI to boost engineering productivity

Adeel Khan: So the way industry is doing this now, what we’re doing at MagicSchool is we, we have an enterprise version of Claude that our employees use and where we have a real big focus on like agent decoding for our engineers to move faster and ship with more velocity, build a lot of really amazing things for our users. And one way we’re measuring the efficacy of generative AI right now is we’re looking at our leaderboard, like, which engineers are using the most tokens in our version of that enterprise dashboard? And then we’re asking the managers, would you call that your highest performer? Like, is that engineer who also is using the most tokens in Claude performing better? Are they shipping more? Are they meeting the goals that you’ve set for your team in a better way because they’re using AI more? And if the answer is no, then we need to rethink about is generative AI actually helpful? But if the answer is yes, then we need to spotlight that engineer and we need to have that engineer teach the other engineers how they’re using it and how it’s making them more productive. And Aurora was doing the exact same thing two years ago. So I think it’s a really, really cool way to think about how this is actually amplifying productivity in a meaningful way.

Michael Horn: Adeel, I think that’s a good place to leave the conversation for now. I appreciate how much you’ve geeked out with us, and also I appreciate that you’ve told us where it is now and where your sort of vision for it is. As you know, a lot of entrepreneurs, they sell the future/present as one package as opposed to distinguishing the two. So I appreciate that in this conversation as well. Before we move to our last segment, as listeners know…

And with that we’ll move to our last conversation, which is it’s just a fun segment we’ve had and people track this and so forth, Adeel on LinkedIn and believe it or not, about things that we’ve been watching, listening or viewing and would love to hear something that you’ve been tuning into what’s either on your bedside table or on your playlist.

Adeel Khan: I think I’ll go with a Netflix show. So it took me quite a long time to get to it, but I finally watched just finished the last season of Stranger Things, which I was late to the party yard in the first place but kind of binged it a few years ago. And so I was eagerly anticipating the final season. None of it got spoiled for me and I got to watch the whole thing and it was delightful. So I felt like I wrapped the bow on a really special I feel Stranger Things is so awesome if you guys have seen it, but I feel like it’s such a special cultural phenomenon that everyone kind of experienced together and watched together. So that was mine. Speaking of a new show. So excited to hear from you guys.

Diane Tavenner: Well, I’m not going to give you a show today. I apologize. I’m gonna give you a book. It’s a little bit ironic to have AI books, I think, but this one feels special to me. So it’s called Co-Intelligence Living and Working with AI by Ethan Mollick. And I will say, when it arrived, my kiddo who works on the models said, oh, that’s perfect for people like you. I was like, what do you mean? He’s like, you know, for, for regular people who don’t understand what we’re actually doing, but, like, have some sense of it. And it’s really useful for kind of who are really trying to make sense of AI and world and what that looks like.

And that’s what it feels like to me. And so it’s, you know, I’m not telling you anything new by sharing this book with you, you know, a bestseller. But it is, it’s short and it’s thoughtful and I think useful for anyone who’s really trying to grapple in this space. I would recommend it.

Michael Horn: The one question Diane, I have, I love Ethan’s Substack as well. And when the book came out, I bought it as well and read it. But I’m just curious, like, does it still feel current given, like, the race?

Diane Tavenner: You know… yes?

Michael Horn: Interesting.

Diane Tavenner: Yeah, I think so.

Michael Horn: OK.

Diane Tavenner: I think so.

Michael Horn: Cool.

All right. I’ve got a book as well. So, Adeel, I’m also striking out on the show, watching, I think, at the moment, but I still haven’t done Stranger Things. Diane, I think this was on her list. I can’t remember how many episodes ago

Diane Tavenner: I started early, but I haven’t finished off the season. So you’ve been —

Michael Horn: I was about to say. You just inspired her to finish it. Yeah. The book I’ve been reading is A Heart of a Stranger by Angela Buchdahl. She’s a rabbi at the Central Synagogue in New York City, which I think is the largest synagogue in the U.S. or, or top two, I guess. And it’s terrific, she’s a Korean American rabbi. And so it’s like a very interesting story about where she, the circle she has not belonged in, and then making sort of this, I guess, momentous movement into being a rabbi and sort of what that’s been like and her life story through it.

So it’s been a very good read. As Diane knows, my wife’s Korean American, so, like, and I’m Jewish, so it’s like hitting on multiple levels in our household. TBD if anyone else in the household reads it beside me. But I’m enjoying it quite a bit. And we’ll leave it there. Adeel, huge thanks for coming on, joining us, geeking out with us, and for all of you, keep the questions, comments, notes coming after this episode and in general, and we’ll see you all next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

Philadelphia Middle Schoolers Explore How AI Changes Their Classrooms and Their Lives

Carly Sitrin — Fri, 15 May 2026 14:30:00 +0000

This article was originally published in

The middle schoolers at Philly’s Marian Anderson Neighborhood Academy have a lot of questions about artificial intelligence.

They want to know how the government is using AI and what impact the technology has on the environment. They’re curious about how it’s being used for creativity, and whether it will be with us forever — or if it’s an economic bubble waiting to burst.

The sixth through eighth graders have been researching these topics and grappling with how it makes them feel about themselves, their education, and the world around them. On Friday, they presented their findings to their parents, teachers, and some state and local officials in their school cafeteria. Overall, they said there’s a lot they still don’t know.

Sixth grader Azizah Simmons said she’s weighed the pros and cons and she’s pretty confident that AI’s overall effect on our society is negative. If used correctly, Simmons said language models like ChatGPT could help kids her age improve their writing. More often than not, she said students use it to cheat on homework or cut corners on writing assignments.

But it’s the ubiquity of the technology that worries her most.

“You can’t really escape AI,” Simmons said.

Conversations about AI have permeated every aspect of education since the arrival of models like ChatGPT. Familiar debates about cheating have given way to Marketing pitches from companies promising “transformative” AI tools are now . In Philly, educators are working with students to build their own curriculum to and that can be embedded deep in the internal code.

And students say they feel like they have as much knowledge — or sometimes more — than the adults in their lives.

Sixth graders Thomas Mapp and Tyshaan Anderson’s research project focused on how video game designers use AI for level design, character creation, and visuals. Outside of school, they’ve been using AI to help them code games in Roblox and edit videos.

Anderson said he thinks the technology has helped kids like him experiment with creative fields like game design without needing to know the ins and outs of specific coding languages.

Marian Anderson Principal Nicole Patterson said she’s been inspired by her students’ civic inquiries and has learned a lot from them.

Patterson said she sees her school as a trailblazer in leading challenging conversations about AI. But she cautioned that “this is unfinished work.” She said students will continue their research and keep talking about these issues.

Marian Anderson computer science and technology teacher Trey Smith said the goal of Friday’s event was to help students and parents discuss how AI is now part of society, culture, politics, and everyday life, not just about how AI works.

“We’re all still trying to figure this out together,” Smith said. “For students to be in dialogue, not just with themselves and each other and me, but also with their families and with legislators and with school district officials and professors — I think it’s so important for them to learn together.”

That learning process can be tricky. Simmons said she ends up using AI involuntarily because search engines like Google now frontload AI overviews. That makes it difficult for young users to differentiate between what is a primary source link and what is AI generated.

“You use it without meaning to. It’s everywhere implanted in our lives,” Simmons said.

Chalkbeat is a nonprofit news site covering educational change in public schools. This story was originally published by Chalkbeat. Sign up for their newsletters at .

Opinion: Why the ‘Middle Path’ of AI Literacy May Be the Future of English Class

David Nurenberg — Fri, 08 May 2026 10:30:00 +0000

Like it or not, generative artificial intelligence is here to stay; the majority of students nationwide now use it for assignments at least occasionally. Policing AI use is , monitored in-class assessments prioritize quick thinking over deep thinking — and disadvantage neurodiverse and multilingual learners. And no take-home assignment, however creative or personal, is fully “AI proof.”

Yet just freely letting students use AI to generate ideas, explain difficult concepts and produce/revise writing ” upon which learning depends and .

So I have been attempting the “third option” recommended by both the and the : teaching AI literacy.

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This year my 10^th and 11^th grade English students used AI itself as a text to advance their critical thinking skills. We still read novels and short stories, still engaged in discussions and wrote essays, but AI was now a regular part of our work together.

As we read, we examined how large language models’ recycled novel “analyses” mis-read and oversimplified complex literature, producing distillations that often lacked nuance compared with the creative, discursive yet defensible readings that the students themselves generated. They learned to discern actual analysis from simplistic summaries, and to suspect the allure of AI’s instant “correct answers.”

As we engaged in literary discussions, we sometimes invited chatbots into the conversation; many students described these interactions as “bizarre” and “disjointed,” adequate for reviewing plot but too circular or directionless for genuinely provocative dialogue. ChatGPT’s sycophancy in particular tended to kill the necessary tension for true debate. One student “started purposely saying dumb things just to see how GPT would still find a way to say `great idea.’ It just felt so fake.”

As we wrote, we compared LLM-generated essays with human-generated ones, teasing out how AI’s “sophisticated-sounding” yet “generic” prose differed from the “messier” but ultimately, in the students’ judgment, more engaging language they themselves created. In a world where everyone has access to LLMs, these students were discovering the value of developing genuine voice. I hope at least some emerged thinking ChatGPT was best reserved for inter-office memos and letters to one’s utility company.

As we researched, we studied how AI search summaries — which users are now — don’t actually represent internet searches, but instead reflect word proximity within a static corpus of text, a corpus lacking access to paywalled scholarly research and therefore drawing disproportionately on unregulated chat forums.

Students examined whether LLMs accurately reported their sources and to what extent AI drew from ideologically extreme sites. They saw how wording a query — e.g., “is abortion safe” vs. “is abortion murder” — could lead to politically-slanted results based on what the AI thought they wanted to see, and how sources often said something very different than AI summaries claimed they did.

As we took and organized notes, students compared their manual note-taking process to the output of AI note-taking tools, learning how what we choose to include or exclude in summarizing notes, how we use emphasis and phrasing — did Africa under colonialism “fuel worldwide industrial production” or were African resources and peoples “exploited for the benefit of Western industrial profit” — create and propagate different narratives.

These narratives do not ; “what is ranked at the top” of AI searches “is ultimately influenced by the priorities of LLMs’ shareholders,” so we studied studying the politics of AI magnates like Sam Altman and Peter Thiel, learning how Gemini’s responses to political questions, and studying algorithmic bias (e.g., image generation requests for “doctor” returning mainly white males), all helped my students re-think their understanding that AI tools were neutral and simply utilitarian.

When we studied AI, we simultaneously studied neurological research about how humans, unlike LLMs, don’t just rely on pattern recognition, but also make intuitive leaps, and used Edward De Bono’s activities as practice. Students did something else that AI couldn’t: related classroom content to personal experiences.

One multilingual student recalled attending a business meeting with her father where he faltered, because he “[knew] that someone who has the ability to speak English better [me] sat right next to him… ‘It makes me want to depend on you’ he told me, ‘when I’m totally capable of doing so by myself.’ He did much better after I left.” The student then made the leap to consider how, even if AI help is readily available, perhaps we gain something by refusing to rely on it.

When I abandoned AI bans, I instituted AI audits. Students had to demonstrate their thoughtful, detailed evaluation of each AI tool they used, including knowledge of how it operated, what they felt they gained and lost by using it, how they verified accuracy of information, and how they had not relinquished their own thinking. The students didn’t necessarily conclude “AI is always bad,” but they did see that using it always requires vigilance. Best of all, they didn’t have to take my moralizing word for any of this; they discovered it for themselves.

Yes, I had to teach fewer novels in order to make room for AI literacy, but ultimately my job is not to teach novels; it’s to teach students. Their insights — how Grammarly’s “correcting” language altered integral parts of people’s unique voices, how personal evolution often comes from struggle and discomfort, how our desire for ease can hold us back from achieving our potential, how dangerous it is to invest authority in words just because they emerge from a machine — were equally valuable as any takeaway they gleaned from novels. And this time I knew those takeaways were theirs, not ChatGPT’s.

I teach an affluent population, but are with more economically and linguistically diverse learners. To be sure, my experience was often fraught. Some of my less-confident students never stopped considering LLMs’ “clear” and “well organized” writing superior to their own, and still hesitated to trust their own readings of literature over “the answers” ChatGPT offered.

I struggle with asking students to critically evaluate AI while their own linguistic and analytic skills are still developing, but I also know I cannot create the conditions that allow teenagers to become master writers and thinkers before they are exposed to AI; they will soon arrive at my classroom having been using it since childhood.

Post-pandemic suggests that, when teaching anything, we cannot wait for students operating well-below grade level to “catch up” before introducing higher order thinking skills; we have to figure out how to teach both simultaneously.

That requires creativity, and creativity is what makes humans superior to AI, which can only regurgitate already-created ideas. Teachers excel at creativity; every day we come up with new ways to meet the ever-changing needs of our students, and right now AI literacy is one of those needs.

that this training is crucial for keeping AI users — a population swiftly becoming synonymous with “humans beings” — from engaging in “cognitive surrender, marked by passive trust and uncritical evaluation of external information,” as opposed to “cognitive offloading, which involves strategic delegation of cognition during deliberation” when using AI.

about AI rendering English classes obsolete forget that the humanities are about studying what is human about us — including both our criticality and our adaptability.

Note: This is an abridged, non-scholarly version of a peer-reviewed article slated for publication in Issue 115.6 of NCTE’s .

11 AI Prompts Every Teacher Should Know

Jeremy Caplan — Thu, 07 May 2026 10:30:00 +0000

The average K-12 teacher works . About a quarter of that time is uncompensated. Most teachers I know didn’t choose this field to spend evenings generating quiz questions, rewriting instructions or creating elaborate rubric spreadsheets to fit a state-mandated standard.

AI assistants like Claude, ChatGPT and Gemini won’t change these realities. But when you’re overwhelmed, they can help you streamline some of the most tedious aspects of your work. They can free up your energy for what only a human teacher can do. And AI assistants can actually push us to be more creative. They can help us overcome teaching ruts, nudging us to revitalize aspects of our teaching that are growing stale.

AI assistants have arrived at a time when teachers need support to do their best work. In a national by the RAND Corporation, just 24% of teachers reported being satisfied with their total weekly hours worked, and 66% said their base salary was inadequate.

AI tools won’t make up for unfair compensation. But they can help us save time and create a better work/life balance. They can also help us do better work.

A 60-second guide to prompting

The first step to making the most of AI is understanding how to use prompts. A prompt is a natural language instruction to an AI assistant. It doesn’t have to include technical or formal language. You don’t even have to use full sentences.

Prompts are tool agnostic, so you can use them with whichever AI assistant you have access to. I recommend the free or paid versions of Claude, Gemini and ChatGPT, but you can also use free AI tools that run privately on your own laptop, like or .

As the teacher, you guide an AI assistant like Claude or Gemini with relevant context. For prompts to work well they have to be detailed, including specifics and context. Generic prompts yield generic responses.

It helps to iterate on AI output with follow-up prompts. Ask for more specificity or detail. Adapt the response for your students, don’t use it as is. Part of retaining your agency in the process is making sure you build on whatever outputs an assistant generates. You’re the director. It’s much like you were adopting an . The advantage, though, is that this material will be more tailored to your students and teaching approach.

Knowing how to use prompts effectively can mean the difference between AI that’s actually helpful and AI that’s gimmicky. The prompts below are designed to provide you with a creative boost. They each illustrate a practical way to use AI in support of thoughtful, pedagogically-sound teaching. You don’t need any special technical skills or subscriptions. You can copy, paste, and customize them to suit your subject matter.

Setting up a project

If you want to use prompts more efficiently, create a Claude or ChatGPT Project, or a . That’s a folder where you provide a summary of context about your students that the AI assistant can reference whenever you ask for support. You can also upload past materials, syllabi, lesson plans, curriculum guidelines, Common Core State Standards, or whatever else would be helpful context for the AI assistant.

You can also provide detailed instructions in the project for how you’d like the AI to assist you. You’re training the AI assistant and teaching it your preferences. Once you set up a project, you won’t have to repeatedly type in the same context. I set up projects for each of the classes and workshops I teach.

The Prompt Collection

The Bell Ringer

Start class with a spark

The first five minutes of class set the tone for everything that follows. A short, well-designed opening activity can draw students in. Engaging openers are especially valuable on Mondays, after vacations or when you’re pivoting to a new topic. The challenge is coming up with fresh ones regularly.

Goal: Generate a bunch of quick activities you can use at the start of a class session, adapted for your subject and your students.

Prompts: “I teach [subject x] to students in [grade level x]. We’re studying [specific topic xyz. Be as detailed as possible about your subject and context. Include a sentence or series of phrases of context about your particular class and teaching style, or any special needs or context for your students. No need to make it formal].”

“Generate five bell-ringer activities I can adapt to open a [xx] minute class. Each should take no more than [x] minutes, require no materials, and either activate prior knowledge or help students reflect on what they’ve just learned. Include one that’s discussion-based, one that’s written and one that’s a game or visual/creative task. [Or adapt these examples to reflect your subject matter. For example, one of the options could be a logic puzzle or an artistic challenge].”

Prompt Example

I teach U.S. History to 10th graders at a public school in San Diego. We’re starting a unit on the civil rights movement. We’re focusing on the tactics used in nonviolent protest: sit-ins, freedom rides, and marches. My students respond well to visuals and storytelling, but some of them are slow to settle into our morning class sessions.

Generate five bell-ringer activities I can adapt to start class in an engaging way. Each should take no more than five minutes, require no handouts, and either activate prior knowledge or get students thinking about why ordinary people take extraordinary risks. Include one that’s discussion-based, one that’s written, and one that involves an image or short video clip I can pull up on the projector.

The Real-World Hook

Answer “Why does this matter?” before students even ask

When you’re juggling administrative meetings, multiple preps and paperwork, it can be hard to give extra attention to helping students relate to a given learning unit. This prompt helps you brainstorm connections to contemporary music, art, film, TV, cultural trends or other subjects of interest to students.

Goal: To generate five ways to show your students how the topic you’re teaching is relevant to their lives, each with a two-sentence hook you can use to open discussion.

Prompt: “I’m about to begin a unit on [x topic] with [x grade level] students. [Provide a sentence of additional context and a few additional details about your students’ interests]. Generate five ways to connect this material to something students at [x] grade level may likely be able to relate to. This can include sports, the arts, social media trends, pop culture, music or other contemporary issues. For each connection, suggest a two-sentence hook I can adapt to help jumpstart a class discussion.”

Prompt Example

I’m about to start a unit on percentages and ratios with 7th graders. I teach in a suburban middle school in Ohio. Many of my students follow football and basketball. Many also spend a lot of time on social media. A few are really into cooking and video games.

Generate five ways to connect percentages and ratios to things 7th graders actually care about. This can include sports stats, social media follower counts, video game scoring, food recipes, or other relatable subjects. For each connection, suggest a one-sentence hook I could use to kick off a class discussion.

The Bad Example Generator

Turn common mistakes into teachable moments

Showing students examples of common mistakes can help them avoid those pitfalls. But we can’t embarrass students by showing examples of their weakest work. Fortunately, AI assistants are excellent example generators. They can come up with nearly any kind of error you specify, saving you hours you might otherwise have spent creating intentionally bad work.

You can adapt this prompt to include any kind of error you want your students to avoid. These can include experimental design mishaps in science or mangled math formulas. If you’re teaching essay writing, showcase logical fallacies or ad hominem arguments.

Here’s an example of a I generated with the help of an AI assistant.

Goal: Produce five realistic examples of a specific error type, unlabeled, so students can identify, discuss and learn from the flaws.

Prompt: “I’m teaching [x subject/topic] to [grade level x] students. [Provide an additional sentence of specific context about your class, the learning goals you’re focusing on, and/or the lesson you’re preparing.] Generate five examples of paragraphs with [ad hominem arguments / circular reasoning / weak thesis statements / misleading use of statistics / or pick any other weakness] related to [x topic]. Make sure each example is realistic and plausible. These should be the kinds of errors students at this grade level might actually make. Don’t label what’s wrong. I’ll use these for a class activity where students identify and explain the flaws themselves. [You can also task the AI with annotating or explaining these errors to help you walk students methodically through these common flaws.]”

Prompt Example

I’m teaching persuasive writing to 11th graders at an urban high school in Chicago. We’re working on how to build a strong thesis and how to use evidence effectively. My students sometimes make claims without backing them up. Or they rely repeatedly on one or two weak sources.

Generate five examples of weak thesis statements on the topic of social media’s effect on teenagers. Make each one realistic. These should sound like something an 11th grader might actually write. Don’t label what’s wrong with each one. I’ll use these in a small group activity. Students will discuss the weaknesses in these statements and work on strengthening them.

The Scaffolding Prompt

Make instructions clear for every student

Complex instructions often trip up students. Simplifying language can help, along with breaking guidance into smaller steps. This prompt helps you clarify instructions for an existing assignment, handout or any other activity. It’s particularly useful if you have students with learning differences or if your class has a wide range of readiness levels.

Goal: Reframe an existing handout or assignment so it’s clearer and more accessible, especially for students who need extra support.

Prompt: “Here is a [handout / assignment / resource] I give students: [paste or upload the handout]. Help me reframe this for students who face [specific challenges or context that impact some of your students]. I particularly want this to be more accessible for students who need extra support. Break the instructions into smaller, numbered steps. Replace any abstract language with concrete, specific directions. Point out any parts I should clarify. Suggest a brief example for each major step and any illustrations or images that might help me make this more visually engaging. Maintain the academic expectations I have for the work. The goal is clarity, not simplification.”

Prompt Example

I’m attaching a lab worksheet I give students. I need this to work better for my 4th grade science class. We’re in rural New Mexico. Several of my students have IEPs, a few are English language learners, and their reading levels vary a lot.

Help me create alternative versions of this worksheet that might be easier to follow for students who need extra support. Break the instructions into short numbered steps. Replace abstract instructional terms with plain, everyday language, but don’t change the vocabulary words, which I need students to learn. Add a concrete example for each major step. Flag any parts that might confuse a 9-year-old. Suggest one or two simple illustrations that could help. Don’t water down the scientific thinking. Don’t alter my expectations. The goal is clarity, not dumbing this down. I’ll edit it afterwards to make sure it fully represents my instructions.

The Review Game Generator

Create engaging questions efficiently for learning games

Coming up with a long list of review questions can take hours and designing multiple plausible wrong answers for every question can be exhausting. An AI assistant can help, quickly turning existing handouts, lesson plans or fact sheets into engaging questions. It can help you customize questions for your subject matter and student level.

Goal: Generative 15 multiple-choice review questions, tiered by difficulty, formatted for whatever learning game you prefer.

Prompt: “I’m finishing a unit on [x topic] with my [grade level x] students. I’m preparing an end of term review session, so I’m trying to come up with some good questions to help students practice [a particular skill or area of knowledge]. Generate 15 trivia questions based on the following key concepts: [list concepts or paste notes or upload a handout]. Suggest a series of multiple-choice questions, each with a correct answer and three plausible wrong answers. Vary the difficulty—five easy, five medium, five challenging. Flag the correct answer for each. Also suggest some true/false, fill-in-the-blank, and open-ended questions for variety.”

Prompt Example

I’m wrapping up a unit on the causes of World War One with my 8th graders at a middle school in suburban Texas. Here are the key concepts I want to review: the alliance system, nationalism, militarism, the assassination of Archduke Franz Ferdinand, the role of imperialism, and how a regional conflict became a world war.

Generate 15 multiple-choice questions based on these concepts. Format each question with one correct answer and three plausible wrong answers that reflect common student misunderstandings. Make five questions straightforward, five moderately challenging, and five that are a little tricky. Add a few bonus questions that require students to connect ideas. Flag the correct answer for each question. I want to use these for a classroom Jeopardy game.

The Fresh Angle Search

Bring new life to familiar content

Some topics get stale, especially when you’ve taught them the same way for years. To liven up an old lesson, it can be helpful to gather new sources, examples, statistics, case studies or unexpected angles.

Use , a free, AI-powered search engine that provides citations alongside its results. The links it provides ensure you have an evidence trail you can use to verify its responses and to dive deeper. Digging into Perplexity’s concise search summary is more efficient than sorting through hundreds of blue Google links.

Goal: Find five recent or unexpected real-world examples of a concept you’re teaching, including perspectives from outside the U.S. and connections to students’ current interests.

Prompt: “I teach [x topic] to [grade level x] students. [Provide additional context here about the topic or learning outcomes you’re focused on]. I’m looking for interesting material [or whatever other description you prefer] to make this subject more engaging for students. [Include any additional context about your students’ interests]. Find me five recent, unexpected or counterintuitive real-world examples of [x concept] that might surprise or intrigue students. Include also several real-world details to help add nuance for students who think they already understand the concept. And suggest several new analogies I can use for students who don’t yet understand this concept. Include international examples, and at least one that has an element of humor.”

Prompt Example

I teach introductory biology to 9th graders at a public high school in Phoenix. We’re finishing a unit on ecosystems and food webs, and I want to make it feel less textbook and more real.

Find me five recent, unexpected, or counterintuitive real-world examples of ecosystem disruption that might surprise students who think they already understand this concept. Include one example from outside the United States, one from the last two years, and one that connects to something teenagers are likely to know about or care about, like a sport, a food, or a place they might actually visit.

The Skeptical Student Prompt

Prepare for the hardest questions before class starts

You never know what odd questions might arise when you teach a new topic. AI assistants can help by generating all sorts of potential questions. That prep can help you avoid unpleasant surprises in class, so you’re ready for nearly anything students might toss at you.

Goal: Generate 10 challenging questions a skeptical student might ask me about this lesson.

Prompt: “Here is a [lesson plan / reading / concept] I’m teaching: [paste or upload material, mentioning the grade level and any other relevant context]. Give me a list of potential student questions about the relevance of this new topic and about real-world applications. Include also a mix of other unusual or surprising questions curious students might ask. If these high school students doubt this material is relevant, what might they ask, and what aspects in particular might they question. Generate 10 challenging questions students might ask. Include questions that challenge the relevance of the topic, the reliability of my sources and the assumptions behind my explanations.”

Prompt Example

I’m teaching the attached lesson next week on supply and demand. Imagine you are a skeptical 12th grader who thinks economics has nothing to do with your life.

Generate 10 tough questions you might ask during this lesson. Include at least two that challenge whether this concept actually works in real life, two that push back on whether the examples are realistic, and two that ask why any of this matters to someone who isn’t planning to work in finance or study business in college.

The Blind Spot Audit

Find your own blind spots before students do

During a typical week, we don’t always have time to trade peer feedback on lesson plans or syllabi. But we can still benefit from getting input on our materials. AI assistants can critically evaluate your materials for clarity, accessibility, inclusivity or other blind spots. You always have the option of ignoring the observations. I find that many of the weaknesses the AI assistant points out are ones that benefit from a fix.

Goal: Identify specific places in your lesson plan or syllabus where I might have an unconscious bias, where my instructions may be unclear, my examples may not reflect student diversity or my assessment criteria might be confusing. Or point out unnecessary jargon.

Prompt: “Here is my [lesson plan / syllabus / unit overview]: [paste or upload document]. Take the perspective of a critic with expertise in inclusive pedagogy and student-centered design. Identify parts of my plan that may not work for someone with physical differences such as a vision, hearing or mobility impairment. Also point out places where an unconscious bias might be influencing the way I’m presenting this topic. Point out places where examples or explanations I’ve included might not make sense to my diverse students. Show me places where my assessment criteria could be made more clear. Note any other sections of the material that might not be inclusive, accessible or relatable for students. Be direct. Include the location of each issue so I can explore potential fixes. I want specific critique, not general praise, and I want you to explain each observation in detail.”

Prompt Example

I’m attaching a unit overview I’m planning to use for a 6th grade reading and writing unit on personal narratives. I’d like an independent critique from the perspective of someone with extensive experience in inclusive teaching and middle school literacy.

Identify places where my instructions might confuse a student who is new to this kind of writing, or who struggles with open-ended assignments. Identify places where my examples or readings might not reflect the range of backgrounds in my classroom. Point out places where I could make my grading criteria clearer before students start writing. Be direct and specific. Tell me exactly where the issues are so I can find them quickly. I want honest, concise feedback, not compliments.

The Differentiation Prompt

Adapt one assignment for three distinct student levels without tripling your prep time

In many classrooms, students arrive at varying levels of readiness. Creating three versions of the same material is one of those things that turns a 40-hour week into a 53-hour one. Tasking an AI assistant with suggesting adaptations of your material ensures that your newly differentiated materials will remain anchored in your own ideas and teaching goals.

Goal: Produce two alternative versions of an existing assignment: one with additional scaffolding, and one with stretch challenges for advanced students.

Prompt: “Here is an [assignment / assessment] I give students: [paste or upload material]. Generate two versions of this: one for students who need additional scaffolding and more explicit guidance, and one that adds stretch challenges for advanced students. Preserve the core learning objectives. Summarize the suggested changes and explain their rationale, so I can decide how to adapt these alternatives for my students.”

Prompt Example

Here is a problem set I give students at the end of our unit on proofs: [paste assignment]. I have three pretty distinct groups in my 10th grade geometry class. Some students are still shaky on the basics. Most are roughly where I’d expect them to be. And a handful are ready for something harder.

Create three versions of this assignment. The first should add more step-by-step guidance and a worked example for students who need extra support. The second should stay close to the original but fix anything that’s confusingly worded. The third should add three harder extension problems for students who finish early and want a challenge. Keep the same core learning goal across all three versions. Add a quick note explaining what changed and why, so I can decide how to use each version.

The Rubric Builder

Help students understand how you’ll assess them.

A well-designed rubric does two things: it clarifies your expectations before students start working, and it gives them a roadmap for revising. Developing rubrics from scratch is tedious. It requires formatting small batches of text into boxes in complex tables. This prompt generates a structured first draft in table format. You can then refine it before sharing it with students. To start, specify the elements of the student work you’ll be evaluating, and describe your criteria.

You don’t have to use full sentences or formal language. Just describe what constitutes excellence for this assignment, what satisfactory work looks like, and what evidence signals to you that a student may need more skill practice. Developing these rubrics with AI assistance is an iterative process. Revise initial outputs by adding your own details and refinements.

Goal: Generate a rubric with three performance levels and five criteria you’ve specified, written in specific, concrete language, without vague phrases like “good use of sources.”

Prompt: “I’m assigning [describe assignment] to [grade level x] students. [Provide any additional relevant context]. Generate a rubric with three performance levels: Excellent, Proficient and Developing. Include five criteria relevant to this assignment: [list criteria, e.g., argument clarity, use of evidence, originality, structure, mechanics]. For each criterion and each level, write two specific sentences describing what that performance actually looks like. Avoid vague language like ‘good use of sources.’ Be concrete. Put this rubric into a table, then await my input for potential edits”

Prompt Example

I’m assigning an argumentative essay to my 8th graders. They have to pick a local issue, take a position, and back it up with at least three sources. Some of my students have never written a formal argument before.

Generate a rubric with three performance levels: Excellent, Proficient, and Still Developing. Include these five criteria: clarity of argument, quality of evidence, use of sources, organization, and writing mechanics. For each criterion at each level, write one specific sentence that describes what the work actually looks like. Skip vague phrases like ‘uses sources well’ or ‘writing is clear.’ Make it concrete enough that a student reading this before they start writing knows exactly what they’re aiming for. Put it in a table, then ask for my edits.

The Case Study Collaborator

Generate fictional scenarios to spice up discussions

Case studies help spark lively discussions. They’re useful whether you’re introducing students to ethical questions or trying to help students relate to a historical situation. They can also be useful for bringing a business decision or a scientific discovery to life. Creating cases from scratch can be exhausting. So this prompt helps you build fictional but realistic scenarios customized to your subject matter and student context.

Goal: Create a fictional case study to illustrate a tension relevant to your subject, set in a context students can relate to, ending with three discussion questions.

Prompt: “I teach [x subject] to [grade level x] students. [Provide an additional sentence of context or specifics to ensure the case studies are relevant and useful.] We’re exploring [x concept or issue. Include as much detail as possible about what and how you’re approaching the topic and your learning goals]. Create a fictional but realistic case study involving [type of character, institution, or situation relevant to your subject] that illustrates the tension between [value A] and [value B]. Set it in [context relevant to your students—a school, a local community, a specific industry]. The scenario should be complex enough that reasonable people could disagree about the right response. End with three discussion questions that I can adapt to push students to apply the concepts we’ve been studying.”

Prompt Example

I teach environmental science to 11th graders at a high school in a small city in Michigan. We’re wrapping up a unit on water access and environmental justice, and I want to end with a discussion that gets students to apply what they’ve learned to a realistic situation.

Create a fictional but realistic case study about a small city council deciding whether to approve a new manufacturing plant near a residential neighborhood with a history of water quality problems. The scenario should involve tension between local jobs and environmental risk. Make it complex and nuanced enough that reasonable people on both sides have legitimate concerns. End with three discussion questions that push students to use evidence, consider multiple perspectives, and take a position they can defend.

Disclosure: Two kinds of prompts appear in this piece. I developed the templates with brackets based on my teaching experience. The filled-in examples showing how teachers might customize each template were drafted with help from Claude, an AI assistant. Using AI to help generate these examples let me stress-test and customize each template across different subjects and grade levels and confirm that the prompts produce useful results. I reviewed and edited every example.

As AI Rewrites the Rules of Coding, Code.org Pushes to Reinvent Itself

Greg Toppo — Tue, 28 Apr 2026 16:30:00 +0000

Updated April 28, 2026

Teacher Jake Baskin remembers exactly where he was when he first watched the that introduced to the world, inviting kids to learn how to code.

“I was sitting in my high school classroom in Chicago,” he said. “I got a link to that first video and thought, ‘I’m so excited. Someone else is saying the things I’ve been saying to my students.’ ”

A longtime educator who now leads the , he watched as the nearly-six-minute video showcased Bill Gates, Mark Zuckerberg, Jack Dorsey and a constellation of tech celebrities recalling their first experiences with a computer: creating games, drawings, quizzes and more. “I was 13 when I first got access to a computer,” says Gates, a wistful smile crossing his face.

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It didn’t hurt that he and a few others onscreen were by then among the wealthiest people on the planet.

The video soon helped spark what would become arguably the most successful education reform campaign of the past few decades.

By 2021, offered computer science, known widely as “CS.” persuaded legislators in 12 states to add it to their high school graduation requirements. And every U.S. president since 2013 has made computer science a pillar of their education agenda.

Baskin liked the video so much he’d go on to spend four years at Code.org, helping the nonprofit write its first curricula and building district partnerships nationwide.

But fast-forward to 2026, and the landscape looks more fraught. So-called Silicon Valley “” have spent the past few years secretly building and while of software engineers. And the organization that made “learn to code” a national rallying cry must confront an existential question: In an era when generative AI tools can create functional code from plain-language prompts — and where kids are making millions “vibe coding” professional-looking apps — where exactly does a nonprofit called Code.org fit in?

New CEO Karim Meghji admitted that he and his colleagues must reframe their offerings and message without abandoning their core ideals. “Our foundational principle is not, ‘More kids need to learn how to be software engineers,’” he said in an interview. “What we’ve been promoting is that a world that is very digital, and has technical products all around us is a world where students deserve to understand how these things function, how they work.”

That reframing comes at a key time for the nonprofit, whose gift-fueled funding has in recent years, from $42.8 million in 2023 to $25.2 million in 2025. It reflects both shifting philanthropic priorities and the existential questions now swirling around the field of computer science.

Is computer science collapsing?

The shift Meghji describes is happening not just in K-12 education, but in the higher ed landscape and in the broader job market.Student enrollment in computer science at four-year colleges last fall, the biggest single-year drop of any major discipline since at least 2020. In one year, computer science fell from the nation’s fourth-largest undergraduate major to its sixth, even as the fortunes of Silicon Valley .

Karim Meghji

At the University of California, computer science graduates are expected to number about 350 next year, from 2025. Across the entire UC system, computer science enrollment declined last year for the first time since the early 2000s.

The job market for young coders has softened, too. A recent study by, using payroll data from millions of workers, found that by September 2025, employment for software developers aged 22 to 25 had declined nearly 20% compared to its peak in late 2022 — even as employment for more experienced developers held steady or grew. The study’s authors described entry-level engineers as “canaries in the coal mine,” early casualties of AI tools that can easily replicate their work.

Other data paint a less clear picture. A by the finance analysis firm Citadel Securities found that in the long term, software developers’ jobs may be relatively safe because replacing them en masse with AI would require “orders of magnitude more compute intensity” than the industry has. Alex Kotran, CEO of the , noted that job postings for software engineers are actually up 11%.

“Something that I just want to shout from the rooftops, is, ‘We really don’t know what is about to happen,’ ” he said.

That uncertainty, it turns out, is what Meghji is emphasizing as Code.org shifts direction.

Yes, AI seems miraculous and it’s improving quickly. But it also fumbles on occasion, , and generally threatening to on the world. Meghji invoked the notion of AI’s “,” which describes its strange, counterintuitive competence in complex processes — but that can also fumble .

For Meghji, a veteran consultant and technologist who most recently was Code.org’s chief product officer, that jaggedness is exactly why teaching computer science matters now: “The further we move away from how these systems work — the further we abstract away from what’s happening under the hood — the more important it is that students learn foundational CS and computational thinking concepts,” he said.

When AI shows its fallibility, he suggested, educators should view it as a teachable moment.

As it rebuilds, his organization plans to keep coding at its center while weaving AI into instruction, Meghji said. It has replaced its well-known “” with an Hour of AI, and it’s developing an “AI Foundations” course for high school students, due this fall, in which students use AI to help build and lay out interactive websites, then use a combination of their own written code and AI-generated code to improve the sites. A middle school curriculum is also planned.

“We don’t start with AI,” Meghji said. “We start with the foundation, teach the principles. Then we introduce AI coding, have students read code that AI is generating, find the issues, and hopefully have a higher ceiling — both in terms of their creative output, their agency, and what they’re producing.” He estimates that where previously perhaps five out of every 100 students built something genuinely impressive, AI tools could raise that to 30 or 40.

He’s also tweaking the organization’s business model. With philanthropic funding down sharply, Meghji said, he’s exploring whether Code.org can generate earned income through curriculum offerings tied to dual-credit and career and technical education pathways, models where public funding could help students earn technical credentials. He wants its curriculum to remain free for students but is exploring state and federal funding to underwrite it.

‘A fool’s errand in any field’

Meghji is also eager to correct a misconception that he believes was never really Code.org’s message: the idea that learning to code was to a six-figure salary.

“Our message was not, ‘Hey, come to Code.org, take computer science, and you’re going to write your ticket,’” he said. “We’ve always been of the mindset that every student deserves the right to learn the foundations of how technology works.”

Jake Baskin

Baskin, the former computer science teacher, said he wishes that distinction had been drawn more sharply from the beginning.��

“If I could go back in time, I would try to keep the movement from explicitly linking computer science to short-term career outcomes, because that’s a fool’s errand in any field,” he said. “No one knows what the jobs of the future will be like, and if they did, they’d be very, very rich. It’s about preparing students for the things we don’t know that are coming and giving them the broadest opportunity to engage in what is meaningful to them.”

aiEDU’s Kotran made a similar case, arguing that computer science should sit “alongside reading and writing and math and science,” not as vocational training but as the place where students practice so-called “durable skills” such as collaboration, design thinking, productive struggle and iteration.

He worries about the consequences if schools abandon the field entirely. “If we turn our backs to computer science, you’re going to have this deviation where kids who have access to those learning experiences are just going to be on a separate track,” he said, with access to knowledge that others don’t have. That’ll worsen inequality.

The strongest case an organization like Code.org can make, Kotran said, is actually a counterintuitive one: That AI, the very technology threatening to upend coding careers, might actually help recruit the next generation of computer scientists.

Alex Kotran

Despite the appealing creation myths embedded in Code.org’s famous intro video, he said most young people who study computer science must put in upwards of two years before they get to a place “where you could build something that’s actually cool.” But many students never made it that far. With AI, the time horizon shrinks: “Your first class is like, ‘OK, let’s vibe-code something. Think of a problem you want to solve that’s relevant to you — finding the right makeup, predicting fashion trends, sports data analytics, whatever,’” he said.

Students build something, but to further develop it, they need to go deeper and understand the code behind the vibe. Code.org and groups like it could open that experience up to students for the first time. “I don’t think we ever had something that powerful before,” he said. “And if we wield it right, we can actually start to reach kids who don’t think of themselves as CS kids.”

Updated: This story has been updated to reflect the most recently released funding figures for Code.org.

How AI Can Help Educators Teach Kids to Read, Without Replacing Connection

Michael B. Horn and Diane Tavenner — Fri, 24 Apr 2026 16:30:00 +0000

In this episode of Class Disrupted, hosts Michael Horn and Diane Tavenner turn from schools powered by artificial intelligence to the tools themselves.

Matt Pasternack, founder and CEO of , shares the company’s journey from its low-tech beginnings to an AI-powered platform for early reading instruction. Pasternack shares how Once now delivers effective, personalized one-on-one tutoring through software, while emphasizing the importance of human connection in the learning process.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey Michael.

Michael Horn: Hey, Diane. Good to see you after a few episodes, diving deep into school models and thinking all about AI and what it enables today.

Diane Tavenner: Indeed, we are going to shift gears a bit today, not away from AI because based on all the emails, the calls, the feedback we’re getting, this is the thing folks are thinking about and talking about. And so we’re sticking with AI. Rather, we’re going to shift away from AI school models, full school models, and infrastructure to how AI is being used directly by and with teachers and students and in classrooms. And so I think this is going to be a really interesting other dimension of what’s happening.

Michael Horn: Yeah, I think that’s right. I mean, we thought this was the next logical place to go. Everyone says if you’re not changing the classroom at some point, you’re not changing much. And so we wanted to go into that classroom and explore how a small number of folks, entrepreneurs, are thinking about really how do we use AI in classrooms without the entire school itself or the system around it being changed. And so this is it. I’m really excited for this conversation. Someone we’ve both known for a long time and get to go deeper on it Diane.

Diane Tavenner: A very long time. I’m really excited today to welcome Matt Pasternack to the conversation. I was trying to think about when we met Matt, but it feels like so long ago. I can’t even remember at this point, because you, you actually were early at School of One, which is now Teach to One as a director of assessment. And then you went and you were on the founding team of Clever, which many people will know as a tool that effectively connected edtech products to student information systems. So this really critical infrastructure piece that enabled so much of what we now sort of take for granted in terms of technology. And then most recently, you’re the founder and CEO of Once, which is a company that leverages, well, I’m going to say what I think it is, and then we’re going to get into it. You’re going to really describe it for us.

A company that leverages the research around tutoring by using reading-based software and human people to teach 3 to 7-year-olds to read. And so we’re just grateful to have you here. Thanks for joining us.

Matt Pasternack: I’m thrilled to be here. I’ve known you both for quite some time, and it’s really an honor to be on the show.

Michael Horn: Well, we’re thrilled you’re making the time for us. So let’s dig into it. As Diane mentioned, you are working on solving a problem that we’ve talked about on this podcast historically, which is teaching reading, something that there’s a lot of evidence around how to do. But as Diane said, before tackling this reading challenge, you helped build Clever. And it does feel like a big shift from, if you will, school infrastructure to teaching, learning, curriculum. Maybe it’s back to your roots in some sense from School of One. But tell us about the origin story of Once and your own personal why for building it.

Rethinking Early Reading Education

Matt Pasternack: Sure, sure. Well, I’ll share two things. You know, one is that, you know, very early in my edtech career, before Clever, I worked on some projects that were very, very expansive in what they tried to accomplish, and both on the data infrastructure side as well as on the curriculum side. And it was sort of the heady days of the late aughts or early 2000s, and then the early 2010s, excuse me. And sort of this belief, if you just sprinkled a little bit of technology or software magic on things, education, everything would just work. And there were these really exciting analogies to Netflix’s personalization. I’m sure you remember those days. And so I spent some time trying to boil the ocean.

And then after that, I just kind of made an abrupt turn and never looked back. And I said, look, I want to work on specific problems in education. And the first one was what Clever tackled. It was an area I had a lot of insight into from some of those earlier explorations on, you know, the specific problems that rostering and single sign-on presented to schools, and particularly schools trying to adopt software in varying ways. After Clever, I kind of went in a little bit of a different direction for a while, focused on voting. And then in the sort of early to mid-pandemic, I was catching up with an old friend of mine who had been a teacher in the same school where I taught after college, and we were talking about how during the early pandemic, both of our kids who are kindergarten age were out of school because schools shut down. And yet both of them learned to read in that year out of school.

Now, my wife is a former kindergarten teacher, and so I was like, oh, I had an unfair advantage, you know? I mean, she had sort of one-on-one tutoring from a kindergarten teacher. Missing kindergarten, but, you know, my close friend was a former middle school teacher, and, you know, it’s not like he had been working on that expertise forever, but he said, yeah, you know, structured 15-minute lessons every day, you know, his child learned to read, and we just sort of stepped back from it and said, wait, you know, for as long as anyone can remember, we’ve been teaching kids to read, attempting to teach kids to read, in these sort of 30-person classrooms, and, you know the numbers, and in sort of any education environment, about half of kids, if given some of the right foundation, will learn to read to some extent. And so any teacher can look at their progress and say, well, some of my kids are learning to read, it’s working. But if you want all of your kids to learn to read, you know, you can’t say that what’s happening today is working, because we’ve had flat NAEP scores for decades. And so we just started to kind of think really big picture and say, you know, what would happen if every kindergartner got the education that our two kids had gotten in the early pandemic, which is just 15 minutes a day of one-on-one reading instruction. And initially, we actually wanted to avoid schools, avoid K-12. We said, look, the sales process in the K-12 is so complicated. Let’s do something different.

Let’s actually focus on, because preschool, you know, research was just coming out about how early the brain starts developing. For where children begin, you know, learning, you know, are able to learn to interpret written language. And then as we start to explore the preschool route, we realized that that was probably even harder than K-12. And so we kind of returned to our roots and started in a charter school, actually was our first implementation, and basically just said, hey, can we put a couple people in the back of this TK classroom and teach, you know, have them provide daily one-on-one tutoring to the students to help them learn how to read. And we had some amazing success stories, and, you know, it was just a couple-month pilot. There was a child who, a child who came to school every day, you know, lots of home context, crying and kind of hiding under his desk and did not want to be in school, And it still makes me kind of emotional to tell the story, but, you know, even a month or two in, he was sort of opening up to school and responding to school, and people asked him, you know, what had changed, and he said it was the one-on-one tutoring he was getting every single day. You know, and that’s a 5-year-old who, you know, that’s going to determine the next, you know, the rest of his life, essentially. And so, you know, we kind of took that and said, and the folks who were providing the tutoring, had not had background as reading tutors.

That was really important to us, because we said there just aren’t enough kinds of trained reading specialists in the country to do this at a scale of 4 million kids a year. So you need a method that allows any adult to access material and provide this instruction. And then we had this really interesting call with Portland Public Schools, and this is kind of right after that first, right as that pilot was winding down. And we were talking to them, and they said, well, you know, we can’t, you know, we can’t afford to, you know, sort of put all these other people, you know, pay you to bring all these other people into schools and teach kids how to read. We don’t even know where you’d source them. But they said, look, we have tons of instructional assistants. That was a position that they had at the time who were supposed to be working on reading in the early grades. You know, could we use this type of program with them? And that was kind of our lightbulb moment because we said, look, you know, we are, we’re not an HR company, right? We have no expertise in how do you hire, you know, tens of thousands of tutors all over the country to provide this instruction.

We, you know, attempted it in some pilots and literally were unable to source even like a couple tutors in some metropolitan areas. And so we said, We want to work with the staff the school already has. And that really, that, you know, that conversation launched everything. We did not win that deal. We did not end up serving Portland Public Schools. It was okay. That learning that they gave us in that phone call was, you know, worth its weight in gold. And I’ll always be grateful to them for that experience and taking the time with us at that early stage in our journey.

Diane Tavenner: Matt, I love that we’re going to get sprinkled into this conversation, this bonus of just what it’s like to try to build something and sell the schools and whatnot. So that’s really fun to hear that piece. Listening to your origin story, people might be saying, wait a minute, you’re spending this season talking about AI and education, but it doesn’t seem like Once is related to technology even, or AI. And in fact, you began in that pandemic period. So before the sort of famous release of ChatGPT in November 2022. And so, you know, as a guy who at face value seems to have been tech-forward, what was your sort of original hypothesis and approach to making sure, you know, to just sort of going what it seems like a full human approach to reading? And is there technology in here anywhere?

Matt Pasternack: Yeah, so it’s a great question. I think it has, there’s a two-part answer. There. The first is that one thing we were certain of from day one was that young children learn best from adults, like actual in-person human-to-human instruction. You know, for millions of years, you know, our species and the predecessors of our species have been, you know, teaching children to identify different berries and, you know, what animal is going to hurt them and which ones are safe and, you know, how to survive. And that was all done, millions of years of evolution. For, you know, in-person communication between an adult and a child, where the child was highly motivated to learn because the stakes were life and death. And sort of the idea that you would deviate away from that just sort of seems somewhat crazy on its face to us.

Matt Pasternack: So that was the motivation for, you know, you need to build trusting relationships with adults, and, you know, technology is scalable in a way that people sometimes aren’t. So how can we apply technology? But let’s do it in a way that deepens that connection and doesn’t attempt to replace the connection or build a kind of robot teacher. You know, the other piece of this was that I’ve had the privilege in my career, I’m not a software engineer, but I’ve had the privilege to work with, you know, some very, very, very incredible software engineers. And what I’ve often noticed is that when you start working with some of these folks, the first thing they’ll say is, hey, let’s build a version of this product in Google Slides or Google Docs or Google Sheets. Let’s build something that’s throwaway, but that we can learn from because we’re going to waste too much time. This is a little bit before the days of live coding, but we’ll waste too much time coding something up. Let’s be really, really lean in terms of how we want to model what we want to accomplish. And so our perspective at the beginning was, That’s what we want to do.

We literally started in, you know, Google Slides and Google Sheets were essentially our technology. And, you know, funders who were interested in tech staff basically ignored us because we weren’t very technical. But the one thing we said we were going to do from day one is we wanted to record every single instructional session that was given. So even though we’re talking about recordings on Google Meet, we sort of knew where things were going. I mean, yes, you hadn’t had these massive releases of OpenAI and various things, but AI was in the air, the late teens, it was in the air. Folks knew where this was going. Technology was going to fundamentally change. And we just had this belief that if you had hundreds of thousands, millions of hours of recordings of adults teaching kids to read, even if you had no other technology, that you could then, one day run software over that archive and begin to do things that others would say, oh my gosh, I wish I had this sort of database, but I don’t.

So that was, I think those were kind of the two key decisions and motivations for that early, less technical beginning.

Michael Horn: Well, so let me actually jump in there then, Matt. Let’s fast forward us to where you are now. So you’re not going to be an HR solution. You say we’re not in that business. You have all these recordings. What does Once look like today? What does it do? How’s it maybe similar, different from, you know, those initial, put aside the very early pilots, but, you know, once you sort of got on the ground and running, tell us how it’s evolved from that?

Matt Pasternack: Yeah. So what we do today is we serve sort of two different audiences. We serve school districts and we actually serve parents at home. And so I can kind of talk about those a little bit in turn. But what we do, I’ll start with school districts. In school districts, we solve two interlinked problems. One problem that we solve is that there are too many kids in school districts who have not effectively learned how to read. And sometimes I think the best evidence of that is when you have, you know, middle schools or high schools saying, hey, we figured out what we have to do differently.

Phonics and Staff Upskilling Initiative

Matt Pasternack: We have to start really emphasizing phonics in 7th and 8th grade or high school. It’s like, on the one hand, great. Kids who haven’t learned that do need to learn it, but that’s the evidence of an enormous problem, because there’s really kind of 1 to 2 years of phonics to learn, and if you’ve had 9 years of schooling and that’s like, that’s the set of activities, that indicates just a huge, a huge, you know, a huge mess basically in the early grades. So one problem is reading, and the other problem is a lack of career ladder for entry-level staff in schools, which is also a really important problem because schools are having tremendous challenges finding a large enough teaching staff to teach the children in school. And so we said, let’s solve both those problems at once. By providing coaching and curriculum to elementary school support staff, you know, paraprofessionals, teaching assistants, instructional assistants, you know, there’s a number of different roles, as long as they’re not lead classroom teacher, because then they have to oversee 20 or 30 kids. By providing curriculum and coaching to those folks, we can upskill them to the point where anyone can provide one-on-one tutoring daily for 15 minutes to, you know, kindergartners is where we focus. We do some TK and first grade, but kindergarten’s really our focus, provide 15 minutes of daily instruction to those children.

And what this looks like, you know, kind of from a technology perspective, ‘cause again, we started very low-tech, we’re now, we are a software solution today. And what this looks like is the child and the paraprofessional sit down side by side in front of an open laptop computer, We call it, we teach the parents how to build a one-desk classroom. So they have their desk, it’s all set up with the physical materials they need and their laptop computer, they sit right there. A ton of work has gone into that organization. And on the screen is, on one part of the screen is what the child’s looking at, on the other part of the screen is the script for the adult. And the adult is basically going through the script and the child is responding to the script and looking at their part of the screen. And going through a number of different science of reading-based exercises and tasks that teach the child how to read. And the adult is using a fair amount of judgment in there as well, you know, identifying when are children saying things the wrong way, when are they saying things the right way, how do you keep children motivated through this process, but the kind of focus is what’s happening on the screen.

And then we record those sessions and we provide coaching. We have a national team of coaches who then watch those recordings, and provide feedback to the paraprofessionals about how to improve, you know, essentially how to improve their instruction. Like you’re basically watching game tape of yourself and using that to improve your instructional techniques. So that’s sort of a big piece of what we do.

Diane Tavenner: Super interesting, Matt. Can you, so you’re starting in this very low-tech way with these Google spreadsheets and slides and, you know, basically prototyping what you’re gonna do. And If I remember correctly, you were videoing and then literally like doing training calls with those folks, you know, like, oh, we watch your video and let’s coach you up on this essentially. And now that’s, that’s a much more seamless software experience as you just described. Take us to that moment where AI becomes the reality and you’ve made this really smart bet and you have all these recordings. How does AI figure in here? And we’re trying to be really thoughtful. We’ve recognized that people use the word AI or whatever that is, acronym AI, to describe almost everything, like this massive range. So the specificity is super helpful of like how you actually use it here and what role it is playing.

Matt Pasternack: So we use it in a couple of ways. All of our AI is behind the scenes. there’s, So you know, this is not a chatbot. This is not, you know, either the paraprofessional asking a quick question, wait, how do I do this? Or obviously the student doing that because, you know, they couldn’t use a chatbot, they don’t know how to type yet. It’s all behind the scenes. One key way that it’s used is we are able to administer oral reading fluency tests very, very frequently on the students because we’re capturing the full session. And so then we you know, we have, have all the data, the speech data, the transcript data from that session, And so we can go and we can evaluate oral reading fluency. You know, in a typical classroom, elementary school classroom, if they use some, you know, assessment like DIBELS, the teacher might do 3 oral reading fluency assessments during the year, once the beginning, the middle, and the end.

Each one takes roughly a month because the teacher has to pull each kid aside one-on-one and administer this test. And so it’s like 3 months, you know, of instruction to some extent are spent not providing instruction, but performing these tests. And that’s just, you know, that’s not the best use of teachers’ time. And so simply by just having all the kids individually learning on camera, we’re able to run these tests in the background, and we have you know, results, every 2 weeks. So that’s a really important piece of AI. Again, the parents may have no idea that AI is even involved in that, and that’s great. Like, this is not you know, sort of that Gemini sparkle in the corner. This is just like, no, it’s actually just making the experience seamless.

Connected Phonation and AI Learning

Matt Pasternack: The next thing that we do is we’re huge fans of what’s called connected phonation. Again, if I’m going too deep into Tales of Literacy, let me know. But you basically, know, a lot in, in some systems, even sort of science of reading evidence-based systems, there’s a lot of focus on kind of tapping out individual sounds. So if I want to say the word bat, it would be the /b/ sound, the /æ/ sound, the /t/ sound. And when you teach a child like, oh, you just sort of say /b/, /æ/, /t/. And it’s like, well, that’s bat. Like, that doesn’t sound like bat. That sounds like /æ/t/.

You know, it sounds like these sort of disconnected sounds. And you can, you know, that is again one way that people teach it. But I think the more modern approach that, you know, University of Florida and others have really emphasized is connected phonation. So you’re not, if a B and an A come sequentially, you’re gonna teach a child to go, ba, like that B is kind of a quick sound and then they’re gonna go right into that long A sound and they’re always gonna do that when they see those sounds in sequence. They connect those sounds. Well, not only do we have audio of what’s happening in the lesson, but we also have little sliders underneath the words, and so a child can move the slider while they’re saying the word, And we can use AI to really notice things about, you know, is the child’s finger actually tracking what they’re saying? How much are they connecting? Are they pausing in the right places? Because some kids, you know, maybe they know the word “bat,” so they just want to jump in and say it really fast. Well, that’s great for that word, but you don’t then learn the fundamental skills that will help you read much more complex words. So, you know, evaluating something like how quickly does a child’s finger move on the screen is another beautiful application of AI?

Then really for us, the last one, it seems obvious, but it’s just using AI to write code. It’s just we are able to move 10 times as fast on 10x fewer resources as we would be able to otherwise before this moment in time. We wouldn’t be able to do what we do without AI assisting in development. But I’ll say, sort of one level deeper is, you know, where does it go from here? Is it just these sort of fluency tests and, you know, kind of tracking fingers on the screen? You know, as we progress, you know, you can really use AI to evaluate, you know, student fluency in real time, right? How did they just pronounce that word that was just said? This is a much harder technical problem than it sounds like because while the tech world has made huge advances in interpreting adult speech, where you basically try and eliminate the errors to turn some garbled thing an adult says into something intelligible. With kids, you want the opposite. You actually want to figure out which of those sounds when they pronounce that word was incorrect. Rather than automated speech recognition, you want automated phoneme recognition. And if you ask, you know, folks who are really deep in that world, everyone will admit we don’t have good automated phoneme recognition today.

It’s just not there yet. But we have, again, the video archives that will let us build towards that. So we’re very, very excited about that. You can also begin to use AI to you connect, know, reading, many folks regard as multiple strands. So there’s comprehension, there’s, you know, there’s phonics, there’s phonemic awareness, there’s these different elements of it. And so how is a child progressing on these different strands, and how does progress on one strand kind of influence what you would want to teach them next? So really, that sort of learning engineering And then the final piece, just to kind of fill out the story, is, you know, a lot of folks will tell you, I’ve heard on your episodes before, that, you know, 90% of learning is motivation, you know, 10% of the technology, the curriculum, but most of it is really the motivation. You know, we have, if you want to learn, between YouTube and all the different educational apps out there, like, there’s never been a better time in history to learn, and yet we see, you know, essentially flat performance over the decades. Well, one way to motivate both learners, but even more importantly, their teachers, is to show them a highlight reel of like what’s gone really well.

Well, it is prohibitively expensive for us to, you know, manually curate a highlight reel of every session of instruction. But if a pair has taught 10 kids and at the end of that day, or 20 kids, know, you they get to see, hey, here’s a 30-second recap of like your best aha moments during the day, I am coming in tomorrow. I’m not gonna call out sick. I’m not gonna do anything else. I’m going to do it, and I’m going to kind of push those kids as far as I can take them. And so that might be the most magical application of AI at all, not even kind of automated phoneme recognition, but just literally highlight reels of the incredible instruction we’re seeing.

Diane Tavenner: That’s fascinating because we’ve had several conversations about motivation here, and I personally have felt kind of unsatisfied about those conversations. So this actually seems like a really amazing use of the technology related to motivation. Let me ask you about something that comes up, I think, a lot in the reading conversations, dare I say the reading wars, which is this idea that kids need to be reading things that they’re interested in and that they care about and that they’re motivated by, you know, those sorts of things. When you’re in this early stage of teaching reading, are you personalizing what the kids are reading at all? Does that matter? You know, is technology supporting that? What’s going on there?

Matt Pasternack: Yeah, it’s a great question. We are definitely keeping an eye on the ability of AI to generate what we would call, or what many people in this sort of industry would call, decodables, which are texts that students are able to decode. A lot of times things are called decodables, that actually aren’t decodable to the student. So one thing that’s really interesting about reading is that, you know, you might think, well, if a kid, you know, if they can read 75% of the words, like, that’s probably good enough. Like, that’s better than not, you know, being able to read 10% of the words or something. But actually, comprehension often requires, like, being able to read 90 to 95% of the words. So if you want to build decodable texts, like, there is not really a margin for error. You need to make sure that kids are reading text with words that they are able to decode.

Doesn’t mean they’ve memorized those words. I’m not talking about sight words, but they’re able to decode those words. And the challenge of reading English, not true of all languages, but English, is that, you know, a single letter can have many different sounds depending on what other letters are around it and kind of how it’s positioned. And so if you just tell a computer, build stories using this letter or you know, using this sequence of letters, it will often inadvertently pull in the wrong, pull in the words that the kid isn’t able to decode. Well, if it pulls in one of those, maybe that’s OK, they can figure it out from context. But if you’ve got, you know, a page with 15 words and 5 of them aren’t decodable, like, this decodable is not decodable. And that’s where kids can lose motivation. So it’s deeply interlaced with this concept of motivation.

So we’re keeping an eye on it. We don’t think it’s quite there yet. At the age that we’re working at. You know, I know some people working in higher grade levels, and I don’t have the expertise there, but in the kind of 3 to 6-year-old grade level, we are very, very carefully still hand-curating stories at this point.

Michael Horn: It’s fascinating, Matt, because as you alluded to, there’s this huge conversation around can you level reading? And, you know, some of that is directed at particular publishers that tried to do it in a way that was not related to Decodables, and some of it is just a broader conversation. I’m taking some new information away from this conversation, so I appreciate that. Let me shift though, rather than go too deep in that, because one of the other things that you’re doing, I think, is pushing on how AI can start to redefine the role of the educator themselves, right? In some ways, you have AI, it seems, making sure that they stick to what’s the evidence around how to teach reading the best, right? So that we’re not getting too far ahead of ourselves or freelancing in ways that may be detrimental. Then you’re also deferring to judgment, it seems, for the educator around that motivation piece and what they’re seeing on the ground with the kid in ways that you just can’t pick up with technology today. Help us understand that shift over the time around what you think that Reading Coach ultimately is and that split between technology, the human judgment, and how that gets redefined. Maybe we make it easier, frankly, or more people can be great reading coaches in the future?

Empowering Adults to Teach Reading

Matt Pasternack: Yeah, yeah, it’s a great question. It’s a great question. You know, our vision is that any adult, and by adult, actually broadened to older teenagers, are able to teach children how to read. And I think if, you know, if you look, you know, we sometimes cite a stat that, you know, 95% of kids are taught, you know, the ABCs by their parents or guardians, right? It’s like parents and guardians, they’re trying to do the right thing, They just don’t know what to do. Like teaching a child the ABCs, for a couple kids will teach them to read, for almost all other kids, it will not teach them to read. And so, and so they have, they’re motivated, they know how important this is, they don’t know what to do. So we want any adult, doesn’t matter if English is your second language, it doesn’t matter, you know, if you’ve struggled to read yourself, like we want to empower any adult to be able to teach any child. Now, you know, you do sometimes, there are conflicts where, do I want to give the adult who’s delivering the instruction more autonomy so they can kind of grow more in their career, or I want to remove a little judgment and make it a little more scripted? And I think over time you’ll see dials in our system where you can kind of dial it up and down either way.

Because once you get to the point where it’s just the computer speaking, well, now you’re back to just computer education with an adult patiently sitting there. That’s not how kids learn to distinguish berries, right? Like, we’re actually, we’ve gone too far. So the adult needs to be really, really involved in that teaching, but, you know, understanding their capacity and what they feel like, you know, sort of, you know, because it’s not just reading a script. It’s, you know, the hardest thing in teaching reading is what do you do when a kid makes a mistake, right? Like, we have a whole sort of flowchart, a whole approach we take when a child makes a mistake decoding. And being able to implement something like that is hard. And so software can help. Our biggest innovation kind of in this regard, which is something that we’ve just started to roll out recently, is no longer restricting this actual teaching to paraprofessionals or instructional assistants or existing support staff at the school, but actually broadening that circle to older high school students. That’s where I sort of hinted at teenagers.

And it is, we’ve just gotten started here, so we, you know, we are learning as much as they are. But I’ve seen some pictures and I’ve seen some videos of a high school student sitting down next to a kindergartner, teaching them to read, with the, you know, the little kindergartner just eyes full of adoration. You know, this is not a teacher, this is someone who they deeply look up to, this, an older kid who actually cares about them. And it is so inspiring. And if we had a world where our high school students could knock out reading specialization for a large number of our kindergartners, I mean, I think it would change it.

Michael Horn: Generational impact would be huge.

Diane Tavenner: I mean, yeah, when you called me with this idea, Matt, I haven’t stopped thinking about it since because I think that this is the type of thinking that we need in education right now because this checks so many boxes. I mean, not only are we putting multi-age groups together and learning, right, but we are giving, let’s talk about the impact on the high school student here who feels a sense of worth and purpose and is actually doing an early job, gaining real experience coaching and developing person. Who knows where that could possibly lead? And then you have this brilliant idea of enabling them to be entrepreneurial. Like, imagine a neighborhood where teenagers in the summer sort of have the Once tool and they can open their own little neighborhood business teaching kids to read in the summer. Like, I love this so much for so many reasons. It’s really brilliant.

Matt Pasternack: Yeah, I appreciate that. I mean, we are, you know, again, we’re learning so much right now. But the response, you know, I mentioned I kind of joked earlier about, you know, selling to school districts and how hard that was. And I mean, it really is every entrepreneur’s challenge. And in K-12, you know, there are these magic moments when school districts don’t become hard to sell to. It’s really hard to find those products. Like, it is extremely hard. Clever was one of them.

Student-Led Reading Program Growth

Matt Pasternack: I mean, Clever just blossomed across the country, and it was so exciting to see, you know, sort of help it spread and kind of watch the spread, and it was something very special. You know, we are not operating at Clever velocity right now in terms of distribution, but we have gotten such an outpouring of response, superintendents who write in saying, you know, I get a million cold emails a day, you know, I don’t read any of them, but when I saw high school CTE students teaching kindergartners how to read, I was like, oh, that’s it. You know, that’s, that’s so obviously it. And we’re hearing that response. One thing that’s so exciting, to date we’ve primarily focused on kind of top 500 districts, it’s very large districts. A lot of them are urban, but you know these very big school districts. And we are getting this response from tiny districts, you know, also from some large ones, but from tiny ones, from districts that can’t really afford to have paraprofessionals, but they’ve got high school CTE students and they’re looking for a great thing for them to do. And they’ve got kindergartners who aren’t learning how to read. And it’s like, let’s go, you know? And that is just amazing.

And so we are flying to, you know, towns across the US right now that, you know, we’ve never heard their names before. And just being embraced by the folks who are there and just going right in. It’s, it’s just, it’s wonderful.

Michael Horn: That’s so cool. I mean, I think about the leadership opportunities, responsibility, judgment, just like it’s checked so many boxes. And then again, the generational impact, if you do that at scale, could be humongous. Let’s wrap up with this last question, which is before we go to our what you’ve been reading and watching outside of work thing. But, you know, look, Once, as you just talked about, is designed to be facilitated by, could be an instructional aide, parapro, high school student, whatever, it’s not a whole class curriculum, right? And so you talked about those magic moments where districts actually start to absorb it and so forth, but take us into the classroom itself. How are schools putting Once into their schedules and days? How are they integrating it? What’s the impact it’s having about how they think about, you know, the whole class activities that they perhaps have been doing? Are there trade-offs that they’re having to make? Help us understand where does it lock into the current schedule?

Matt Pasternack: It’s a great question. It’s like the fundamental question to making tutoring work. I mean, a lot of the leading tutoring researchers have said tutoring is fundamentally a question of logistics. You know, anyone, you know, so many providers have great training and many have great coaching and various things, like can you get the logistics right or can you not, know?

And so we go very, very deep with our school district partners. We have, you know, a world-class, we call them our program team, and they go in and they work because it is not like, they’re not district-wide answers to how you solve that question. You cannot go to, you know, XYZ Public Schools with 100 schools and say, OK, we’re going to do once during these blocks in this space in the school, because every school has slightly different requirements, different spaces, you know, different people. It’s all different. And so we go school by school. We help them map out a schedule. Will this instruction happen in the back of the classroom? Will it happen in a little annex room that’s right near the classroom? Does it happen in the hallway if the hallways are quiet? Often in elementary schools they are, it just sort of, again, depends very much on that school. Oh, you know, here was this space that was used for teachers to congregate, but teachers aren’t actually congregating there, or, you know, the lunchroom is actually empty from 8 to 10 AM every day, that’s unused space, like, let’s take advantage of that.

Customized Solutions for Every School

Matt Pasternack: We have worked with so many schools by this point, we just have thought through all these different permutations. And so we sit down with the schools, school by school, come up with a customized solution for each one. And, you know, the challenging part, if you’re listening to this, is that it sounds hard and expensive and, you know, and it is, I don’t want to minimize like that is, that slows down scale a little bit when you have to kind of do hard things. On the flip side, we have never come across a school that can’t do this. So sometimes you have a solution that says, oh, well, when we rewrite the language of schooling, when we completely change what schools look like and how they’re structured and what the day is, then that will unlock AI and unlock everything, and then kids can finally learn. And if you’ve been around schools for a long time, I think many of us would be pessimistic that you’re going to see rapid changes in those dimensions anytime soon at scale. And so we’re really proud of the fact that we can go into any school district, any school building, and we will find a way to, you know, to build up the schedules. And the neat thing again about the application of technology, in our earlier days, we, you know, back in the kind of Google Meet and Google Sheets days, it took so much training and coaching on how to make those very lightweight technical tools work that realistically someone giving instruction could only give it to, you know, would have to give it to at least 10 or 15 or 20 kids because the investment you put in and teaching them the ropes made it, it just wasn’t worthwhile to teach a single kid.

But as we’ve amped up our software and amped up this use of AI, you know, we can realize this vision. You have high school kids coming in and doing it. The school secretary, does she have a free 15 minutes or 30 minutes? That’s 2 kids right there. In many schools, the principals say, hey, I don’t want to just administer this. I want to work with the you very hard, not hardest, like most difficult kindergartner, the most challenged kindergartner, the one who maybe has, you know, home circumstances that are you know, the most or, challenging, you know, who has you know, a, a learning difference, or for some reason is really struggling in the classroom. I want to take that kid under my wing as a school principal, and I you know, I want to teach that kid to read, and I want the school to see that I’m teaching that kid to read. And it goes a little bit back to, you sort of think of Steve Jobs, he’d go into Apple devices and he’d look at the wiring configurations in the background, like the things customers could never see. And he’d say, if the wires are messy in the back, then it shows that we don’t actually care about what we’re building and we’re never going to build good stuff, even though customers would never see that.

And I think it’s that attention to detail. And historically, principals maybe are really strict about kids lining up, are really strict about like certain small details that then create that school culture. Well, here’s another detail that shows, you know, my focus is on the kid, and it’s really beautiful to watch.

Diane Tavenner: Yeah, well, and thank you for that. In early elementary school, I mean, Michael and I have talked about this often, like we are hard-pressed to think of something more important than what happens in early elementary school than literally every child learns to read. And so I’m glad to hear that people are doing what’s necessary and that they understand that it’s totally possible to organize school in a way that every child will learn to read. That feels so critical. So thanks for the inspiring story of what you’re doing and the connection between the humans and the AI. Really, really fun.

Diane Tavenner: And so now, of course, Michael and I are very curious to hear what you are personally reading or listening to or watching outside of your work. We try to stay outside of our work.

We break our rule often, but if there’s something that you have to share, we’d love to hear it.

Matt Pasternack: Oh, well, no, I’m happy to. Actually, yeah, I feel like for many years between raising kids and having intense jobs, I really didn’t find much time to read other than, you know, sort of the newspaper and things like that. But I have recently joined some book clubs and gotten back into reading, which feels great, and I cherish it. I’m currently in the middle of Demon Copperhead. I don’t know if you’ve read that, but the first part was so depressing. It just, you know, it’s, you know, lightly based on Dickens, and it just felt like, oh my God, like just kind of going down that, going down that hole. And it was a little challenging to get through. And now things have turned around a little bit.

So I haven’t finished it. I’m excited to see what happens. But I’ve been having a lot of fun with it. And then for watching, I just got to watch, I watched Alex Honnold’s ascent of the Taipei Building on Netflix.

Michael Horn: Very cool.

Matt Pasternack: Which was very fun. I watched it after it was over, so I knew at the beginning he was going to stay safe. But I got to watch it while running on a treadmill, which is a really fun experience. I’m not sure I’ve ever been able to push myself that hard. It’s like, well, this guy’s doing something much harder, so I can at least run fast. So that was enjoyable.

Diane Tavenner: That’s awesome. Well, Michael and folks who’ve listened for a long time will know that when I tell you we’re preparing for a trip to Morocco and southern Spain, that means that I’ve got a combo fiction and nonfiction reading list that I’m working through because that is sort of how we do vacationing. And on this one, I’m digging into sort of religion, culture, history that is not very familiar to me. So it’s been a fun learning journey. I’m grateful to Gemini, who is my study buddy for this one and is actually such an incredibly useful tool for these purposes. At the moment I’ve got 3 books going. So, one is called Dreams of Trespass: Tales of a Harem Girlhood by Fatima Marisi. Sorry, butchered that one.

And it’s not what you think. I’m learning a ton. It’s actually quite an interesting feminist, story,, and then 2 others. So Islam by Karen Armstrong and No God But God by Reza Asad. And those are really interesting different perspectives on the religion that I’m sort of reading side by side with each other. So super fun.

Michael Horn: Very cool. Very exciting. I always love when you share these, Diane, because as listeners also know, you’ve changed my own practice around travel, uh, to start to do this habit as well, and Matt, I liked your Dickens, reference because it connects in an odd way for the one that I’m going to do, which is Diane knows I don’t read a ton of fiction, but I actually finished a fiction book here, In the Shadow of the Greenbrier by Emily Matchar. I’m probably also bungling her last name, but I picked it up, honestly, I was at synagogue. I saw it in the temple library, and Greenbrier was a place that we used to vacation with my grandparents and my cousins a few times growing up over Christmas. And so I was sort of curious, and it’s like this intergenerational Jewish sort of mystery story, if you will, trying to piece together different puzzle pieces. And the Greenbrier is sort of the central part of it.

But the only reason I say that, Matt, is I remember one of the years that we vacationed at the Greenbrier, they had like the foremost Charles Dickens expert or something like that in residence, and he gave lectures, which we as 12 or 13-year-olds dutifully attended and I think did not cut up too much during, which, which was impressive for us. But, so I, I feel like I’m connecting on a bunch of these things at the moment, but, uh, it was a fun read, and Diane, one of the reasons I don’t read a lot of fiction, I forgot, is because when I read it, I don’t put it down and I become a bit of a zombie around the house for a couple days.

Diane Tavenner: So yeah, there is such a thing as binge reading, just like binge watching, right?

Michael Horn: Exactly. And nonfiction, while I love it and I read it a lot, turns out it doesn’t do that for me, whereas fiction I’m a lost cause around the house.

Matt Pasternack: The secret is just simultaneously reading it on the Kindle and Audible, and then you can, you know, you can volunteer, hey, I’ll go do the dishes, you just put in

Michael Horn: Exactly, plug it in and power through.

Matt Pasternack: You keep going, right, you know, right where you were.

Michael Horn: So good, good tip, good power tip. All right, huge thank you, Matt, for joining us. And again, to all of the listeners, who keep coming in with all sorts of feedback, both positive notes, questions, we like those and then some of the hate mail too, we love it all because we learn tons, and just, keep it coming off this conversation. We look forward to more, and we look forward to seeing you next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

Five Things to Know About the New Khan TED Institute

Greg Toppo — Tue, 14 Apr 2026 13:01:00 +0000

Three well-known but very different names in nonprofit education say they’re coming together Tuesday to launch an improbable enterprise: a new, AI-focused college, designed for a world in which artificial intelligence is reshaping what employers want. It promises a bachelor’s degree in applied AI, delivered almost entirely online in as little as two years — for less than the price of a used Toyota Corolla.

Applications are expected to open in 2027 for the Khan TED Institute, a joint project of Khan Academy, TED — the purveyors of the popular TED Talks — and the Educational Testing Service.

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“I think there’s always been, frankly, some need for a program like this,” said Khan Academy founder Sal Khan. Many people, he said, can’t afford a college degree or can’t take the time out of their work lives to attend four years of classes. “It could be that they have pursued a degree, but it’s not giving the signal that would give them the opportunities that they would want.”

Another founder, Amit Sevak, who leads ETS, acknowledged that they are still working out many of the details, but that the new institution could someday enroll “tens of thousands” of students, rivaling flagship state universities. Sevak said he’s “100%” anticipating that its instructors will be humans, most likely a large network of adjuncts.

“We still believe in the value of a human teacher,” he said. “We think that there’s so much socialization and collaboration that takes place [in the classroom]. There’s also the classic need for classroom management and some pedagogical oversight over the assessments.”

Here are five things you need to know about the new enterprise:

1. It’ll offer a bachelor’s degree in applied AI in various fields such as business, marketing, human resources, healthcare and more.��

The college will offer a full undergraduate bachelor’s degree organized around three pillars: core academic knowledge — math, statistics, economics, computer science, science, history and writing — applied AI skills and “durable” human skills such as communication, leadership, collaboration, peer tutoring and public speaking.

Early employer partners include Microsoft, Google and , an AI app development site.

2. It’s expected to be competency-based, cost less than $10,000 and take as little as half the time of a traditional bachelor’s degree.

The college’s founding partners say its total cost will likely be under $10,000, a fraction of the of a four-year degree.

Amit Sevak

Rather than requiring four years of seat time, Sevak said, the institute is built around a competency-based model, offering students the opportunity to advance when they demonstrate mastery. That means students could potentially complete the degree in two to three years, he said, depending on how quickly they demonstrate required competencies.

That opens it up to many different kinds of students, he said, including motivated high schoolers who want to earn undergraduate credits quickly before graduation, working adults seeking advancement in their jobs and students already enrolled in traditional colleges who want to stack an AI credential on top of their existing undergraduate credits.

Khan said the new college “is something I’ve thought about doing in some way, shape or form, for many years, and the changes within the job market, because of AI, only accelerated that.”

He said the idea came out of conversations with TED chairman about a year and a half ago. “We started saying, ‘It feels like there’s something powerful between Khan Academy and TED. We’re both learning organizations. Khan Academy is known for academic learning from K-through-14. TED is known as [embodying] lifelong learning. And it’s about human connection. And it feels like we both have fairly unique brands in the not-for-profit space and the education space.’”

Khan later spoke at an ETS trustees dinner and got to know Sevak.

“They’ve been looking at the same things,” he said, “and they’ve also come up with a framework on durable skills and thinking about ways to assess them. And we realized, ‘Look, the world needs this. And if the three of us come together, this will be very credible and hopefully has a high chance of helping a lot of people.’”

3. It’s an “AI-first” institution, weaving artificial intelligence into how courses are designed, taught and assessed.

Sivak said courses will be shaped by AI and teaching will be supported by AI agents, software systems that can tutor students, answer questions and provide feedback. And students will be prepared for work in “AI-native” environments.

Instruction will likely be 100% online at the college’s launch, with an emphasis on asynchronous coursework to accommodate students in different time zones and life circumstances. Over time, Sevak said, they’ll likely explore a hybrid format.

4. Khan Academy will provide the college’s learning platform and pedagogical infrastructure, despite its founder’s tempered enthusiasm about AI and learning.

TED, the conference organization best known for its short, , will incorporate its content into the curriculum, giving students access to live talks, Q&A sessions and community-based learning with TED speakers.

And ETS, the testing and measurement organization that produces the GRE and TOEFL tests, will contribute its assessment expertise, said Sevak.

Khan Academy, the popular free tutoring website, which has about and operates its own , will offer its technology to deliver the college’s coursework, organizers said. Khan, who founded it in 2008, will hold the title of “TED Vision Steward” in the new partnership.

Sal Khan

The announcement comes just a few days after Khan told Chalkbeat that the learning revolution he predicted in 2023, upon Khanmigo’s release, .

In September 2022, Khan and Kristen DiCerbo, the organization’s chief learning officer, were among the first people outside of Open AI to get access to GPT-4, the large language model that at the time powered ChatGPT. Their experiments gave rise to a revolution in Khan’s thinking: In 2023, he delivered a TED Talk in which he predicted “the biggest positive transformation that education has ever seen,” saying we’d soon be able to give “every student on the planet an artificially intelligent but amazing personal tutor.”

In 2024, Khan’s book, , bore the subtitle “How AI Will Revolutionize Education.”

But more than three years after Khanmigo’s launch, Khan admitted, “For a lot of students, it was a non-event. They just didn’t use it much.”

A few students, he said, have used the AI chatbot readily, while others haven’t. AI tutoring, he concluded, doesn’t necessarily motivate students to learn or fill in knowledge gaps they need to learn more. He’s still optimistic about AI in education, but also sees its limits. ”I just view it as part of the solution,” he said. “I don’t view it as the end-all and be-all.”

On Monday, Khan told �� that AI is “just going to be part of our arsenal to help make more engaging tools. Maybe we’ll be able to give more rich assessment practice. Instead of having multiple-choice questions, you can start to have ‘explain your thinking’ [questions]. So it starts to open up the aperture.”

5. It’s very much a work in progress.

Speaking four days before the launch, Sevak admitted that nearly everything about the venture “is still evolving,” and that the team is “workshopping the pedagogical design” of the new college.

Sevak said the institute is in talks with regional and national organizations that can offer “the highest form of accreditation,” a step that would set it apart from a growing number of online certificates, micro-credentials and boot camps.

“We’re really in the early days, and it’s just going to take some time for us to adapt,” he said.

The college’s curriculum isn’t yet finalized and applications are 12 to 18 months away. Likewise, the specific structure of its hybrid and asynchronous models, its faculty roster and the full range of majors are all still in development.

“Our intention is, over time, to have a whole range of specializations,” said Sevak. But the program’s core is designed to prepare students “to be really AI-centric” for a new reality. “We’re seeing [AI] as ripping through the economy,” creating a lot of uncertainty for young people.

More to the point, said Khan, “Work is changing very fast. AI is changing everything.”

Gen Z Increasingly Skeptical of —��and Angry About —��Artificial Intelligence

Jo Napolitano — Thu, 09 Apr 2026 04:01:00 +0000

While some might envision Gen Z welcoming artificial intelligence into their lives, a new Gallup survey finds people between the ages of 14 and 29 are becoming increasingly skeptical of — and downright mad at — AI.

Compared to a , they’re less excited and hopeful about the change it could bring and more angry at its existence, citing concerns about AI’s impact on their cognitive abilities and professional opportunities.

Respondents said they used AI at nearly the same rate they did before — they reported only a slight increase in daily and weekly exposure — but when asked how it makes them feel, the answers revealed growing misgivings.

Thirty-one percent said it made them angry, up 9 percentage points from 2025. And just 22% said it made them feel excited, down 14 percentage points from last year. Only 18% of respondents said it made them feel hopeful, marking a nine-point drop. Forty-two percent said it made them feel anxious, roughly the same as last year.

Zach Hrynowski, senior education researcher at Gallup, said the switch was swift.

“One of my working theories is that (it’s) the high schoolers, who are in their senior year, or especially those college students, who are maybe thinking, ‘AI is taking my job. I just went to college for four years: I spent all this money and now it’s turning my industry upside down,” he said.

Only 46% of respondents believed AI would help them learn faster, down from 53% the prior year, Gallup found. Fifty-six percent of respondents said it would help them to expedite their work compared to 66% last year.

Hrynowski notes, too, that users’ unease wasn’t entirely tied to the amount of time they spend engaging with AI.

“Year over year, among that super user group, they’re much less excited, they are much less hopeful — and they are more angry,” he said. “So this is not a case of some people who are adopting it and loving it and some people who are just avoiding it and feel negatively about it.”

Nearly half of respondents said the risk of the technology outweighs the benefits in the workforce. Just 37% believed it would help them find accurate information, down from 43% the prior year and only 31% believed it would help them come up with new ideas compared to 42% in 2025.

The survey also notes some disparities by age and race. For example, older Gen Zers are more likely than younger ones to voice concerns about AI’s impact on learning in general.

Asked how likely is it that AI designed to mainly complete tasks faster will make learning more difficult in the future, 74% of K-12 respondents said it was “very likely” or “somewhat likely” compared to 83% of Gen Z adults who said the same. Men and Black respondents were also less concerned about learning impact than their peers overall.

Results are based on a survey of 1,572 people spread throughout every state and Washington, D.C., conducted between Feb. 24 and March 4, 2026. It was commissioned by the Walton Family Foundation and , Global Silicon Valley. Together, Walton Family Foundation and Gallup are conducting ongoing research into Gen Z’s attitudes toward AI.

Hrynowski believes there might be a link between recent revelations about the harmful nature of social media and AI-related distrust: Many of the respondents came of age, he notes, just as former surgeon general Vivek H. Murthy called for a about its use.

shapes the user experience in social media. Just last month, a California jury found social media company Meta — owner of Facebook, Instagram, WhatsApp, Messenger and Threads — and YouTube injured a young woman’s mental health by design in that could encourage untold others.

This was the second of two critical decisions: Just a day earlier, a New Mexico jury found Meta — and hid what it knew about child sexual exploitation on its platforms.

“I’ve always been very impressed from the start of this work with Gen Z that across the board, not just with AI, they are keenly aware of the risks of technology, whether it’s social media, whether it’s AI or screen time,” Hrynowski said.

They are not the only generation to harbor these worries. A growing number of parents of K-12 students are pushing back on their screen time, not just , but

Despite respondents’ skepticism about AI, they’re also readily aware that the technology won’t be walked back: 52% acknowledge that they will need to know how to use AI if they go to college or take classes after high school, while 48% think they will need to know how to use AI in the workplace.

An earlier Gallup study, released just last week, shows 42% of bachelor’s degree students have reconsidered their major because of AI.

Gen Z, in its reluctant acceptance of the technology, wants help in how to navigate it, both in an academic setting and in the workplace. Schools are stepping up, the survey revealed: The share of K-12 students who say their school has AI rules moved from 51% in 2025 to 74% this year.��

Disclosure: Walton Family Foundation provides financial support to ��.

Behind the Reinvention of Summit Public Schools With AI

Michael B. Horn and Diane Tavenner — Tue, 07 Apr 2026 14:30:00 +0000

In the latest episode exploring new school models powered by artificial intelligence, Summit Public Schools’ Cady Ching and Dan Effland join Michael Horn and Diane Tavenner to discuss Summit’s transformation into an AI-native school model. The conversation examines how clarity around school outcomes and model design enables the effective integration of new technology, followed by insights into the evolution of Summit’s expeditions. Ching and Effland emphasize the importance of a holistic, purposeful education, as well as the need for a robust technology infrastructure to scale innovation.

Listen to the episode below. A full transcript follows.

Cady Ching: I think what has been really helpful for me is to list the ways that a model is not. It’s not a curriculum, it’s not an LMS, it’s not a schedule by itself, it’s not a set of beliefs or a graduate profile by itself. Those are parts of a model, but a lot of the building that we’re seeing right now is focused on building for parts versus building for an actual whole model. And so the AI-native model is how all of those model elements are working together. And it is not going to be replacing a school model. It’s going to expose whether or not you actually have a model. And I think AI is forcing a lot of school systems right now to get really honest, because if you don’t know what students are supposed to be learning and you’re not sure how they’re showing that or what adults are responsible for, AI just layers on complexity and, quite honestly, chaos. But if you do have the level of clarity of what Dan is speaking about, AI is actually making systems work a lot better, or it can make systems work a lot better.

I think the jury is out on the tools that we need and how we can create the tools that we need. But AI really isn’t replacing, it’s revealing whether or not your school model actually exists.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane, it is good to see you with some excitement for today’s episode.

Diane Tavenner: Yeah, we have a real treat today. We’ve got two of my favorite educators in the world joining us for what I’m sure is going to be just a really interesting conversation.

Michael Horn: Well, and for years, as obviously I’ve learned about Summit from you, direct from you, and yet it’s been nearly 3 years, I think, since you passed the baton, if math is still a thing. And I know from afar that the team continues to be among the most innovative schools in the country and so I know that they continue to think about reinvention, and frankly, you know, what does Summit need to look like? How can it get even better? All these questions for its learners. And so I’m incredibly excited to dig in and learn about what they’re calling Summit 3.0 on today’s show. I will say it’s also interesting to have this conversation because we’re sort of in our model geek out, if you will, at the moment, right? While we’re having this conversation, we’ve had the founders of Alpha School, Flourish on, both of which are designed as AI-native models. And for those who listened to those episodes we sort of created a little bit of a side-by-side, if you will, where we said, hey, Summit is here as this baseline for a pre-AI model trying to do personalization or optimization of each kid’s learning. And we explored what can you do in an AI-native world? How can you design differently? But today what’s exciting, I think, is we’re going to get to dig into what does it look like for an existing model with that orientation to become, quote unquote, AI-native.

And as you know, transformation and how organizations reinvent themselves, that’s something I get really passionate about and excited. So I cannot wait to learn from the real-life example in progress.

Diane Tavenner: Well, we’ve got the two perfect people for that conversation, Michael. And so let me introduce you to Cady Ching, who is the CEO of Summit Public Schools, where she was an extraordinary teacher and school and network leader for a decade before taking on that role. So she brings this full spectrum of experience to this next phase. And Dan Effland, who is the senior director of innovation at Summit, where he was also an extraordinary teacher and school leader before taking on this new role of leading for the second time in the history of Summit, the reinvention of the model. And so welcome, Dan and Cady. We’re so happy that you’re here with us and excited to talk to you about the work you’re doing.

Cady Ching: Thank you. Thank you so much. I’m excited too. It’s coming at this moment for Dan and I where we’ve been trying on a lot of language about where we’ve been, where we are today, and where we’re going. So selfishly, this is a milestone for us.

Michael Horn: Well, and I get to feel like I’m jumping in on a team huddle of y’all. Yeah, this will, this will, this will be fun.

Cady Ching: Welcome, Michael.

Michael Horn: Thank you.

What Is a School?

Diane Tavenner: Dan and Cady, a few weeks ago we got together and you walked me through the thinking and planning you’re doing. And honestly, I was captivated, you know, because I got stuck on it and I wanted to dissect every word. By this simplest definition of school, it’s honestly the simplest definition I’ve ever read of a school. And I wanted to start there today because I really think we always have talked about getting to the simplicity on the other side of complexity. And I think you’ve done it with this definition, and I think it’s going to be really powerful in this next chapter. And so maybe, Dan, kick us off. And if you will share that definition and a little bit about how it came to you or how you all came to it in your process and what you think it unlocks.

Dan Effland: Yeah, happy to. And thanks for having me here. I’m so excited to talk to you all. Yeah, so, I mean, we’ve been working on this for years, right? What is simplicity on the other side of complexity? And I think as we’ve been digging into what does redesigning look like, it became really clear that you have to get down to some foundational elements to avoid designing within conventions and not even really realizing you’re doing it. And so the way we’re thinking about schools is simply, it’s a group of young people. It’s a set of outcomes or competencies. And then it’s a set of resources that help you support young people to achieve those outcomes or competencies. That’s it.

Kids, outcomes, resources. And stripping all the way back to that has allowed us then to engage with our community, because all this work is like with students, caregivers, and educators, and go like, OK, what do we really want? What do schools really need to be? With full freedom, we call them dreaming sessions, where we can really engage off the simplest foundational elements and not get hooked by any of the conventions that have existed, you know, for decades or longer than that in a lot of cases.

Summit 2.0: Evolution and Vision

Michael Horn: It’s really cool because you’ve sort of, like you said, you sort of have a conversation around what those end posts, and we can sort of figure out what’s inside the box to get there apart from what’s always been there. But before we go to that sort of Summit 3.0 vision and where you’re thinking currently is, because I’m imagining you’re going to have lots of trade-offs and changes as you go through the design process, but I think it would be helpful to do a quick turn on Summit 2.0. Both to ground, frankly, our audience, but also to set up a question of how things are changing and where and so forth so that we can understand that. And so I’d love, and maybe Cady, you dive in on this first, how would you describe the Summit 2.0 model, which was not only in your schools, but schools across the country? It’s one of the reasons I think it can be called a model, it’s scaled beyond Summit itself, right? And as you think about that, the new model, what is it in the Summit 2.0 that you’d say, we really want to hold on to this? Or where are the things that you’re saying, hey, actually, that’s something we can leave behind or start to question whether we want to change that?

Cady Ching: Yeah, thanks for asking this question. I think it’s so important. The reason why I keep smiling when you all say Summit 2.0 and 3.0 is because Dan and I actually got into it a couple weeks ago about if we wanted to use that language or not. And my issue with it was I think it’s really, it serves a purpose because like to Diane’s point, it is simplicity at the other end of complexity. And there is a danger in the simplification of the 2.0 and 3.0 because at Summit, we really think about innovation in two ways. One just being innovation through refinement, which is the day-to-day tightening of the model elements that we’re building on for these larger moments of innovation, which we call innovation for redesign. And so those are sort of the sector-shifting, big model, what we call Big M changes. But I’m going to use Summit 2.0 and 3.0 language today in shorthand.

Michael Horn: Thanks for doing it for the listeners.

Cady Ching: Yeah, and so Summit 2.0 really speaks to our personalization era at Summit, where we showed personalization doesn’t need to be a luxury. And we did that by designing cohesive student and teacher experience., and it included model elements like mentoring and skills assessment and differentiation using real-time data, which we enabled through tech. And the tech that we co-built was called the Summit Learning Platform. For me, what I think was most remarkable about what we proved in Summit 2.0 is what you mentioned. It was scalable, and it did scale, and schools were able to implement and sustain the Summit model on public dollars. Which was remarkable. And so we reached 100,000 students, 6,000 educators, and 400 schools across 40 states.

And we did it with district, charter, private, rural, suburban, and urban. It was completely shifting the field. And then we normalized mastery-based learning, personalized playlists and skills and habits in a way that now is the foundation and the baseline in so many places that we’re now talking about building these AI-native models on top of. And so to the second part of your question, which I’ll kick off and then, Dan, I’m going to pass it to you to add on, we think about model elements and processes that we want to carry forward into Summit 3.0. In the process side, which is where I thrive, we were successful because we were leading from this intersection of the learning science, community engagement, and technology, and we centered teachers and students at every part of the design.. And we’ve used those same design principles to continuously improve our model since Summit 2.0. For me, I feel like we’re 4 years into Summit 3.0, and we’ve already gotten some really exciting data back about situating us as leaders in the field again around what we’ve built on top of the personalization.

In last year, this is our most recent data, we saw that our Summit alumni have some of the highest post-graduation incomes and lowest debt loads, as compared to other top-performing charters. And this is the type of longitudinal outcome evidence we’ve been really longing for. And when you think back about how Dan just defined the system, what that data does for us is it grounds us in that we do have a really strong set of outcomes and competencies that are timeless. Our young people are now achieving them, and we’re letting go of the old technology to create space for AI-reimagined infrastructure that’s going to help us to better allocate resources. And we think our biggest resource levers are people, technology, and time. So that’s really how we’re thinking about Summit 2.0 setting us up for Summit 3.0.

Michael Horn: Dan, did you want to jump in there and add some?

Dan Effland: Yeah, yeah, I think I’ll just like, you know, I think, you know, Cady and I were both teachers in Summit 2.0. We were both school leaders in this, and so we have a lot of really direct connection to it. And the thing that really makes me think about it is like, you know, the learning platform is no longer in existence, but the elements of the model really deeply took root. Mentoring, mastery, what we called habits of success, I think we’re calling durable skills in our world now. Like, I’m fine with it, whatever we want to call it. It’s become ubiquitous. And I think it really helps. I mean, I think it really gives us a sense of a strong foundation of like, we’ve done this before, we’ve built a model that’s scaled and really stuck.

And it doesn’t matter if the technology, you know, is stuck or not, because that technology is not the model. The tech model is these elements of how you support kids to master these outcomes with whatever available resources you have are. And so, yeah, I think there’s a point of pride when we think about, you know, what we’re begrudgingly calling Summit 2.0. And then I think there’s a sense of the strength of the foundation to then build what’s coming next.

Personalization & Durable Skills

Michael Horn: It’s interesting. And we’ll come back to the technology, I know, and we want to circle back to that. But hearing Cady, you described the model, used a few words that I think are really important for people to hear. One of them was cohesive, because I think a lot of the tech efforts right now around personalization in so much of the country are the opposite of cohesive. And that’s why we’re seeing a blowback sometimes against technology, because it’s sort of all over the place and hundreds of things going on at once for a young person with tons of distractions. And you talked about it being grounded in the learning sciences and personalization as a, as a means, not the ends, right? And, and then you have these longitudinal outcomes. And I’m just calling them out because I think people often lose sight of, this is the bedrock, right, of how we build from, and then go from there. And the other piece, and Dan, you just referenced this, the field is now calling it durable skills.

I still prefer habits of success. Let me just be on record on that one. But one of the things you all really did well around Summit 2.0 was have incredible clarity on the mission, what success looks like, such that you could measure in the way you just said, Cady. And I didn’t know those stats. I mean, it’s fascinating., and then you had these commencement-level outcomes, right? You were super clear on what does it look like from a, you know, for a Summit graduate as they go out in the wild. And it seems in some ways those commencement-level outcomes have been precursors to the movement across states that we’ve seen in the Portraits of a Graduate. And I do think that there’s some key differences. I’ll hold my editorial back on what those are more because I want your take on that.

Like, what, if anything, are the differences and, and between those commencement-level outcomes that you all have defined, the portraits of a graduate that we see states doing, and more broadly, like, what’s the importance of being super clear on what those outcomes are and, and how you’d know, on the other side, if you could speak to that. And I don’t know, I’ll make it a grab bag of which one of you wants to jump in on that.

Dan Effland: Dan, take it away. Awesome. Yeah, I mean, so our vision has been the same for 23 years. It’s preparing young people for a fulfilled life, really all people. We think of our staff as part of that too. And fulfilled life is in some ways, again, simple. It is purposeful work, financial independence, strong community, strong relationships, and health. And so that’s given us a holistic picture, a holistic point B that we’re always going for.

You know, I don’t, I don’t know how I compare it to Portrait of a Graduate or Portrait of a Learner. What I know is it gives us a lot of clarity in that you can’t design a coherent model without clarity of where you’re headed. And that it’s also really important that that clarity is holistic and is not simply a set of academic outcomes. It is much broader than that. And that gives us a huge advantage in this work right now because we’re not spending a lot of time. We certainly talk to our community and affirm, you know, on a regular basis, is this still what people want? Is this still what our communities are after? And it is. And so we can move right to like, okay, how do we get there?

Cady Ching: The thing that I would add on top of that is, I loved, Michael, what you called out around the language of a model. I think that at the operator level, and when I’m talking to, to other school leaders, this word is used in a lot of different ways. And I think what has been really helpful for me is to list the ways that a model is not. It’s not a curriculum. It’s not an LMS. It’s not a schedule by itself. It’s not a set of beliefs or a graduate profile by itself. Those are parts of a model.

But a lot of the building that we’re seeing right now is focused on building for parts versus building for an actual whole model. And so the AI-native model is how all of those model elements are working together, and it is not going to be replacing a school model, it’s going to expose whether or not you actually have a model. And it’s, I think AI is forcing a lot of school systems right now to get really honest, because if you don’t know what students are supposed to be learning, and you’re not sure how they’re showing that, or what adults are responsible for, AI just layers on complexity and quite honestly, chaos. But if you do have the level of clarity of what Dan is speaking about, AI is actually making systems work a lot better, or it can make systems work a lot better. I think the jury is out on the tools that we need and how we can create the tools that we need, um, but AI really isn’t replacing, it’s revealing whether or not your school model actually exists.

Diane Tavenner: I’d love it if we go back to your simple definition, Dan, that we started with, when we sat down. You use the word package of outcomes, and I was obsessed with that word package for this reason, because you know, maybe I will jump in here a little bit on the portrait of a graduate.

Michael Horn: The table’s been set for you, Diane.

Diane Tavenner: Yeah. And one of our, you know, Summit’s longtime beloved board chair, board member, who honestly is one of the most forward-thinking, I think, philanthropists who launched a scholarship for Summit graduates going into Pathways years ago, like ahead of the curve, you know, sent us a note the other day with a real critique of portraits of a graduate. He was sort of reading about them and was just very, you know, like, what are these people thinking? And I think what he was responding to was a lot of the portraits of the graduate, like, feel very checkboxy and compliance-oriented. Versus this sort of holistic. And I know that’s not the way they were intended.

AI Evolution in Education Models

Diane Tavenner: They all have good intentions behind them, but the way they have been sort of brought to life and then communicated and then implemented are what Cady, I think, is speaking to, not as a model, but as like these individual components that don’t have a coherence about how they’re actually organized an organized set of resources to achieve those package of outcomes, if you will. And so I think that what you all just described is at the core of your success going forward and what an advantage you have. And it really speaks honestly to the durability that you’re carrying all of that forward in this next phase, that being, living a life of wellbeing it actually hasn’t changed, right? The elements of that haven’t changed, and that’s what you’re equipping young people for. So, you know, in a recent episode, Michael and I had a conversation, just the two of us, which was super fun, and we were dissecting a way of thinking about school models in three buckets. And I know you are both familiar with this framework, which is essentially that, you know, Model 1 will use AI to make sort of the existing industrial model school more efficient and better. Model 2 will stretch the bounds of that industrial model school with integrated AI. And Model 3 will be AI native, you know, essentially built from the ground up with AI capabilities that are assumed to be at the core. And, you know, as you think about where you’re now going with Summit 3.0, how do you view it in the context of this framework? And, you know, what does AI make possible that wasn’t possible in 2.0 because it was designed pre-AI?

Dan Effland: Love this question. And I did listen to that episode. So I’ll start with the model part, and then I really want to get into what AI makes possible and kind of what it pushes us to do. So I love reading like Learner Studios’ 3 Horizons model. I love Bob Hughes’ paper on the 3 models. I find that stuff really, really important for evaluating what exists and really valuable for visioning and for getting into this place of what really is possible. And I think, and that’s really useful. I will say, when we start designing and working with our young people and working with our caregivers and our educators, I actually find it useful to kind of set those categories aside and to ask the more foundational questions around, like, we know where we want to go, we have this clear vision, we have this really simple, you know, conception of what a school is with kids’ outcomes and resources.

And now let’s go from here. And when you get into, like, as we’ve talked about, we have a lot of clarity about our outcomes already. We really believe deeply that this holistic model of a healthy, thriving, you know, young person, young adult, adult is going to be durable regardless of the transitions that are happening in our society. But when it comes to the resources part, now we have this whole huge different potential, one, AI being a resource, but also a way that I think we’re most really interested when it comes to AI is how we can use it if we integrate it into our tech stack. Really how, like, with a really robust knowledge graph and really strong data layer, you could be dynamically reallocating resources in a way that just would be impossible for people. You know, like when I used to build an annual schedule, like the primary schedule with our Dean of Operations, she and I would sit in an office for a week with a spreadsheet to make a schedule for the year that never changed, right? Like, it’s just so labor-intensive. But now I think when we think about AI as part of our infrastructure, and it’s kind of a layer in our tech stack interacting with a really robust knowledge graph and data layer, we can start to ask ourselves, like, how do we get the right resources to the right kids at the right time for the right outcome? And really get very, very precise, and also do that dynamically. And I think that then allows us to think about personalization, just-in-time instruction, integrating real-world experiences, ensuring that personalized learning still happens in community and there’s deep human connection that is part of personalized learning journey in a way that was, was not possible when, you know, 12 years ago when we were thinking about Summit 2.0, the technology just didn’t exist.

And so, I mean, it’s exciting. I mean, I really think there’s incredible possibility there. And while there’s definitely lots of really cool tools being built, we’re much more focused on the, like, where does this fit as part of our technology infrastructure or our tech stack, because we think that’s, like, potentially a huge lever for transforming learning for young people.

Current Applications of AI in Schools

Michael Horn: It’s fascinating to me, ’cause you just named a number of things that AI could do that I had never thought about in terms of, like, dynamically changing the schedule for, you know, the school and students and, like, there’s some pretty cool things you can start to imagine that ripple out of that. One of the things in that conversation that Diane referenced that she and I agreed to hold ourselves accountable for was to get really specific when we talk to school leaders about, so what’s happening today in your schools that’s actually leveraging AI or is quote, unquote AI native, if you will? And so you all are obviously still in the design phase for 3.0. I use that with trepidation now, but put that aside for a second. Like, today, if I were to, you know, get to be in California again and I was hanging out in your schools, what would I see that’s powered today by something that’s AI native? What is it? What are the tools? What does it look like? What does it do? What are you building versus partnering with? Give, give us a sense of some concrete applications. Anywhere in the tech stack or during the day, that is AI-powered?

Cady Ching: I think this would be a good opportunity to talk about a specific tool that we’re using, which maybe not ironically is Futre as one model example of what it can look like. And Dan can speak to specifically what it’s looking like in the student and teacher experience. But one of the reasons why I start with speaking about a specific tool is because I think that largely edtech has not— has been really unsuccessful in solving for what we need to operationalize innovative school models. And Futre has been a nice shift of pace for us because it is truly a tool that is building for the child versus fitting a child into a tool or larger system. And I think that the way in which we’re using it with our young people can work in many H2 and H3 model contexts because it’s able to give us real-time data about our young people and then allowing us to build their student experience based on the data that we have about them. Dan, can you introduce, Michael a little bit more to Futre and how we’re using it at Summit?

Dan Effland: Yeah, absolutely. So Futre right now we’re using with our juniors and seniors, although we anticipate starting younger, in the coming year. And right now, our juniors are really using it to do a lot of career exploration, which the tool excels at, and really like exploring very deeply different possibilities. And then what those possibilities mean as far as what they need to be working on now or experiences they have between kind of their current point A and their future point B. And then our seniors are using it to get more concrete about what really, what is my next step? What does that mean? What is the thing I’m doing immediately after high school? — I think we deeply believe this and will proudly say it is best-in-class career-connected learning. It is. Absolutely. It is the thing when we do — when I do focus groups, when we do alumni data, kind of research, it just comes up over and over again because our young people actually get out in the community or within the school building and really doing what we now are calling real-world experiences. We’ve called them lots of different things over the decades, but we are — one of the things about that though is that kind of like we were talking about, how do we really curate the journey with this resource allocation stuff? Just tracking all of those different experiences, often there’s 50 or 60 choices for students at one school when we had those expedition cycles. We’re now pulling those experiences onto the Futre platform so we can really start to map what students have been doing, what they haven’t been doing, maybe what they should be doing. And then their mentor can take an even more engaged kind of role in coaching them through that pathway. We’re really excited about that.

We’re kind of just starting, you know, to pull those on. But I think in the future it’s one of the things that we see that the Futre tool will be really, really helpful with because, you know, young people need coaching as they’re figuring out that concrete next step.

Michael Horn: So super interesting. I actually have two questions, but let me go to you, Dan and Cady, first. And then I have a question for you, Diane. I’m going to put you on the hot seat. But I think we’re allowed to do that. But it’s interesting. You just said something there in your answer, Dan, which was then the mentor or coaching.

And so just like to put a fine point on it, The, like, this works really well because you have a model where there is that function that is meeting on a regular weekly basis, right? And like, so therefore that touchpoint, like it’s coherent again to use that word, but I, I would love a quick update on how Expeditions has evolved because when I think when Diane was exiting Summit, like, y’all were in the middle of redesigning it and I’ll be super honest, like even though she and I talk basically weekly, I don’t actually know the new version of Expeditions. And so, I still have a slide in my talk about Summit that says, you know, like every 8 weeks or whatever, you go off for 2 weeks. And y’all should update us on what’s the current state of Expeditions at Summit.

Cady Ching: Yeah, I’ll respond to 2 pieces. One, with the mentoring piece, that model element does exist. One of the reasons why I personally love Futre is because it takes some of the lift of mentors needing to be the vessel of all career pathways off the human. So when we think about that resource allocation of, you know, people, talent, it’s creating a better, more coherent system for the adult as well, which has been so important because we love to center our teachers as well in the design. And then the Expeditions redesign, it’s been really cool. We’ve been, you know, continuously shifting that program based on what our alumni are sharing back with us, based on how the world is shifting. And of course, AI, as so much a part of our students’ experience today and in the future, has shifted it again. It is non-graded— so this is actually surprisingly one of the most controversial things when we rolled it out to parents— they are not receiving grades on the different career exposure pieces that they try out as they’re with us at either the high school levels or as early as 6th grade in Seattle.

And it’s really about ensuring our students get about 9 career exposures between the time they start with us to the moment they leave, because we know it’s really important for them as they develop their identity to see themselves in different career pathways that are all mapping towards high opportunity where they can build their generational wealth for their family. So it’s probably pretty similar in terms of the time allocation. They’re in sort of what we call their core classes for 6 weeks, and then they’re pausing for 2 weeks to go out, usually in the upper grades, off campus. You don’t see — when people come to observe this on our site, they’re not actually a lot of kids in the building because learning happens without walls. Dan, what else would you add as you’re going? Dan is quite literally on an expedition tour currently. He’s at one of our school sites right now, and right after this recording, he is going to go in and speak to our teachers. So what else would you add?

Dan Effland: Yeah, I mean, I think that’s an important side of it is so that, I mean, one, it’s just, I was still in a school leadership position when we transitioned to this kind of redesigned Expeditions, and I just can’t tell you how powerful the experiences are. I can think of so many stories, so many young people, but like one in particular that a young, he’s — well, he’s probably not even that young now, but he’s 25, but he was a young, young man at the time who was really, really struggling. And this kid was having discipline issues, attendance issues, struggling, like, not necessarily living at home on a regular basis. And we really, we thought we were gonna really lose this kid. And he started doing an expedition experience related to culinary arts. After he did that first one, he did a second one, and then there was kind of a sequence of them where he had, you know, like the first one was kind of like a survey course. It was the community college. It was about 25 kids.

Finding Passion and Purpose

Dan Effland: Then he was able to do one where he was actually kind of shadowing one of the actual culinary arts program college students and learning in a second wave. So I’m having a hard time not using his name, but I’m going to keep it out. But I just loved this kid. And he found his pathway. And not only did he find his pathway and ended up going to a culinary arts program and graduating and now works, you know, like in the culinary arts, you know, scene in Seattle, his attendance improved, his grades went up, his connections with his mentor, with his teachers, with his peers, which were, you know, fraught, got better and better. And he became a healthier human because purpose and passion and having a pathway is essential for all of us. And we’re at a time when, you know, you can read about this everywhere, there’s studies, our young people are really searching for that clarity about purpose and pathway. And when you see it, I mean, it’s just like Cady said, it’s kind of hard, like it’s not a good thing to tour because the kids are mostly out in the community.

Dan Effland: But when you have the privilege of being a school leader and you see these kids over the years and they do their cycles, you just, the impact is unbelievable. So yeah, I just wanted to, yeah —

Designing Education for the Child

Michael Horn: No, the anecdotes make these things always so much more powerful. And I mean, you can, through your story, hear him building a positive identity of himself, right? And that’s incredible. Diane, something Cady said made me think of it, which is obviously, you know, folks who listen to us know that you’re the entrepreneur behind Futre. I now understand why it was originally called Point B based on Dan’s language and I guess, but she said something interesting, which was like a lot of edtech has not helped the launch of new model design, right? Because it’s been, and that, that’s sort of been obvious to me for why, right? Because the market is schools as they are, and venture capital wants big markets, and right, like, it’s — so it’s, it’s this sort of reductivist thing that happens. But she said you’ve been designing for the child, and so you’ve been able to escape that and I wondered if you just might want to reflect on that, because I imagine it is still hard though, um, because you’re still like — schools are the conduit to the kids. So just sort of like, what’s the advice, or what have you learned, right, through, through navigating that?

Diane Tavenner: Well, I think that I mean, so much of what Dan and Cady have just said is so important. And I think that what, what was one key thing is, you know, I sort of set out to build Futre as an edtech partner that did things differently than what I experienced when I was sitting in, you know, the seat that Dan and Cady are in. And you know, that core value of our company is how we do the work is as important as the work that we do. And so how we do the work is very much co-building with schools and leaders and students. And so, you know, we are out in the field working with students and teachers and people like Dan and Cady literally every other week. So we are literally co-designing and code building what happens. And so what you just heard, that Futre is being designed to help young people build this identity over a 10-year journey. I mean, that’s unheard of, I think, in any sort of tech market.

People don’t think about that. We have real outcomes that people are aiming towards, and most tech products just look at what’s something that exists and try to make it more efficient or slightly better. They don’t think about the integration of it, the flexibility of it, how it will be used by the adults. I mean, As an example, they just told you Futre can be used both in individual coaching, mentoring, advising, counseling. It can also be used with groups of students in a classroom, and it’s actually literally designed to support both of those. And I will say the, the inclusion of really supporting real-world experiences came directly from our engagement with our school partners and our students. That emerged as this real need And we were watching people literally running around schools with laptops on their arm and all these spreadsheets and trying to organize. And so we have co-built these elements together.

But you’re right, the incentives in the business side of things are not to build this way. And so, you know, like always, we’re going to see if we can prove that wrong and say, no, when you do build this way, you not only get better outcomes for young people, schools and teachers and educators, but you also can be a successful, scalable product.

Michael Horn: So certainly a more enduring product if you, if you thread that needle, right? So for sure.

Cady Ching: Yeah, exactly. So I think it’s I think it also speaks to why it’s so important for Dan and I to sort of pull together a coalition of the willing with other operators. One thing we haven’t spent — I know we’re almost at time — that much time talking about is how hard this work is. It is challenging, and we have so much to learn. We are not perfect. We are learning every single day. We are constantly seeking out other school systems that have similar visions for education, and we’re trying to learn from them. We’re trying to get out onto their campuses and be in community with them because we know that if we want to build something that’s enduring and lasting and maximizing impact on the number of students in our country, or even globally, we have to build for the students of Summit as well as all students.

And I think that, that’s what’s most important for me as I set out to lead some of this work is if it only works at Summit, it’s not good enough. And what we’ve learned about leading change at scale is that we need a shared purpose for what school is actually for, and that belief that it’s possible to build a system for that purpose, which is actually no small feat. And it’s why we’re spending so much time building what I would call a coalition of the willing, which is educators and systems who agree on our common destination before we start building the actual tools. I think my core idea is that beliefs come first, model comes next, and then the tools come last. And when we get that order right, that’s when the scale can become possible.

Summit Learning: Model vs. Technology

Diane Tavenner: Cady, I want to double-click on what you’re saying because, you know, you talked at the top of this about how Summit Learning had really scaled across the country to 40 states and, you know, 100,000 students, etc. But Dan, you also said the technology, the Summit Learning platform was not the model. It is not the model. And the model has really taken root even as that particular piece of technology has gone away. That said, I do know that you both believe deeply that having an aligned core technology that is the infrastructure that sort of I think, Dan, you used the word guardrails, like puts up the guardrails and the support for the model is profound. And I know that you’re in conversation with other folks who’ve done some at learning who are, who it’s taken root for them as well, but are having a hard time really keeping that model intact. And so talk about sort of the need for that infrastructure, the role that it plays and what you think it might look like in 3.0. And Cady, you just said it, no one’s going to build technological infrastructure for a single school or a single school system.

And so there has to be this coalition.

Cady Ching: We have to create the market.

Diane Tavenner: Yeah. And so talk about that because the market generally is not very coherent. And as I sit on the other side, it can be really confusing and hard so talk about how you guys are thinking about that.

Enabling Learning Through AI

Dan Effland: Yeah, I think this is something we’ve started to be spending more and more of our time on as we’ve gotten clearer in the work with our students and caregivers and educators this fall. We’ve gotten clearer about where we’re going. There is this need, which is that technology is not the model, but it is, you know, there’s a reason we talk about time, talent, and technology as the big levers with resources. It is a huge enabler. And I think the possibilities with AI as part of that technology infrastructure make it an even stronger enabler. So I’ve already talked about like the idea of like dynamically reallocating resources, which is, I think, I love in a conversation educators here, because I think sometimes it’s not the, like the shiniest thing to talk about, but we know that getting kids the right thing at the right time in the right sequence is often the difference between learning and not learning, between progress and not progress, and between finding that pathway and not finding it. And so, at a high level, when we’re thinking about that infrastructure, we need to make sure that, like, we have a really rich, you know, amount of data.

And there’s a lot of work to be done there. Our school systems historically have not put data together in ways where you can create what like a technology person would call the data lake in a way where you can really access that as you need it. And then the next element is going to be a really robust knowledge graph that is not just academic standards. It’s got to be much broader than that. And then, of course, the way that AI would then interact with that to allocate and think about your resources. And I’ll share too, like when we think about resources, I generally think of everything as a resource. My time is a resource, Cady’s time is a resource, our educators’ time is a resource, curriculum is a resource, YouTube is a resource. Anything that can help a young person move towards those outcomes, we think of as a resource, and how can we constantly repackage those and get them in the right order while holding onto the vision? Because I think there’s a version of personalized learning that I would call like individualized learning.

That’s not what we’re talking about. I believe this has to happen deeply in community and with really strong relationships and human connection. And so the personalized learning, then it’s actually more complex when you’re committed to maintaining community and relationships, because you’ve got to figure out configurations of young people and not just put everybody separately on a computer they have a particular pathway and so.

Cady Ching: And that’s what we’re seeing, we’re seeing people just run, sprint towards an outcome without doing the diligence. And I think that it’s resulting in a lot of binary. If you’re either tech-forward or you’re human-centered, and there is a way to bring that together and build a model that’s doing both and that’s what we’re setting out to do.

Dan Effland: Yeah. There’s another binary too, that we haven’t talked about, but we should stamp here, which is this binary of like, real-world readiness or academic foundations. And that we now, we have these camps and like, we’re all about academics and we’re all about the real world. And when you talk to students, you talk to students and caregivers and educators, no one thinks it should be an either-or. That’s the scarcity mindset we’re often in, an area that we engage in educators. And we’re deeply committed that our young people will be prepared with college-ready academic foundations and real-world readiness, which means for us habits of success, communication, collaboration, all executive functioning. That is has a purpose

Diane Tavenner: Yeah. One is, as Dan, your story of that student showed, the sense of purpose, which is connected to what my life will look like in the future, really is what drives everything for a young person, right? It’s how they’re forming their identity as they build that vision. It’s what motivates them to stick to the hard work every single day on this journey to get where, where they’re going, and so yeah, I think what you’re up to is really critical. I hope that a lot of schools and systems engage with you to create this demand in the market for this type of infrastructure, dare we say, you know, Summit Learning Platform 3.0 as well. Because I think that it’s really, it’s hard to conceive of a post-AI model that doesn’t have that. That real infrastructure.

And I know you all haven’t seen it or found it yet, but continue to make strides in bringing it to life.

Michael Horn: This season of Class Disrupted is sponsored by Learner Studio, a nonprofit motivated by one question: what will young people need to be inspired and prepared to flourish in the age of AI as individuals, in careers and for civil thriving. Learner Studio is sponsoring this season on AI and education because in this critical moment, we need more than just hype. We need authentic conversations asking the right questions from a place of real curiosity and learning. You can learn more about Learner Studio’s mission and the innovators who inspire them at www.learnerstudio.com.

So a good place maybe, Diane, to wrap up.

Should we pivot to our before we let you off the hook section? Cady, Dan, we have a tradition here where we, where we talk about something we’ve been reading, writing, watching, listening, whatever it is, not writing, listening to, and eventually I’ll get my verbs correct. But and then, so just often we try to keep it outside work, but we often fail. So, Cady, you want to go first, and then Dan, we want to hear what’s been on your playlist or bedside table, and then Diane and I will wrap it up.

Cady Ching: Yeah, sounds great. I have been— I taught my 7-year-old what it means to brain rot. I don’t know if you’ve heard that term, but where you just sit on the couch and just kind of watch nothing for hours and hours. And we did do a Spider-Man and Avengers binge this past weekend. So that is something I have been watching a lot of. Reading is going to be hard for me to separate it from the professional. I’ve just been really deep in leader succession. I think to do this work, you need really strong talent in leadership pipeline.

And so I’ve been in HBR. I check the Marshall Memo every week to see what, what they’re pulling out, to really think about how I’m leading personally, locally, individually, but then also what the sector needs. Dan, I’ll pass it to you.

Dan Effland: Similarly, like the kind of first answer on my mind is just this fire hose of like white papers and podcasts about education and AI.

Cady Ching: And then he screenshots them and sends them to the whole team.

Dan Effland: Yeah, drive everyone nuts with them. But I do have a more, maybe a more fun one on the personal side. Kind of finally reading the Foundation series, the Isaac Asimov kind of classic sci-fi. It’s honestly about connection for me. My siblings are sci-fi readers and I’m very late to the party. And then my father is retired now, and one of his, it seems like, main activities as a retiree is to reread everything Asimov ever wrote multiple times.. And so for Christmas this year, I got a stack of these really great, Half Price Books paperbacks of all the Foundation novels, and I’m starting to work through them.

And we have a text thread about them, and they are, it’s a wonderful story, it’s very complex, and it certainly does also make me think a little bit about the future of our world and AI and, and what, you know, where, where young people fit in that, but it’s also just been a really fun way to connect to the family.

Michael Horn: That’s cool. Wow.

Diane Tavenner: What about you, Diane? Well, picking up on that. So first of all, apparently this is not going to be a novel recommendation because this Apple TV series, I guess, is the most watched at this point. But we watched Pluribus, which was created by Vince Gilligan, who — yes, Breaking Bad. Yes, Better Call Saul. I didn’t watch either of those, but I was a huge X-Files fan

Michael Horn: Back in the day.

Diane Tavenner: OK. And so there is very much some X-Files feel here in Pluribus. But to what Dan said, and I think Foundation is related, I just find this series to be so provocative in the questions that it’s bringing up and sort of the contemplation of where we’re going as a society and how the choices we’re making each day might affect that and what we actually want. And I will— I told you I would report back my goal. I did finish Ian McEwan’s novel that I pre-promoted. Yeah, yeah, yeah. But it was everything I expected and more.

It was just extraordinary. And I did both of those over the holiday. And I will tell you, I feel like I’m sort of in surround sound right now of asking these big existential questions along with everything from what’s happening in the news on a day-to-day basis to all the work in AI. So, but I would highly recommend it. Super provocative and interesting.

Michael Horn: Perfect

Diane Tavenner: Perfect. Crazy. Like, you never know what’s gonna happen next.

Michael Horn: That’s fun when you can’t predict it coming.

Diane Tavenner: Yeah.

Michael Horn: Yeah. Yeah. I was gonna say, so the brain rot theme that you brought up, Cady, I mean, we talk about it all the time with our 11-year-olds, here at home. But I was — this is not where I was going to go at all with this, but I — something one of my kids said made me think of the Animaniacs theme song, if you all remember that cartoon from back in the day, and I pulled it up and showed it, and my wife just dismissively said, this was brain rot when we were growing up. so, there you go. the one I’ll say is, we all went with another family and saw Wonder, at the American Repertory Theater. Many people may know the book, Wonder, which follows the story of Auggie Pullman, a 10-year-old who has Tretcher Collins, syndrome that presents as disfiguration of the face and sort of how going into a school environment for the first time and all the things that it does. And there’s a movie about it as well, but now there is a musical too.

And Diane, you will not be surprised, I was crying from the opening number and I kept it up through the whole thing. So it was, I was true to form. That’s a good one to cry over. It was good. I represented well, but it was fantastic. We’ll see if it makes the jump from sort of off-off-Broadway to something bigger, but until then, if you’re in the Cambridge area, definitely check it out. And for all of you, just huge thanks, Cady, Dan, for joining us, getting us to have a peek under the cover of what’s coming next at Summit and the broader — as usual, you all are thinking about the broader ecosystem as well, which I admire so much about the work you all do at Summit. It’s not just our model, but how does our model spur this greater change across education.

So huge thanks for joining us. And for all of you listening, keep the questions, comments coming. Diane and I feed off them, and we really appreciate all of you. We’ll see you next time on Class Disrupted.

Disclosure: Diane Tavenner founded Summit Public Schools and served as its CEO from 2003 to 2023.��

This episode is sponsored by LearnerStudio.

Opinion: When It Comes to Developing AI Rules, Who Asked the Students?

Ruhan Gupta — Fri, 03 Apr 2026 10:30:00 +0000

Three years ago, schools took a side.

Within weeks of ChatGPT’s release, hard rules appeared almost overnight. AI tools were banned throughout departments. Teachers watched what seemed like an existential threat materialize in real time, and they responded the way institutions usually do under pressure: They drew a line and told everyone not to cross it.

Three years later, that line is still there. And at many places, nobody ever asked whether it should be, at least not the people most affected by it.

When I looked into how my Austin, Texas, high school’s AI policy was developed, I found that my administrators made the decision internally. There was no student committee, no open forum, no campuswide survey. The rulebook was simply handed down. In K–12 education, require districts to develop and publish AI policies; when they are published, they’re often developed without proper consideration of all stakeholders, including students themselves.

It’s reasonable to counter that students are minors, that institutions need coherent governance and that not all decisions can go to a committee. But AI policy isn’t a routine curriculum adjustment. It governs what tools students are allowed to use to think, draft, research and communicate — tools that increasingly shape how knowledge is produced and evaluated outside school. Getting those rules wrong produces consequences for students.

Brittany Carr’s situation is a well-known example. In early 2023, the had three assignments flagged by an AI detector. She provided her revision history and explained her process writing deeply personal essays about her cancer diagnosis, her depression and her personal recovery. It wasn’t enough. Fearing that a second accusation could cost her financial aid, she began running every essay through an AI detector herself, rewriting any sentence it marked until her writing voice felt flattened and unfamiliar. By the end of the semester, she left the university.

Carr is not alone. The same NBC News investigation found that students across the country deliberately simplified their vocabulary and avoided complex sentence patterns — not to write better, but to write less like themselves. Creative writing assignments exist to help students find their voice, which they can’t do in fear of an algorithm. Carr’s case shows a student reshaping her writing, and ultimately her education, around a software system she had no role in approving, in a policy she had no voice in developing.

Student involvement would not necessarily have guaranteed a different outcome in Carr’s case. But it might have changed the structure that enabled it. Students could have brought up concerns about relying on automated detectors without corroborating evidence. They could have described how fear of false accusations pushes students toward simpler vocabulary, safer syntax and less intellectual risk. They could have asked what procedural protections exist before a software flag becomes an academic charge.

Instead, at many institutions, enforcement architecture was built first. Conversation came later, if at all.

It doesn’t have to work this way. In Los Altos, California, did more than sit in on policy meetings — they designed and ran community workshops, facilitated discussions between sixth graders and administrators, and built an AI chatbot to help other districts draft policies.

A found that students overwhelmingly want to be part of decisions about how AI is used in their education — and that many already hold sophisticated views on its risks and potential. The fact that Los Altos made national news tells you how rarely that invitation is extended.

But there is a deeper reason students belong in these conversations: We know something policymakers don’t.

At my high school, I’ve witnessed — and experienced — a secret loop in the learning process: we use large language model tools like ChatGPT and Claude to genuinely improve learning by unraveling concepts, studying for tests and brainstorming ideas.

A few days ago, a student asked a question about a formula in my AP Physics C class — and nobody knew the answer. Another student opened his laptop and asked Claude, and after a few minutes of back-and-forth, we had completely straightened out our question, improving everyone’s understanding of how circuits worked. I used an LLM to compile notes from my Multivariable Calculus class, which helped me study and earn a near-perfect score on my test. My friend used ChatGPT to learn Java syntax for a project — not to write code, but to understand the language.

A found that 54% of U.S. teens now use AI chatbots for schoolwork, with the most common uses being research and brainstorming — not copying and pasting answers. But that message hasn’t reached the people writing the rules. This secret loop goes completely disregarded by schools, simply because it’s easier to blanket-ban the technology altogether. The generation that grew up with these tools understands their texture in a way no outside committee can replicate.

These AI policies directly affect students’ outcomes and futures. To exclude them from the conversation is simply undemocratic.

If educational institutions are serious about preparing students for democratic citizenship, that commitment must go beyond coursework and into policy-making. The time to invite students into these critical conversations is now. Will schools treat students as subjects of policy, or as participants in it?

Opinion: We Don’t Let Babies Play With Electricity — Why Are We Letting Them Play With AI?

Rachel Romeo and Ellen Roche — Mon, 30 Mar 2026 14:30:00 +0000

AI is newly electrifying every corner of our lives, charging ahead faster than most of us can follow. If adults are barely keeping up with tools like Chat GPT and Claude, how are babies and young children supposed to make sense of a stuffed dinosaur that sings them songs or a plush bear that draws them into conversation?

We are developmental cognitive neuroscientists who study how children’s daily interactions with parents, caregivers, teachers and peers shape , and development. We are not anti-AI, but we are extremely concerned about corporate efforts to market AI toys to parents and educators of young children. We do not yet know how many young children are already engaging with generative AI bots, but if are any indicator, this is a rapidly growing market.

Some companies say their toys and devices are “age-appropriate” and will support children’s learning and development, but that’s not always the case. For instance, the makers of Kumma, a plush teddy bear, promised to build conversational skills for children from ages 3 to 5. But the toy was pulled from the market last year after it was caught encouraging researchers testing it .

Beyond these physical safety risks, we have essentially no data on how interacting with generative AI “friends” will shape very young children’s foundational brain, socioemotional and language development. Rather, the preponderance of evidence about how brain development works in the earliest years of life suggests that families should proceed with caution before letting their littlest children play with these new technologies in the form of toys.

We are not alone in this concern. Together with scientists around the world who study the exquisite, human-to-human interactions that shape early brain and cognitive development, we recently released an about the risks of direct infant-AI interaction.

Decades of scientific studies paint a clear picture of optimal development in the first few years of life. Babies and toddlers grow and learn through daily, moment-to-moment interactions with their close caregivers. Indeed, humans cannot develop fully without these foundational interactions. Present, responsive, real-time interactions shape children’s language, sculpting their growing understanding of new words, grammar, pronunciation and social intentions.

These real-time interactions shape children emotionally, helping them map their inner experiences to their outer perceptions. There is evidence that when a caregiver and a young child interact, — from eye contact to to heart rates, oxytocin levels, and even .

Unlike AI models, which can parrot human-to-human interactions, caregivers pair their words with touch, eye contact and facial expressions that signal their love and attention. Real conversations include inside jokes, local dialects, family lore, and the distinct conversational patterns that make a family a family and a community a community.

Development is about real-time rhythm, and every unique caregiver-child dyad develops their own. It’s not about perfection. It’s about presence, something an AI model can never and will never be able to provide.

In fact, toys that imitate social responsiveness may interfere with an infant’s developing sense of how people relate to one another. The better these toys get at mimicking a parent, a child care provider, a grandparent or other adult caregiver, the more concerned we should be, particularly in the earliest years when infants and toddlers are developing a distinction between self and other — a growing awareness that the other humans who surround them each have inner worlds of their own.

From a policy perspective, . There is much more to learn about these new technologies before parents let their babies play with them.

Without these policy protections, parents and educators must take the lead, that simulate social reciprocity, replace face-to-face caregiving, or are designed to replace soothing behaviors that infants and toddlers need from caregivers in order to build attachment, trust and human connection.

The earliest recorded scientific experiments with electricity happened 3,000 years ago. Today, access to electricity has raised the standard of living for nearly the entire world. Still — after more than a hundred years of widespread use, safety standards and engineering to wield electricity for the common good — no responsible adult would let a child anywhere near it in raw form.

AI has the power to improve human lives, but these are early days. We take for granted that we cover our light sockets to protect all our community’s children. We must take the same protective stance with AI.

NYC Releases Guidelines for AI in Schools. Some Say it Raises More Questions Than it Answers

Lizzie Walsh — Fri, 27 Mar 2026 14:30:00 +0000

This article was originally published in

New York City’s Education Department unveiled its for artificial intelligence use, offering a rough road map for if and when to incorporate AI tools in school.

The guidance, released Tuesday, arrives nearly three years after a short-lived on ChatGPT. It also comes in the midst of ongoing debates about student privacy, AI’s effect on student learning and development, and the role of private companies in schools. Some schools had as they awaited citywide guidance.

Hot button issues, like how and if students can use AI for homework assignments, or whether students can use personal AI chatbot accounts in addition to tools approved and supervised by the Education Department, are still being hashed out.

City officials are asking families and educators for feedback, which will inform future versions of the guidance. The Education Department released a and will also host webinars and events to answer questions and gather feedback through May 8.

“AI is here, and our responsibility is to put strong systemwide safeguards in place,” schools Chancellor Kamar Samuels wrote in an email to parents.

The early framework is structured in a “traffic light” approach: green light for approved uses, red light for prohibited cases, and yellow light cases for gray areas, which require significant oversight.

For example, brainstorming lesson plans and drafting non-critical communications fall under “green light” cases.

In “yellow light” cases, schools can use AI to find trends in student data, to generate translations for bilingual learners, or adapt materials for students with disabilities — but a trained professional must first review the outputs before it is used with students.

All decisions made about students, including grading, development of special education and 504 plans, discipline, counseling and crisis intervention, and other academic placement decisions, are strictly forbidden. These “red light” cases are not expected to change in the final playbook the city aims to release in June.

Pushback has already been fierce among parents and education advocacy groups: A asking the city to put a two-year pause on AI use in schools has garnered about 1,500 signatures since October. Several Community Education Councils have also passed resolutions calling for a moratorium of AI in schools.

The guidance was written by the Education Department’s AI Task Force, and informed by the city’s external AI Advisory Council, which includes education technology partners from Google, OpenAI, and other companies hoping to contract with the city’s roughly 800,000 K- 12 students.

Questions remain about student privacy and third-party AI contracts

Before schools can use AI tools in the classroom, each product must go through a data privacy and security vetting process called the Enterprise Request Management Application. The process, created in 2023, applies to all third-party technology vendors.

But AI has become ubiquitous. The Education Department’s contract with Microsoft 365 programs did not originally include AI chatbots, but now do, said Naveed Hasan, a member of the Education Department’s Data Privacy Working Group.

“Just like TikTok was unregulated until school networks blocked it, so are these free AI products,” said Hasan, whose group advised on data privacy policies prior to the AI guidance.

Schools can visit the department’s to see if a tool has already been approved; otherwise, schools must submit an application for new use.

The process, however, doesn’t yet include guidelines on how to review certain aspects of AI products, such as algorithmic bias or instructional effectiveness. Those are expected to be included in the final June version of the playbook.

The guidelines, which were shaped by federal and local laws, say personal student information can never be entered into unapproved AI tools, and under no circumstances can student information be used to make money or train AI models.

Although the general sentiment about privacy protection is clear, how to ensure it remains protected in every use is a key question that some close to the policy development say remains unfinished.

Hasan said the guidance alone can’t guarantee privacy and relying on third-party products, even approved ones, makes it difficult to know what’s secure and what’s not.

He has called on the Education Department to consider maintaining its own hardware and training its own group of AI experts instead of relying on outside companies.

AI moratorium advocates push back

The Parent Coalition for Student Privacy, one of the groups on the AI moratorium committee, said in Tuesday that the guidance does not address the potential long-term effects of AI use on learning and thinking.

The city has already accepted that AI will be a part of school learning before proving its value and safety for students, said Kelly Clancy, founder of Parents for AI Caution, another group on the committee.

“The city needs to have a burden of proof about why this is good,” Clancy said. “It shouldn’t just be about harm reduction, but rather why AI is better for my kids than a human-centered, traditional classroom.”

Education Department officials said proposals for new, AI-focused schools and programs — like Next Generation Technology, an “AI-focused” high school — must demonstrate how they align with the guidance’s principles.

The full preliminary guidance can be accessed .

Chalkbeat is a nonprofit news site covering educational change in public schools.

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Anthropic Launches Claude for Teachers to Influence America’s Classrooms

The End of Homework? Teachers Grapple With Cheating in the Age of AI

Start your homework in class

‘How this could this stronger?’

Finale: Takeaways from a Season of AI in Education

Reflecting on mid-season insights

College and career readiness concerns

Discussing H3 framework development

Refining educational processes with AI

Parents exploring educational alternatives

Challenges of educational system changes

Rethinking microschool education

Defining a school’s basic elements

Rethinking traditional classroom model

Reflecting on content personalization

Critique of current education standards

Discussing educational model challenges

Understanding norm-referenced assessments

Exploring AI’s impact on education

Dana Suskind on How To Protect Childhood in the Age of AI

Opinion: The Final Piece of the Ed-Tech Backlash Has Finally Arrived

‘Rehumaning’ Education: Banning Screens Is Only Part of the Solution

Opinion: While Washington Debates Screen Time, Many Students Lack Access Altogether

Opinion: As AI Advances, Student Voice Must Keep Pace��

Reed Hastings on What It Will Take for AI to Be Different From Other Ed Tech

Leadership changes in education systems

Rapid AI advancements predictions

Improving education over the years

Experimenting with individualized tutoring

Self-learning education technology

Sharing AI education globally

Study: Giving Kids Access to AI Tutors Doesn’t Mean They’ll Use Them

Survey: Young People Turn to AI to Be ‘Their Real, Unfiltered Selves’

‘It just keeps feeling easier than the alternative’

What adults get wrong

As AI Use in Schools Grows, Lawmakers and Districts Scramble to Set Up Guardrails

‘Giving a tool to children’

Like aviation in 1905

Opinion: From Tutoring to Translation Help, Crowdfunding Shows Ways Teachers Use AI

The AI App Seeking to Scale ‘Magic’ in the Classroom

Challenges using general AI tools in education

Exploring MagicSchool’s AI features

Using generative AI in education

Improving model quality and selection

Building personalized learning assessments

Discussing AI-generated IEPs and privacy

Integrating curriculum with MagicSchool

Measuring feedback and student outcomes

Using AI to boost engineering productivity

Philadelphia Middle Schoolers Explore How AI Changes Their Classrooms and Their Lives

Opinion: Why the ‘Middle Path’ of AI Literacy May Be the Future of English Class

11 AI Prompts Every Teacher Should Know

A 60-second guide to prompting

Setting up a project

The Prompt Collection

The Bell Ringer

Start class with a spark

Prompt Example

The Real-World Hook

Answer “Why does this matter?” before students even ask

Prompt Example

The Bad Example Generator

Turn common mistakes into teachable moments

Prompt Example

The Scaffolding Prompt

Make instructions clear for every student

Prompt Example

The Review Game Generator

Create engaging questions efficiently for learning games

Prompt Example

The Fresh Angle Search

Bring new life to familiar content

Prompt Example

The Skeptical Student Prompt

Prepare for the hardest questions before class starts

Prompt Example

The Blind Spot Audit

Find your own blind spots before students do

Prompt Example

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