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.”

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.

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.”&�Բ�����;

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, 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.”

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.

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.

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.”

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 ���Ķ���vlog, 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.

For many schools, it’s a race to keep up. Others are leading the pack. And some are unsure what to do with it.

Artificial intelligence is playing an increasingly prominent role in Hawaiʻi education, from the recent opening of the state’s first AI-focused charter school to the development of new coursework teaching students how to navigate rapidly changing technology. Teachers are also coming face-to-face with new technology, whether it’s using AI avatars to test students’ grasp of Mandarin Chinese vocabulary or confronting kids who are submitting assignments written by ChatGPT.

But there’s wide variation in how much teachers and students are willing to engage with the new technology. Civil Beat spoke with nearly two dozen educators, administrators and students across the state about their experiences with AI in schools and what they expect the future to look like.

A few years ago, the focus of AI in education was preventing students from cheating, said Michael Latham, president of Punahou School. Since then, he said, administrators and teachers at the Oʻahu private school have pivoted to teaching students how to use new tools responsibly, rather than banning them altogether. 

“We wanted our students to become really thoughtful and critical users,” Latham said, “to be able to understand these tools, but do so really with eyes wide open, aware of what some of the challenges would be.”&�Բ�����;

Public schools are piloting their own technology, including MagicSchool, a platform with AI tools like image generators and chatbots that teachers can tailor to their own lessons, said Winston Sakurai, educational administrative services director in the Department of Education’s Office of Curriculum and Instructional Design.  

While teachers receive training on how to use these platforms, Sakurai acknowledges that some are faster to embrace technology than others. It’s important for schools to find a balance between innovating with AI and meeting teachers where they’re at, he said. 

“We want people to embrace it,” Sakurai said, “but we cannot afford to burn people out with the adoption of it.”&�Բ�����;

Students also have varying levels of enthusiasm for the new technology. Some are emerging as statewide experts in the field, with one Punahou student developing resources to help teachers incorporate AI into their lessons. Others are more skeptical of the technology after they’ve seen their classmates cheat on assignments or receive incorrect information from AI chatbots.  

“I think personally, AI is a useful and dangerous tool,” said Charwin Irebaria, a junior at McKinley High School. “People should be cautious about what they try to get out of AI, because it can really affect our future.”&�Բ�����;

McKinley High School

In Cynthia Reves’ McKinley High School classroom, an assortment of items litter students’ desks: a bear-shaped pencil case, neon highlighters, a manila folder stuffed with papers. 

Laptops and iPads are conspicuously absent from students’ workspaces. Teens place their cellphones in mint green containers minutes after the bell rings and leave their devices there for the remainder of class. 

Like many teachers, Reves is questioning if her students are using AI on their assignments – and whether it’s appropriate for them to do so. In the past year, she’s tried to eliminate some of the uncertainty by doubling down on paper and pencil assignments and reducing students’ use of laptops and cellphones in her class. 

“What is dishonest use of AI?” Reves said. “That implies there’s an honest use of AI. What is that?” 

On a Friday morning in May, Reves ran her students through a series of short writing exercises to prepare them for their upcoming Advanced Placement English Language and Composition exam. Although the test would be online, Reves tasked her students with annotating passages and writing thesis statements by hand. 

“It feels like this huge tension,” Reves said. “I feel like I’m walking backwards, and yet there’s this AI thing that’s pulling forward.”

Reves went to school in the 1980s, when students used typewriters to complete assignments and Macintosh computers were the newest technology. Reves has constantly adapted to change in her 30 years in education, from teaching remotely to managing cellphones in class, but she’s worried about how the latest AI tools could impact students’ learning. 

In the past, Reves said, she could quickly identify when a student didn’t write a paper on their own. Just by plugging the opening sentences of a paper into Google, she said, she could tell when students found a copy of a similar assignment online and claimed it as their own. 

Now, Reves doubts herself. While she occasionally questions if students have written an essay on their own, they’ll deny using AI – and there’s no way to prove it, Reves said. Many AI detection tools have proven unreliable. 

Reves now confines most of her writing assignments to the classroom, rather than assigning essays as homework. If students want to finish a writing assignment during study hall, she said, she requires their teacher to sign off on the paper at the end of the period, affirming that the student completed the work on their own. 

But Reves also knows she can’t avoid AI forever. At the end of a recent English class, she assigned her students homework requiring them to read a short passage and answer questions. Once they completed the assignment, she said, they could run the writing prompts through AI to see how their answers compared to what the chatbot suggested.  

She wouldn’t have offered this option in August, Reves said. But her students have developed stronger writing skills and judgment throughout the year, and she believes they can now evaluate the AI responses’ critically.  

“I trust you,” Reves told her students.  

Mid-Pacific Institute 

Leslie Gleim’s preschool students at Mid-Pacific Institute are confident they can outsmart ChatGPT.

On a recent Wednesday morning, Gleim led a small group of 5-year-olds in a lively conversation about the observations they made on a recent field trip to Hoʻomaluhia Botanical Garden. She encouraged students to use their imagination to describe the clouds and explain how they came to life during their visit.  

“It was the happiest cloud!” one boy said. Another student speculated some of the clouds looked sad because the class hadn’t visited the garden for a few weeks. 

As the children chimed in, ChatGPT transcribed the discussion word for word on Gleim’s phone. Later, Gleim asked it to summarize the themes that emerged during the students’ conversation and compared the notes against her own observations.

The AI tool didn’t go unnoticed by the kids. 

“We seem smarter than Chat,” one student, Knox, said as Gleim checked the AI transcription on her phone. 

“Are you?” she asked. The group of kids erupted into cheers and enthusiastic nods. 

A few minutes later, Knox and his classmates put their theory to the test. One by one, they spoke into Gleim’s phone, asking ChatGPT abstract questions about their field trip to the garden and the personalities they gave the clouds during their discussion. 

After every question, Gleim asked her students to evaluate ChatGPT’s answers. Students were skeptical of the responses the AI generated – and Gleim was proud. 

“You guys won!” she said as students gave ChatGPT’s answers a resounding thumbs-down.

Using ChatGPT to record and transcribe her discussions with students has significantly saved her time over the past year, Gleim said. It also increases the stakes for students. When students know ChatGPT is listening to their conversation, she said, they’re more intentional with their words. 

But Gleim doesn’t want kids to think AI is the source of all answers. When she uses ChatGPT in front of her students, she said, she’s not afraid to point out its mistakes, using phrases like, “Chat didn’t get it.”&�Բ�����; 

“It’s not the gospel, but a tool,” she said. 

Hawaiʻi Technology Academy

Two years ago, Kingston Collman came to a sombering realization: AI was coming for his dream job.

Collman, a recent graduate from Hawaiʻi Technology Academy’s Waipahu campus, spent his early years of high school studying game development and planned to pursue a career in computer science. But as AI became more advanced, he realized projects taking him a year and a half to complete could be completed in minutes by new technology. 

“I was like, freshman year, AI is going to take over,” the 18-year-old said. “I don’t want to study for six years ahead and then be jobless.”&�Բ�����;

Collman pivoted to using AI to pursue his passion of producing social media content. For his end-of-year project, Collman developed an AI assistant to expedite the process of creating videos for social media. The assistant can generate scripts, suggest a list of video shots and assist with creating social media posts in a matter of minutes — a process that previously took him over an hour, Collman said.

The goal is to increase content creators’ efficiency, not to remove humans from social media altogether, Collman said, adding that viewers still want to see authentic content. 

It’s hard to tell what the future will look like for social media creators, however, and he’s worried more content could become AI-generated. Already, AI slop — low-quality videos created with AI — are becoming����on YouTube and other online platforms.��

“You don’t know what’s real or not,” he said, “which is very dangerous.”&�Բ�����;

Some of Collman’s peers are equally skeptical of AI and how it could dominate fields traditionally untouched by technology. 

Adriana Hunt, another recent graduate from Hawaiʻi Technology Academy, is still reconciling how she can use AI to promote her artwork without compromising her creativity. For her end-of-year project, the 18-year-old asked ChatGPT to analyze her TikTok and Instagram art accounts and provide feedback on what types of posts were most likely to grow her online following. 

ChatGPT offered her advice she previously hadn’t considered, Hunt said, like adding more hashtags to her social media posts or posting more about her process of creating drawings. 

Hunt draws the line at using AI to create new art.  

When one of her teachers suggested using AI to generate ideas for her art or assist with her drawings, Hunt immediately rejected the idea. She uses art as a way to express herself and challenge her creativity — something that can’t be replicated by a few keystrokes on the computer, Hunt said. She’s also worried that relying on AI to generate art could set impossibly high standards.��

“It’s never going to go away, because it’s now so woven into the fabric of everything,” Hunt said. “But we can learn how to rely on it less.”&�Բ�����;

Kūlia Academy

In James Morice’s data science class, sixth graders throw out phrases like “bimodal distributions” and “right-skewed graphs” with ease. 

At Kūlia Academy, a charter school in Kalihi, data science and coding are just as important to the curriculum as English and math. Kūlia is Hawaiʻi’s  middle and high school, opening in 2024 with ambitious plans to develop a seven-year program specializing in data science and coding.  

Before students can pursue careers in AI engineering and technology, Morice said, they have to learn the basics. 

On a recent Thursday morning, sixth graders at Kūlia were working together to examine graphs and input formulas in Google Sheets that could help them analyze a large dataset on the maximum speeds of roller coasters across the country.    

“What does every case represent?” Morice asked the class. “What are the attributes you see?”

The goal is to give students the expertise they need to understand how AI works so they can go on to develop new tools. 

Students still receive plenty of opportunities to work with new technology at Kūlia Academy, executive director Andy Gokce said. In English classes, Gokce said, students work with ChatGPT to receive feedback on their essays under the guidance of their teachers. Others have learned how to train AI models to distinguish between venomous and non-venomous snakes.

But Gokce doesn’t want his students to simply consume AI — he expects them to go on to lead the field and develop new technology as engineers or cybersecurity experts.��

“We want them to know, inside and out, how it actually works,” Gokce said.  

Waiākea Intermediate School

Can students beat AI? 

It’s a challenge sixth graders were eager to tackle in Tyler Kojima’s world history class at Waiākea Intermediate School on the Big Island. 

On a recent Tuesday afternoon, Kojima tasked his students with defending a historical invention of their choice to an AI chatbot. He had programmed the chatbot to be skeptical of students’ inventions, which ranged from the Mesopotamian wheel to the woodblock printing technique. 

Kojima’s students could hold a conversation with the chatbot to convince it of the value of their invention, but they had to prove their point in eight messages or fewer. 

“You’re not trying to throw in the towel,” Kojima told his students. “You’re trying to beat it.”

For the next 30 minutes, students furiously typed on their laptops as they responded to detailed questions from the chatbots. 

“How do you know it’s more comfortable?” the chatbot challenged one student who was trying to sell it on the value of a new camel saddle. 

“Why should we trust ordinary citizens to make important decisions about our city?” it said in response to a pitch about Athenian democracy. 

It’s natural for people to be resistant to new innovations and change, Kojima told his sixth graders. When AI emerged a few years ago, he said, many adults were skeptical of the new technology — and so were his students.����

At the start of the year, Kojima said, many students believed AI was taboo. Most sixth graders don’t seem to use AI in their personal lives, Kojima said, and were wary about using it for the first time. 

Kojima is hoping to change students’ perceptions by introducing AI tools like chatbots and image generators that can make abstract topics in world history feel more concrete and exciting. He said he’s also careful to set clear limitations on how students can use the technology, instructing the chatbots not to give students immediate answers during class activities and occasionally rejecting the feedback kids receive on their essays from AI writing tools.  

“This tool is not going away,” Kojima recalled telling his students. “It’s up to you guys to know how to use it responsibly.”&�Բ�����;

Civil Beat’s education reporting is supported by a grant from Chamberlin Family Philanthropy.

“We’re in the middle of an industrial revolution that’s bigger than the dot.com revolution, and the world is not prepared for it,” Weingarten told The 74. “And our country’s leaders have a laissez-faire attitude about it. So I feel a huge responsibility to try and get it right.”��

Weingarten has proposed reshaping how U.S. public schools navigate AI in particular and technology more broadly, saying our kids are experiencing a crisis of attention and well-being — and that teachers are getting precious little guidance on how to help young people navigate these challenges.

Her proposal: Trim tech use, especially for younger kids, and teach all students how to think critically, communicate, collaborate and persist.“One of the worst things we’ve done in education was to call collaboration and communication ‘soft skills,’” she said, “because applied learning, problem solving, communication, collaboration, persistence — all of these — are the skills that any young adult is going to need in an AI world. In fact, these are the skills that are going to be much more competitive in an AI world.”

In a May 27 at the National Press Club in Washington, she proposed a near-ban on computer screens for students through second grade, including for assessments. She proposed banning student-facing AI in elementary schools, arguing that young children need to build foundational skills without algorithmic shortcuts. 

And she said that young people should not have access to “social companion” chatbots that simulate human relationships until age 16.

The speech makes Weingarten and AFT, the second-largest teachers union in the nation, new and potentially powerful supporters of a growing parent-powered movement to trim technology from U.S. classrooms, even as the union pushes to train thousands of teachers on how AI works. 

Weingarten proposed that schools redesign their offerings so that “active learning, including project-based, experiential and career-connected learning,” is the norm across all grade levels. She decried “drill-and-kill” rote instruction, saying that in an age when any fact is retrievable with a single prompt, the ability to apply knowledge, think critically, communicate and collaborate matters far more than memorization.

“To really prepare young people for complex challenges, our true goal is to have students who can work together and problem solve,” she said.

Weingarten noted that 31 states have now adopted some form of phone ban, and that several countries that were early adopters of education technology are pulling back. Sweden, she said, has returned to printed textbooks. Estonia, where research linked higher screen time in young children to weaker language skills, is calling for more human-to-human interaction. And Italy is re-emphasizing handwriting and traditional instruction.

Weingarten also called for establishing a rigorous new national safety and privacy standard for AI products sold to schools and creating an independently funded research consortium to study tech’s effects on children. And she proposed a new tax on Big Tech companies’ earnings to offset the environmental and societal costs of AI-driven disruption, including workers “being displaced by AI.”

In an interview Monday, Weingarten said AFT’s own $23 million AI academy, launched last year in New York City to help teachers understand and shape how AI enters their classrooms, exists in part to provide crucial guidance on how to understand the technology. Over the next five years, the National Academy for AI Instruction is expected to provide hands-on workshops for 400,000 educators, or one in 10 U.S. teachers, effectively reaching the more than 7.2 million students they teach. 

She said the institute’s mission and her new stance on tech aren’t incompatible.

“The AI Institute is really about teachers teaching teachers, and how the tech companies are not in control,” she said. “It is a people-first, safety-first focus.”

When she announced the academy in July, Weingarten said teachers face “huge challenges,” including navigating AI wisely, ethically and safely. “The question was whether we would be chasing it — or whether we would be trying to harness it.”

Nearly a year later, she said the institute now serves a crucial role in the absence of guidance from the Trump administration, which last week issued a U.S. Surgeon General’s urging families and schools to reduce children’s screen time. It suggested that schools limit school computers to computer labs, invest in physical textbooks and “prioritize pen-and-paper curricula, hands-on activities and social activities for all grade levels.”

In a media appearance last week, U.S. Education Secretary Linda McMahon said schools “need to embrace A.I., and to use it .”&�Բ�����;

Weingarten said it’s “crazy” that the U.S. Surgeon General’s office is offering more detailed recommendations than the Education Department. 

“When you actually have two-thirds of teachers in the United States having no idea how to use AI in schools, and when you have one-third saying there’s no formal guidance, and then you have the Education Secretary saying they should use it ‘appropriately,’ I mean, this is part of the problem,” she said. 

U.S. Education Department Press Secretary Savannah Newhouse said McMahon “has highlighted the many types of schools that are successfully and responsibly integrating AI in the classroom to help our nation’s students meet the challenges of today.”

Weingarten also took a swipe at Melania Trump’s recent tech-and-education event, in which the First Lady the White House alongside a humanoid robot to highlight the potential benefits of robots replacing teachers. The stunt, Weingarten said, “spoke volumes. So did the responses from teachers wondering how a robot was going to build trust with students or know when someone was having a bad day. There’s no algorithm for that. Students need their teachers — real human beings, not robots and not chatbots.”

Newhouse didn’t address Weingarten’s allegations about the administration’s leadership on AI, instead criticizing union priorities more broadly: “If there’s finally going to be an honest conversation about the damage done to American students, it should begin with the teachers unions’ enthusiastic support for a federal bureaucracy that has spent over $3 trillion only to watch student outcomes decline, along with their relentless push to keep schools shuttered during COVID,” Newhouse said. 

‘Kids are getting burned’

The effort to curb tech in schools comes on the heels of a similar one, led in large part by social psychologist Jonathan Haidt, to limit cellphone use in schools.

Weingarten on Monday said she has steeped herself in research on educational technology and artificial intelligence. But it wasn’t until she spoke to Haidt last summer about young people’s worsening that she knew she had to draw a line. 

“What really drove me was the issues around attention,” she said. 

Haidt, author of the best-selling 2024 book The Anxious Generation, has said short-form videos and other social media tools have decimated our kids’ ability to pay attention in school, resulting in fewer books read, poorer basic skills and worsening mental health. A more recent book, The Digital Delusion, by the educational neuroscientist Jared Cooney Horvath, argues that basic classroom technology has had a similar effect on skills.

In her speech, much of Weingarten’s criticism centered around increasingly widespread fears that our society is losing its way when it comes to young people’s technology use. She noted that more than half of 11-year-olds already carry smartphones, a figure that climbs to 95% among teenagers. Four in 10 teens report being online almost constantly, she said. “The pace of this tech revolution has been blisteringly fast, and kids are getting burned.”

She pointed to Haidt’s research linking heavy smartphone and social media use to rising rates of social isolation, anxiety and depression among young people, with academic consequences as well from the rollout of classroom technology. Scores on the National Assessment of Educational Progress, which had been climbing steadily, have in many cases worsened after widespread digital adoption. Weingarten acknowledged that correlation is not causation, but said the pattern, appearing consistently across states, grade levels and subjects, deserves serious attention.

She also pointed to research showing that 88% of teachers in a survey reported that their students’ attention spans were shrinking, which she attributed in part to the instant-rewards of online platforms such as TikTok and YouTube. Cognitive scientist work, she said, suggests students are not incapable of focusing, but are increasingly unwilling to do so when schoolwork feels dull by comparison to their online lives.

But she cautioned that she’s not anti-tech.

“I’m not calling for an AI ban or a Chromebook bonfire,” she said. “What I am calling for is getting the balance right to harness the benefits of technology while mitigating the harms. I’m wary of the dangers of AI, but it is here to stay. We need enforceable guardrails and help to cushion the disruption to people’s lives.” 

Alex Kotran, the founder and CEO of , said Weingarten is “right where it counts” about limiting AI for younger students but giving teachers access to the tools. “It’s about getting the balance right,” he said. “And I really don’t talk to anybody that believes that we shouldn’t have some sort of balance.”

Kotran said he’d recently spoken at an National Education Association meeting and saw that, like AFT, they’re focused on understanding AI. “There’s this almost-meme, ‘Oh, the unions are getting in the way of AI transformation, AI readiness,’ and I really disagree with that fundamentally. The unions have a very sophisticated understanding of what really matters here.”

Weingarten’s push to give teachers a better understanding of AI makes sense as well, he said. “When teachers feel like they are the main characters of the story of AI transformation, their willingness to really lean in and learn, it’s a lot more. You see a lot more buy-in.”

More broadly, Kotran said, supporting active learning, project-based and career-connected learning is “what all the smartest people in the field,” including CEOs and labor economists, are recommending. “What everybody’s basically saying is that the skills that matter now are people who can just get shit done, who can work independently and proactively on projects, who can create and build. And so it’s really, really important to hear a union actually naming that.”

On Monday, Weingarten said parents are leading the way on this issue — and that schools risk being caught between parents who opt their children out of classroom technology and those who want to keep it. “How does a teacher in kindergarten work in a classroom where half the kids opt out of screens and half the kids are on screens?”

The University of California at San Diego recently shook both higher education and K-12 when it a startling reality: Many incoming freshmen could not perform basic middle school math. 

The university was commendably specific about the causes: the COVID-19 pandemic and its educational disruptions, elimination of standardized testing, grade inflation and expanded admissions from under-resourced high schools. Together, these forces produced a class increasingly unprepared for quantitative rigor.

But here’s the paradox: These students looked highly successful on paper. Ninety-four percent had taken advanced math courses like calculus or statistics. They averaged a 3.7 grade-point average. One in four had a 4.0. 

So where did America’s K-12 school systems go wrong?

There isn’t a simple answer. But I would suggest a fundamental, largely unacknowledged problem driving these outcomes:

America’s schools are terrible at catching kids up.

A 2023 of nearly 3 million students across seven states found that those who started out behind academically rarely caught up. Students in the 25th percentile in third grade tended to remain in the bottom third through eighth grade and into high school. Low-income students, and Black and Hispanic kids,  barely moved at all. When the national ed nonprofit TNTP 28,000 schools nationwide, only 5% helped the average student catch up to grade level.

Kids who are behind stay behind. Kids who are far behind stay far behind. Call it the Catch-up Crisis.  

Why are schools so bad at catching kids up?

Because teachers are being asked to do the impossible.

In a classroom of 25 to 30 students, teachers must determine who is on grade level, who is behind (and why), how to modify instruction for each struggling child and how to extend learning for advanced students — all while delivering grade-level content.

Diagnostic exams, designed to give educators information on how students are progressing, are infrequent and often test different subject matter than what is used in the classroom. Intervention programs designed to catch kids up are purchased but poorly implemented. Students needing intensive help are sometimes segregated into programs with low expectations and weak outcomes.

In short, teachers rarely have the effective tools they need.

And in the absence of solutions, another practice creeps in: grade inflation (as evidenced by the incoming class at UC San Diego).

It’s hard to tell parents their child is behind. It’s harder still when the school cannot explain how it will help them catch up. So .

But when teachers have a roadmap for acceleration, honesty becomes possible. Poor grades become temporary markers on a path to growth, not permanent labels to be hidden.

There is reason for hope. TNTP’s study 1,400 schools where students consistently learned more than a year’s worth of material annually, enabling those who started behind to reach grade level. 

In other words, the Catch-up Crisis is reversible. But first. we need a bold, shared goal: that students who fall behind grade level will catch up to — or exceed — grade-level standards within two school years, and without fail by high school graduation.

Call it “On Track in 2.”

Governors and state education commissioners should adopt this goal publicly and report each year on how many students are behind, how many are catching up and how many are on track to do so.

Of course, setting the goal is easier than achieving it.  Doing that will mean tackling three big gaps for teachers: limited real-time insight into student learning, little evidence-based guidance on how to address specific learning gaps and minimal job-embedded coaching.

Before artificial intelligence, solving these at scale was nearly impossible. Students generate enormous amounts of work daily — assignments, quizzes, writing, projects. No human can analyze all of it for 25 students every day. But AI can surface patterns quickly and provide teachers with usable, digestible insights.

More profoundly, AI can help generate evidence-informed strategies for specific challenges.

Imagine a fifth grader who is struggling with fractions. His teacher knows he earned a C- on the last test but doesn’t know why or what to do to help. AI can analyze the student’s work in real time and discovers he tends to invert numerators and denominators; it draws on data from thousands of similar children to see what worked best to help those with the same misconceptions and recommends content for a 15-minute tutoring block for the teacher to review and revise.  

Of course, the teacher should always make the final call. But now she has a playbook to use as a starting point.

The same applies to feedback for teachers. High-quality coaching is rare because it is time-intensive and expensive, and the quality can vary without intensive oversight and training of coaches by the district. AI-supported , used responsibly, could provide timely, standards-aligned feedback on recorded lessons, supplementing human coaching rather than replacing it.

This is not science fiction. In the Bronx, Superintendents Cristine Vaughan and Harry Sherman have launched pilots designed to catch kids up in math using AI. They are partnering with organizations such as , and that provide smart and safe AI tools for teachers that assess students in real time,�� identify what’s holding them back and recommend instruction that helps them get over the hump where they’re struggling. The pilots also include high-quality professional coaching for the teaching staff. Schools using these types of programs are seeing and increased student engagement.

These efforts are not yet the answer. And no technology should enter classrooms without strict vetting to ensure data privacy and security and to avoid adding to the problem with excessive screen time for students.  But they show the potential of smart and safe technology to give teachers new tools to catch kids up.

The evidence that the Catch-up Crisis is solvable is all around. It’s up to the adults who make and implement education policy to remove the barriers that are preventing America’s students from excelling.

Correction: The essay mischaracterized how many incoming freshmen at UC San Diego could not do middle school math. The correct number is 12.5%. Also, the name of one of the Bronx superintendents was misspelled. It’s Cristine Vaughan.

That era produced real gains, particularly in its early years. But over the past decade-plus, national academic progress has slowed, a trend that began before the pandemic and worsened afterward. 

The show that reading and math achievement remains below pre-pandemic levels nationally. Researchers and educators point to several overlapping challenges: rising student mental health needs, chronic absenteeism, widening opportunity gaps and the growing demands placed on schools both inside and outside the classroom.

To meet this moment, another incremental adjustment to the old reform playbook will not be enough. A new education revolution is needed, one that prepares students for a world shaped by artificial intelligence and puts their voices at the center.

AI offers the possibility of something truly transformative for students and educators, but only if it is approached differently than past waves of education technology.

Properly used, AI can help students research complex topics, test ideas and accelerate learning in ways that were previously impossible. It gives young people greater independence to create, question and problem-solve.

For teachers, AI can reduce routine administrative work and create more time for engaging, rigorous and deeply human learning experiences. Most importantly, it makes individualized learning more achievable at scale, particularly for students living in poverty, English learners and children with disabilities.

But education has been here before.

Schools once believed that putting a laptop in every student’s hands would transform learning. Devices alone, however, did not change instruction or improve outcomes.

The difference now is not access. It is adaptability. AI has the potential to reshape how individual students learn, but realizing that potential will require far more than adopting new software.

In the United States, education is deeply local. That creates room for innovation, but it also creates fragmentation. As schools begin adopting AI, districts across the country are developing different approaches, frameworks and expectations, often with little coordination or shared direction.

At a moment when the country should respond as it did to Sputnik — with a national call to action — there is a risk of disjointed efforts while other countries move forward with greater urgency and coherence.

AI will not replace human relationships or judgment, but it will reshape many aspects of work and learning. What is increasingly clear is that people who understand how to use this technology effectively will have a significant advantage over those who do not. 

That makes AI literacy essential. If every child deserves a fair shot at the American dream, then every student in every community must have the opportunity to develop these skills.

So where should that work begin?

Right now, many of the frameworks guiding AI in education are being written by adults: policymakers, technologists, researchers and commentators. Many are thoughtful. But most are still missing something critical:

The voices of students.

Today’s young people are digital natives in a world designed by digital immigrants, already navigating these tools with greater fluency than the adults around them.

Students will inherit the consequences of the decisions being made now. Shouldn’t they have a say in how AI shapes their education and future?

This summer, AASA, The School Superintendents Association, and Day of AI, a nonprofit initiative born out of MIT, are launching a that will bring together 50 school leaders representing every state to explore the future of AI in education.

Superintendents will participate in learning experiences on emerging AI innovations, breakthroughs,and the implications for schools alongside MIT experts, including , director of MIT RAISE and one of Time magazine’s 100 most influential people in AI.

But the school leaders will not come alone. Each will bring two students from their state.

Those students will participate in a parallel convening at the Edward M. Kennedy Institute for the United States Senate, where they will serve as “student senators” and work with Kennedy Institute staff to draft a National AI Policy for the responsible, productive and ethical use of artificial intelligence in public schools.

The student-developed policy will be shared with AASA’s nationwide network of more than 10,000 district leaders, giving students an opportunity to influence real-world conversations, decisions and guidelines on how AI is used in classrooms across the country.

Thirty years ago, education reform gained momentum when leaders across political and ideological lines rallied around the belief that schools could better prepare students for the future.

Today, education faces another inflection point.

If students are expected to live and work in an AI-driven future, they should help shape how that future is designed.

The next revolution in education should not be led by policymakers and pundits, but by students themselves.

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 .

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.

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 .

Dacia Toll, co-CEO of and co-founder of , joins Michael Horn and Diane Tavenner to share how Coursemojo is using artificial intelligence to support students and teachers in English Language Arts. The conversation dives into how Coursemojo functions in real classrooms and the very human process it took to build the product itself.

Toll explains how she and her team started with the core curriculum — high quality instructional materials that build knowledge and vocabulary over time — in schools, then focused on how to ask students the right questions to gauge their understanding, give them the right feedback and then ask the right next question. They then figured out how to surface those insights for teachers in actionable ways. 

Listen below to hear about the professional development Coursemojo offers teachers and how AI makes it much easier to rapidly incorporate feedback and update the product, but, of course, with limitations.

Listen to the episode below. A full transcript follows.

Diane Tavenner: Hey, Michael.

Michael Horn: Hey, Diane. Good to see you and continue to crank on, uh, these, uh, AI tools that are starting to change what learning looks like in schools with you today.

Diane Tavenner: Today is gonna be a really fun one. I’m very excited to dive in with our guests today, but before we do that, this is our second time making this ask of our listeners, a second time in like seven seasons. And we never really thought this was important or quite frankly even thought about it, but it turns out it would be super helpful if you all could rate or review Class Disrupted wherever you’re listening to the podcast. And of course, please subscribe to it, and, and we’ve never asked you to do this because this is very much like a labor of love for us, but, but it turns out it kind of matters.

A little bit.

Michael Horn: Yeah, it’s absolutely true, Diane. And so a good way for other listeners to find out about it. And we of course get tons of private feedback from listeners, so we know you’re all out there. But, you know, if you can rate it, review it, subscribe it, you’ll help other people figure out as well what’s going on here. And while, as you said, this is a passion project for us, we do want it to matter and change the broader dialogue so people are having these conversations. And it turns out those, 5 stars, subscribe, beep, whatever it is. Those are a big deal, right, Diane?

Diane Tavenner: Yeah, they are. And then the last thing we’ll say about this is please keep telling us privately the things that we’re asking you to now say publicly, which is like what you like, who we should talk to, what’s working. We really do love the feedback and try to incorporate it every chance we get. And so please keep that, keep that coming.

Michael Horn: Keep it coming indeed. This is going to be a fun episode today though. It’s a friend of both of ours who, deep admiration for Dacia Toll, is our guest today. She’s a lifelong educator, school builder. She’s known for her work, obviously in 1999, founding principal of Amistad Academy in New Haven, Connecticut, dedicated to closing that achievement gap and then went on to found, of course, Achievement First. A network of many, I think 40-plus charter schools in the country, recognized nationwide as one of the highest performing school systems and so forth. And then in 2021, Dacia left Achievement First and soon after launched this company called Coursemojo, which we’re gonna get to talk about today. We’re gonna break it down.

It’s an AI-powered teaching assistant, but we’re gonna actually say what that in fact means, ‘cause it has very, very cool specific use cases that I think people are gonna enjoy learning about. And Dacia, thank you so much for being here. We’re thrilled to have you.

Dacia Toll: Very happy to be here with both of you guys.

Diane Tavenner: Yeah, super happy. I will say that over the years I have learned so much from Dacia on so many fronts. And so I’m really grateful to be here with you.

Dacia Toll: Right back at you, Diane.

Diane Tavenner: Yeah. Yeah.

Michael Horn: Well, I was gonna say, this is fun, right? We’ve all known each other and Dacia, you and Diane, you have something in common, which is you founded a school, then a CMO, and left a few years back and now both of you running edtech companies. We may come back to that. I should say you both raised venture funding. Like, there’s a lot of interesting things here. Listeners, of course, have a sense for why Diane made her job move, in my parlance. But why, why did you make yours? Like, what’s the founding story behind Coursemojo and the problem you were trying to solve by founding it?

Teaching, Outcomes, and Challenges

Dacia Toll: Yeah, so as you pointed out, I’ve been at this for a while now. I’ve spent a lot of time in my happy place, which is classrooms. Trying to figure out how to create the student experiences and the student outcomes, short-term, long-term, that we all want. And I do think we had a fair amount of, our students had a lot of success by at least the traditional measures. And then importantly, we always anchored in college graduation and launched into a career as part of what we were very focused on. But, it was hard. Like everything, you know, to really get a great teacher in every classroom with high-quality instructional materials and a strong classroom culture and relationships amongst kids and teachers and family and community connections. And then, I got inspired by Diane and tried to pull off project-based learning and expeditions and personal goals.

And sort of double down on, I don’t know, I don’t like calling them soft skills, but like the whole package, both as a parent, I now have teenage boys and as an educator.

Michael Horn: I always remember seeing your kid in the school, whereas you were making that transformation. It was so much fun to watch.

Dacia Toll: Yeah. Yeah. So yes, my own kids went to Achievement First schools. And so it’s just, it’s all very personal and I, I both believe as much as I did in the, in the possibility of success, but if we’re honest about it, it took so many things to line up to be successful. And I just, especially when AI emerged on the scene, I thought, wow, this, I’ve been frankly for most of that time an edtech skeptic. I think there have been lots of promises and it’s sort of overpromise, underdelivering is I think the pattern if you’re honest about most edtech. And AI felt different. Like we refer to it internally, I know others do as well as an electricity.

And you still have to build the light bulb or the power screwdriver or, you know, the tools that will leverage that electricity, but you are fundamentally working with something different now. And I found that inspiring. And so how, the problem we are focused on, it does feel like it’s been a lifelong effort, is, uh, reading achievement. Like, as you guys know, the NAEP scores in 8th grade are at the lowest level in 30 years. It does really feel like too many kids are falling off a cliff when it comes to basics of reading and writing. We could debate whether we think that’s still gonna matter in an AI-powered future. I do. And so it, and it feels really urgent.

And so we are, we have two big north star goals. We are trying to improve reading achievement. Specifically, we focus at the middle school level, although we’re now expanding to grades 3 through 10 next year. And then second, on teacher efficacy leading to teacher retention. Like we want more great people to stay in this profession. I think a lot of AI tools are trying to save teachers’ time. We certainly do that too, but we think we are in this profession because we wanna serve kids well, and that what will motivate you is if you feel like I’m doing a really good job at this thing that I care a lot about.

Michael Horn: So let’s dig in then, and just Coursemojo, obviously that tool, as you were saying, that you built using the electricity of AI to help solve that reading problem and boost teacher effectiveness. How do you describe what it does today in middle schools? What is Coursemojo?

Dacia Toll: So, there’s a student-facing side and a teacher-facing side. On the st— well, let me just say, take one quick step back before I dive in. You guys have been having so many fascinating conversations. Thank you for that. and people should like and subscribe and rate.

Michael Horn: Thank you.

Dacia Toll: Favorably, but what I, you know, I think it’s Bob Hughes and others who’ve talked about like multiple levels of AI in schools or models or paradigms. There, there is the one that I think is happening the most, which is finding efficiencies in small ways for teachers, whether that’s grading or on the operational side. And I’m, again, that’s great, but it’s sort of at the margins. And supplemental or not actually touching students at all. Then there’s this second model, which is more transformational when it comes to the teaching and learning experience. I think that’s at the moment where Coursemojo sits. And then there’s the third model, which is the AI-native schools. And I did listen to your episode with John Danner, and that I think is so.

AI’s Impact on Future Schools

Dacia Toll: And my personal belief as somebody who spent a lot of time in schools is that some people will want to and be able to make a leap to an AI-native school that’s an entirely different design, including in the, the goals that it’s trying to achieve with young people. But I think a lot of folks are gonna, hopefully make this transition into the model 2, work where it is meaningfully, it’s transformative in terms of what the experience is like, but it’s not a different universe. It’s like you’re still in schools, you’re still trying to do the core jobs, many of which I think are still important, although we could have that conversation if you wanted. So we’re going into the ELA classroom as it current — mostly as it currently exists, where you, especially in middle school, generally have content expert teachers who are trying to help kids improve their critical thinking about text and their writing skills, discourse, collaboration among students. And we start with the high-quality instructional materials. As you all know, it’s been one of the, I think, most positive steps forward to really have a quality curriculum that’s anchored in building knowledge, vocabulary, and reading skill over time. But that’s another one of those things that we all believe in, but is hard to execute effectively. And as a result, we have not universally seen the gains that we all believe we could. So we start there.

The first thing our team does is identify the hardest thinking part of every lesson. Which we know from the national research on these core curricula is often the part that gets a little watered down or skipped. Teachers run out of time, or frankly, they’re worried about the diversity of learners that exist. I think there’s on average a 5-grade-level span in a typical middle school classroom right now, so you can understand why teachers are anxious about giving the rigor of the text and task. So we identify the hardest thinking part of the lesson. Then for that part, not the entire thing, but for that 25-minute chunk. That’s where, for the kids, as a student said to me, it’s like the handout is talking to me. So it’s the same rich, wonderful text we’re already trying to read.

Adaptive Learning for Collaboration

Dacia Toll: It’s the same analysis questions we’re already supposed to be grappling with, but now kids are in partners or small groups, and we can come back to why that’s important to us, but they’re talking and then typing, and Mojo is like a learning buddy in that context. And what happens is Mojo figures out what the kid knows or doesn’t know about in response to that question, and then affirms, gives a little moment of metacognition if they need it, if they’re, if they’re struggling at like an insight, and then gives them the next just right question. And we could get back to why all three of those steps I think are important, but that’s something we worked on over time to make sure, we’re not trying to replace the teacher, but we’re trying to, as our great teachers have said, I can’t be in 27 places at once. So how can we get as close as possible in what a good teacher would do when a kid’s struggling, with a rich, meaningful question. So the kids are working. Meanwhile, the teacher has a live dashboard that shows every student’s level of understanding for every question. And so every class is wonderfully different, but In general, 85% of kids are humming with their partner and with Mojo, but the teacher knows right away the 15% of kids who are struggling for whatever reason, could be motivational, could be comprehension, and directs their effort and then sort of tees up for them what’s the gap between what the student’s current response and the criteria for success for that question. And so the teacher can, does, go around the room, conference one-on-one or with the entire small group, and push them forward as well. So we don’t think the AI’s gonna get every kid exactly where they need to be for deep understanding.

And in fact, we very meaningfully want the teacher to be focused strategically on what they can uniquely do well. So that happens for about 12 to 15 minutes that the kids are working on these close, generally close read questions, could be writing, and then the teacher goes over to, pushes a button and Mojo tees up the two biggest misconceptions in the class right now, and a suggested discussion question. So the teacher reviews them. We always want the teacher to make the choice about what’s the best use of time. Then the teacher pauses the class and facilitates a class discussion, not about every single question, but about the thing that is most holding kids back, often cuts across questions. And what we found is, I think it’s 85% of kids say they’re more likely to participate in class after having worked with Mojo. So on both the student side, they’re encouraged to participate. And then on the teacher’s side, they’re more confident because they kind of know what to go after.

And I think this just hits on another point. There are AI-powered learning experiences that are silent solo.

Diane Tavenner: Tons of them.

Dacia Toll: We, for a whole bunch of reasons, we’re talking about core Tier 1 instruction. We want it to be as beautiful as what these curriculum materials and teacher vision calls for, with more discourse, not less. And then again, there’s a lot we don’t know about the future for these young people, but we know we’re going to need our human skills more than ever. So that ability to work together, both in a full group setting and a partner setting, is really important. Anyway, and then finally it does end with an exit ticket, which is kids do independently, often writing. What’s different now is kids get, in every phase of this, kids get multiple rounds of feedback and then they revise their thinking and they revise their writing to make it better. And we know that also, like, if you don’t, I think about all the grading I did over the years and like, you just grade and the kids don’t.

You have to revise.

Diane Tavenner: Yeah.

Dacia Toll: So that’s inherent. And then there’s celebrations throughout. And Mojo also can make, it pulls exemplary student work, highlights kids who should be shouted out. So that’s the long whirlwind tour as to what it looks like.

Diane Tavenner: It’s awesome. Thank you for making it so concrete.

Michael Horn: Yes.

Reimagining Teaching with AI

Diane Tavenner: Literally taking us into a classroom about, and you know, anyone who’s been in a classroom, an English language arts classroom, what I taught certainly, like what you’re describing is what I aspired to do as a teacher, right? But I had to use my whiteboard or in the old days, my chalkboard. And I would, what I would call bumblebee around the classroom. Like you’re just trying to like bumblebee around so the kids are supposedly doing what they’re doing, but you’re not giving any feedback. You don’t actually know if they’re getting it or not, you know, like and so, it is making, well, in my words, it’s making the mere mortal be able to be the sort of superhuman teacher that we all want and imagine. And it’s just like my partner in doing that, right? I can be everywhere all at once and, and I, you know, have this brain working next to me and whatnot. What I know is that well, What I know from you in our previous conversations, and this is where I’d love to dig in and get a little bit nerdy right now, is I don’t— Michael and I keep pressing people to say like, what do you mean when you say by AI? You know, AI, because I think most people think that’s like, you know, logging into ChatGPT or Claude or Gemini and just asking a question. And you have gone through an extraordinary amount of work with AI to enable everything you just described. So will you take us sort of under the hood a little bit?

Talk about the type of work your team had to do to train the AI and get it to do the things you’re talking about and for you to feel good about it.

Dacia Toll: Yeah. This is something I both think AI is a game-changing electricity and I’m sort of frequently disappointed from a pedagogical perspective, even with the general large language models in terms of how well they evaluate student responses and how poorly generally they do at coming up with the next just right question. They’re so wired to give away the answer that it really is not the best pedagogical experience at this point. But what we do is, so first we start with the text and the questions, but then that’s nowhere near enough. It’s about how the AI, what the AI uses to evaluate the student response. And what they’re using is not just the knowledge of the unit and the unique text complexity and the lesson objectives for the day, but rather we have programmed in question-specific criteria. So they know if you’re analyzing this beautiful Langston Hughes poem and you’re answering this question that you need to— it’s maybe a vocabulary and context question— you need to know both what the word means and you need to know what it means in the context of this beautiful extended metaphor. and there’s not one right answer.

This is what’s complicated about the criteria of success, but there is a universe of good answers. There is a universe of typical kid confused answers, and that can be constantly refined. Like as kids teach us new insights into, you know, something like poetry, we can upgrade and do in real time. But so they’re criteria-specific questions, criteria, I’m sorry, question-specific criteria for success, which to my knowledge, I don’t know that anybody else is doing. Because it takes a lot of work on the front end.

Michael Horn: Now, I was gonna say, like, you have to go deep into these texts, right? I mean, just describe a little bit of that creation process also.

Dacia Toll: Yeah. And then there’s a whole other reading framework, but we can get to that. So yes, initially it required some of the best teachers we know on staff to do that kind of level of intellectual prep that you would do. Basically, what constitutes an exemplary answer? What are the transferable criteria of that great answer? So originally we human-powered it. Now we have so many examples of excellent human-powered criteria and have trained an AI authoring platform, which is internal-facing only, to give us a good rough draft. But we are still having humans do a gut check basically on, on— we could talk about what that looks like, but there are multiple steps. It’s like dominoes that the AI agent will tee up different things, and we still want those excellent teachers to go through and check.

Complexity of Reading Challenges

Dacia Toll: But so that’s what’s happening on the question-specific criteria. The other thing that we found is essential, which is why I really do believe for at least a long time, the specialized products are gonna outperform the general, like thin-layer products. And so we are very clearly geeking out on reading. It’s, as we know, reading is not math, and it’s not as simple. Like when a kid is struggling to understand a poem and to identify the central idea, it’s rarely that the problem is the skill of identifying the central idea. It’s almost always that there’s something else about the way that text complexity that is getting in the way, or it could be a fluency issue or background knowledge or a vocabulary issue, and we sometimes wanna say, oh, it’s a main idea problem, which then leads people to go outside the curriculum and do a bunch of main idea practice, which we know does not work. So like there are a set of ways in which we now have a whole reading framework that we’ve developed with, the good folks from Anet, Whitney Weldon, a whole bunch of reading experts, and it sort of honors the complexity of reading.

So Mojo’s looking at the criteria-specific success, it’s looking at the reading framework, and it’s trying to figure out what does this kid likely not understand about this? And that’s where there’s now this light bulb step that gives them like a little hint, and then it asks them the next just right question. And that is actually pulled from a bank of suggestions that is also, was initially human-authored, is now AI-authored specific to that question. So that’s not even tuning or training. It’s that the AI in real time is consulting with a set of resources and trying to pull the exact right instructional move for that kid. Would it be helpful to give an example or

Diane Tavenner: Yeah, well, because these have been created by expert teachers, vetted by expert teachers.And so like, I think the thing I want to, and Michael helped me here with the language, but like, there’s a lot of people, I would say the majority of people who are doing AI products are literally just putting like a wrapper around the language model right.

Michael Horn: With a bunch of instructions of guardrails around the context window.

Dacia Toll: It’s like prompt engineering, maybe.

Michael Horn: Yeah, not the training you just described.

Diane Tavenner: No, they haven’t literally taken expert professionals to work hand in hand with the AI to then produce this new experience, if you will, that brings the best of both of those. And so please do give us an example. I think it’s super helpful.

Dacia Toll: Well, I was in a classroom last week in Colorado, and the kids were analyzing a poem, and the question from the curriculum was, what role do lines 6 and 7 play in this poem? And these 7th graders were like, I really have almost no idea. So to the best of their ability, They are, they sort of, most of them tried to say what was going on in lines 6 and 7. And so what Mojo does is like, OK, affirm, first of all, good job, you know, in your own words explaining what’s happening in lines 6 and 7. But then the light bulb step comes. This question is actually asking you about the author’s craft and an intentional choice the author made to include these lines. Follow-up question: how would this poem be different without lines 6 and 7? That is like a — both the kid is like, oh, I didn’t even understand that this was about a choice the author was making, like author’s craft, and then I did — now I’m like guided into a process of actually trying to figure this out. And then if they struggle with that, Moja will say, well, what happens before and what happens after, you know, there’s like a set of additional questions that come out that what new teachers have told us, it’s one of the greatest compliments, is they do the Mojo activity before the kids so that they understand how to ask a scaffolded question without totally draining the rigor out of the thinking work that’s required, or how to give bite-sized feedback.

So that’s what we’re trying to do.

Diane Tavenner: Dacia, let’s stay here for a minute, as like a lifelong English language arts teacher. I wonder if I know some people will hear this and be like, who the heck cares if that author, like what their purpose was in those two lines? Like why, why are kids even learning this? Can’t we just teach them to read? So let’s spend a minute on how that transfers into the world and why that is so important and how, yeah, let’s start there. And then yeah.

Dacia Toll: Well, there’s so many layers to your question. And I’d love for you as a lifelong ELA teacher to offer your own point of view as well. But I think first we have the question, do kids in this new future still need to learn to read and write? And my, my strong conviction is yes, they do. Like, we’re going to be processing lots of information, but as we all know, there’s no firm line between listening, reading, and writing. It’s all the same cognitive process with each of them reinforcing the other. So learning to read is also a way in which, even if we believe AI is going to talk to us in the future, I think there’s still like the vocabulary and the, and the sort of way sentences get put together to effectively or ineffectively convey meaning. So that’s one. It’s like we could talk more about that. But I also think, I actually do believe we should be letting kids write more advanced pieces using AI.

That’s a whole separate other question. But if they haven’t learned to write themselves, I think that is a very dangerous place to start.

Diane Tavenner: Yeah.

Dacia Tol: So, that’s one. Second, it’s really just critical thinking.

Diane Tavenner: Well, that’s where I’m going. Like everyone’s talking about cognitive offloading and the lack of critical thinking and what you just literally put your finger on that activity is, oh. I can question or get curious about what an author’s intention was and why they did something. And that applies to every article. It applies to the —

Michael Horn: Well, it applies to the AI reading output you’re getting, right?

Diane Tavenner: Literally. Like, that is —

Dacia Toll: It applies to art. I mean, it applies to human interactions. Like, why is this person doing or saying what they’re doing in the way they are doing or saying it? And what does that reveal about them, their purpose? The message they’re trying to convey. Yeah, I think, I mean, just to, in defense of productive struggle, the brain, that’s the way we learn. Like if we don’t attend and focus and think and productively struggle, now there’s a zone in which that’s productive versus unproductive. But, and that’s part of what I think AI can help us get more kids like in their zone. But you have to, you have to remember. And forget and recall.

And these are the ways that the neural pathways get formed in our brain.

Diane Tavenner: Yeah. And I think your ability to like bring this into the classroom every day where it’s like, let’s just imagine now these young people every day in their class, they’re just doing this type of work over and over and over and over again. It is a muscle. You have to work it out. You have to build it. You have to practice it. You know, it will go away if you don’t use it. And as much as we’ve aspired for classrooms to look like this all day, every day, they, they for the most part don’t and haven’t.

And I’m not putting blame on anyone because it’s just so hard to do. And I think that’s why you call AI electricity, because it actually enables that, right?

Balancing Curriculum and Students

Dacia Toll: Well, I think so. Again, part of what we’re doing is going into the. Part of what you asked again, what set me on the journey, one teacher said to me, she said, am I supposed to teach the rigor of the curriculum or the kids in front of me? And the answer is both, but back to the whole thing being a little hard. Yeah. Like, she’s not wrong that bridging that, much less bridging that for 26 wonderfully unique individuals is very hard. And I think what too often we see happening in classrooms is the teacher, one of two things happens. Either they are so worried about kids struggling, and they’re not wrong, left to their own devices with that question, I watched it. A lot of kids out of the gate didn’t know what to do with that question. And so, the teacher holds it for the whole class and they ask the question and maybe, maybe 3 kids answer and then they move on.

And then the next question, same thing. And frankly, intellectual engagement is optional. For the other 24 kids in the room. And some of them are probably paying attention. Some of them, this is middle school, may not be or they’re going in and out of attention. The other option is I give it to them on a handout and kids are set to struggle and they put down the best answer they can. They put down the literal comprehension of those lines of poetry and then they move on.

Diane Tavenner: Yep.

Dacia Toll: The difference now is every kid is answering every single question. And intellectually grappling with it, and they’re getting real-time feedback that allows them to revise their thinking and their writing, and they get closer to encoding success before they move on. Cognitively, that’s a very different experience. Yeah.

Michael Horn: Yeah. I was gonna say the headline I’m taking away from this, right, is everyone’s worried right now about AI and cognitive offloading. You actually have used it to do the exact opposite, which is to make sure no one escapes this cognitive work and struggle, which is a significantly different use of use case of AI, but it’s significantly different use case of the classroom in a lot of average schools across the country right now. It leads into the question I have, which I’m just curious, like, is it hard for schools to figure out how to use Coursemojo? What does that on-ramp look like? Do they think of it as AI? Do they think of it as like an engaging digital learning activity? Are you, are you getting caught up in the backlash against edtech and the overpromising and underdelivering? Like, how is that all playing out?

Dacia Toll: Yeah. I personally lead a lot of teacher PD. I just love doing it and I learn a lot from it and it’s kind of fun to show up and we have a phenomenal partnership with Jackson, Mississippi and I was their leading teacher PD and they’re so nice. Like teachers are so nice. They come in, they, but the truth is they’re looking at me like, who is this lady? And it didn’t help that all they were told is like, come and learn about a new AI tool aligned to the curriculum. Like, That does not inspire confidence in the vast majority of teachers. And one of the things that’s wonderful is they actually have a significant number of experienced teachers. So it was actually teachers who had been teaching for a while and they start out a little side-eyed, like they’re nice, but they’re like, eh, skeptical.

And the first thing we do, like within the first 15 minutes of the PD, is they become students in a Mojo-powered classroom. And it’s often a text because it’s a curriculum that they’ve taught. And we start normally with, you know, we give, serve up a very challenging one. I remember in this case it was historical fiction about westward expansion, and they were like, “Ooh, the kids really struggled with this one.” And 5th grade. And then the teachers get into it and they realize how delightful it is. I mean, one thing I do really want to emphasize is we care a lot about joy and you should love reading. This should not be, yes, it’s cognitive and you have to productively struggle. But we are often like Mojo and the, and the way it’s organized, we’re taking delight in the text and the insights and the combination of affirmation.

And then when you get a partially correct or fully correct answer, Mojo has like just little emojis, like hundreds of them that sort of like your message the same way it would on social media or texting. And the kids love that. Like, oh, I got the little man on the surfboard or the muscle. And, and then if you get a 3 out of 3 on your writing after multiple rounds of revision, you get different gifts. And I saw a llama on a surfboard yesterday. And like, it’s the digital sticker. Like, we don’t overly gamify. They’ll never be playing Asteroid Blasters or whatever in reading class, but We believe in recognizing quality thinking and quality work, you know, the same way a great teacher does that praise.

And we make it easy for teachers to celebrate kids too. That’s even more meaningful. But the point is the teachers start out in this skeptical place and then they experience it and they experience the delight of my ability to identify who needs help and make my way over to them or facilitate the class discussion. And the other thing I would say is because they have this live dashboard teachers have told us they’re more comfortable letting kids work together in partners in small groups. Which I also think is something that I’m anxious about is there’s not enough of in classes in this, especially in this AI future. And because now they have, as a teacher said to me, eyes everywhere. They know immediately if that group in the back that’s gotten good at looking like they’re working is not actually working. So that’s important.

Improving Instructional Effectiveness

Dacia Toll: I think in terms of what’s hard, we also have the pleasure of working in New York City, and I was walking classrooms there with some coaches, and we were identifying that some of the small group work could be more effective. They were just happy it was happening, but they want— with it, it could be more effective, and that the full class discussion could also be more effective. Those are things they said their work— I mean, New York City has seen great gains with New York City Reads. But that’s the thing, that’s what they’re working on already. Like that’s the same way we’re all trying to improve effective instruction. Yeah. And I think the feeling was in the moment Mojo is nudging those behaviors to be enabling and nudging, but that there still is a level of teacher training and expertise that has to run alongside. So we’re asking ourselves, how could we support that even more in the context of the product with suggestions and sort of additional insight in real time.

But that’s really the hard part. I would say logging in is smooth and easy because of all single sign-ins. The dashboard is clear and intuitive. It’s colors that direct you where you need to go. So that’s not the hard part. It’s teaching and helping. We’re giving an alley-oop, but the teacher still has to execute some of those, those effectiveness moves.

Diane Tavenner: Dacia, along those lines, I think about this often, and I don’t want to give you heart palpitations because it gives me heart palpitations, but if you were back leading a network of schools again, like we, we used to, and I know neither of us, that’s not our chapter of life right now, but if you were there at this moment, what other opportunities besides this one, because clearly this is a need and you’re passionate about, like what else are you seeing in the world that you would be hopping on and wanting to bring into your schools and your network? What’s the sort of low-hanging fruit that you’d be going after?

Dacia Toll: Yeah, well, I think those are two different questions. Low-hanging fruit versus, I mean, what you inspired me to do, Diane, oh my gosh, more than a decade ago, was to create a whole new school model. And I do think back to this model 1, model 2, model 3. I mean, we’re all finding incredible efficiencies just by using AI and finance, the operations that there’s so many ways in which we should be doing that. And I am for saving teachers’ time. I’m most interested in the space I’m in, which is like, how do we improve the core teaching and learning? And I think those have to be in some level student-facing because, yeah. And I know there’s been some resistance to that and it has to be safe and, and pedagogically strong, but I would be trying to create a new model as well.

AI-Powered Learning Transformation

Dacia Toll: And I think that I don’t think it, you take it, you know, what we did inspired by you was first one school and then three schools. But I have the pleasure of being a part of a CIPRI fellowship, and they asked us to redesign, you know, to design a school, if we could, or a school model based on what we know now. And I do think the emphasis— I still believe knowledge matters. I still believe core skills matter. And the emphasis has shifted for me, like entrepreneurship, creativity, the human connection skills, leadership, judgment, ethics. And I think because we can potentially have the AI-powered learning experiences be so much more effective, I think that opens up more time for, I know a personal favorite of yours, project-based learning. I think there’s a way for AI to be embedded coaches in projects so that we, they were always so darn hard to pull off. But I think what’s exciting to me about this chapter, and the first thing we’re always talking to district leaders about and they’re talking to each other about is, Start with what’s your vision? What are your goals? What’s your vision? What are your values? And now increasingly AI can power a lot of that.

Now it takes this kind of specific design the way we’ve done it. I don’t mean to imply like you can go to ChatGPT and it will run your school for you. Like that is not how it’s gonna work. But if you decide we’re committed to project-based learning based on a knowledge graph, that can be powered now. And I’m hopeful that there’ll be more and more products that are trying to bring more and more of these experiences to life.

Michael Horn: Let me ask one last question. And Diane, I want you to answer this as well. So it’s a question for both of you. You didn’t wanna give yourself PTSD on running a network again. But, but, you know, we’ve talked about it, like you both founded schools, you both founded charter networks, you both had distinctive philosophies, enjoyed success. You both left, founded edtech companies. I’m just, I’d love to hear one reflection that you both have from being on the other side, if you will, on the edtech company side, or a company that provides to schools. You can view it either way.

One reflection that your school network founder self would have been surprised by at the time. Both of you, I’d love to hear your reflections.

Dacia Toll: Do you want to go first, Diane?

Diane Tavenner: I’m thinking, I’m thinking.

Dacia Toll: I will just say. There’s so many things, but what I was, it took a while for me to build new muscles, frankly, because initially what, what, particularly when you were running a system that got into a certain size, like as you said, we were 41 schools when I left, you had to set these big multi-year priorities and goals and you had to go after them in a sustained focus kind of way. And it was a lot about. Keeping this large organization aligned around the pursuit of those goals and cascading communication and systems to support this. And this is fast and iterative and responsive. And if we had tried to write a 2-year product roadmap, it would’ve been so painfully wrong. And so we start with vision and values. I’m not saying you don’t start with vision and values.

But then part of why I’m spending so much time in schools right now is watching and listening to kids and teachers and both what’s causing them friction in the moment and what their aspirations are. Like, what are they trying to get done they can’t get done? And then that literally dictates our product roadmap in the most wonderful way. And it’s really a code— we consider our school partners co-designers with us. Like it is that we’re running design input sessions with them about next future-facing things. Like it is fun and then the tech is just moving so fast.

Diane Tavenner: Yeah.

Dacia Toll: So like our ability to now have an agent that takes the do, all the work from today and creates your do now for you the next day in all of these things, like this is now that’s so different than it was 4 months ago.

Michael Horn: Wow.

Diane Tavenner: Wow. Yeah. That, that totally resonates with me, sort of the misalignment between how quickly well-run schools and systems can run and how fast you know, actually I should say how slowly that moves, even though you think it’s moving so fast as a school or network leader. And then this outside world is just going at a different rate and pace. And the co-building obviously resonates. I think what I think we’re in this moment in time right now where it’s really, I appreciate school and network leaders who are trying to think about streamlining and focusing and only partnering with so many people and not having a million platforms and how does it all integrate and whatnot. And I think the truth is that’s just not the reality yet. I think we should be striving to get there, but if we don’t kind of open ourselves up to what Dacia’s doing in this one little space and what other people— how do we actually ever pull together a model that has the best in class of everything versus just sort of this one generic, not good at anything approach? And so there’s a real tension there I’m feeling.

And on the tech founder side now, I’m like, how do I collaborate with, how do I build a community on our side that makes it more thoughtful and doable for the schools? And then on the school side, I would invite them to think about how do we, you know, sort of work more expansively right now while we collectively, you know, bring into the, our space all the possibility and then move to, I think, more coherent and elegant models over time. I’m not sure that was a-

Michael Horn: No it’s one of the hypotheses when we started Entangled Ventures back in 2015 or whatever it was, that and this was higher ed, but it was similar, which is like, you know, Arizona State University is getting pitches from like how many hundreds of companies every single day. They throw up their hands and they’re like, I won’t say the name of a textbook publisher, but we’ll go with them. We know every product is sort of mediocre, but like they’ve got everything. It’s just simpler. Right. And how do you bring forth a portfolio of, of best in class to an organization to simplify procurement and all that messiness?

Diane Tavenner: Yeah.

Michael Horn: But also give them confidence that they’re getting the best.

Dacia Toll: Well, I would say two things on that front. I don’t envy the tsunami of pitches that land in, I mean, I, it’s bad for me that the amount of AI-powered marketing now that lands in my inbox. And so this is, I think, a huge issue. And the more that others can step in and help school leaders make sense of all the different use cases, and again, what’s aligned to their vision and values. And then we haven’t talked yet about outcomes, but the North Star for us, as somebody who’s focused on ELA achievement, is, is ELA achievement improving? And we have actually a number of outcomes-based contracts aligned to that, which I know can be scary.

Michael Horn: So you’ve put your money, but you’ve put your money where your mouth is.

Dacia Toll: Yes.

Michael Horn: Yeah.

ELA Improvement with Mojo

Dacia Toll: And, and it’s, and we have seen, thankfully, huge improvements. I mean, as somebody who spent my entire life trying to improve ELA achievement and, and actually somewhat and successfully improving, it was always like 3 percentage points a year as reflected on the imperfect but consequential state tests. And we’ve seen across multiple partners, 6, 8, 10 percentage points on state tests in a single year. And, that’s not normal when it comes to— and, I think on some level, maybe it’s not surprising because we’re getting every kid to do the cognitive thinking. Those results are based on using Mojo 2 to 3 times per week. And, in general, we have much higher uptake and usage because it’s a core tier 1 because it’s everything else. But, I think it’s only Amira and Coursemojo on the reading side that have multiple independent efficacy studies. So, that’s not even just us, that’s ESSA Tier 2 research studies now that show that.

And I think people say, maybe this is why Diane and I, coming from the seats we were in, they said sometimes ed tech folks like, it’s too soon to evaluate impact. And I’m like, I just— kids are spending an entire year of their life in one of the most consequential classes of ELA, and we’re saying we can’t evaluate impact? Like, yes, the product is very different at the end of the year, thanks to all the feedback we’ve gotten along the way, but we still had this precious time with kids. Did it or did it not improve the core thing we’re trying to go after together? So, yeah.

Michael Horn: No, I think that’s very well said. All right.

Diane Tavenner: Yeah.

Michael Horn: Let’s wrap the conversation. We could clearly talk to you for a long time, but we’ve got one more segment before we do that.

Michael Horn: And before we let you go, Dacia, the fun segment we always have is something we’ve been reading, watching, listening to.

We try to get outside of education, but as Diane and I often note, we fail probably about half the time. So we’ll let you go wherever, wherever you go on this.

Dacia Toll: I am reading and thinking and listening to a lot of education-focused stuff. I would say one quick on the work thing, Lenny’s podcast has taught a tremendous amount about— I, as somebody who was making a transition into product and tech, I feel like it’s my weekly tutorial. But I also, we mentioned my teenage boys, so I try to spend some of that time with them and they’re getting me into anime. And so I just finished Death Note.

Michael Horn: That I didn’t have that on my checklist for you. OK.

Dacia Toll: No. Well, I, I have never watched anime before, but if it’s an opportunity for me to hang out with teenagers and then and you know, they’re pretty great stories of like heroes and even more, they’re kind of into the anti-hero, which then leads to a whole bunch of good conversations.

Diane Tavenner: That’s awesome. I love that. Anything that I can connect with my kiddos on is definitely something I will do. In this particular case today, I’m going to recommend a documentary film which is nominated for an Oscar, and this was my husband’s pitch to me, which was, I will say, not very compelling, which is there’s this new film out and it’s about death, and I really want to watch it. And then I think you’ll like it because apparently you also laugh at it. And I was like, wow, that’s not compelling at all. But it turns out that Come See Me in the Good Light is actually an extraordinary film.

Diane Tavenner: And it is the relationship of two poets who have these really interesting backstories and, and one of them is diagnosed with cancer. And I think he was wrong. I don’t think it’s about death. I think it’s about living and it’s a really beautiful film for this moment in time. So I recommend it.

Michael Horn: I was gonna say it sounded like a Shelly Kagan Yale, sort of course, the way you started to pitch it and then you changed that up on us. But I’m going to go a totally different direction because you’re outpacing me at the moment, Diane. But as I said in our last episode, it’s America’s 250th. And I’m going to give my brother a shameless plug on this one. I know we both read a lot on, you know, outside of our day jobs, but this one’s a little bit more personal because it’s my brother, Jonathan Horn. He’s been writing This Week in American History for the Free Press. It’s a weekly column, comes out on Wednesdays. It’s a fresh look at history that 

I’ve really enjoyed.

He names events happening around the country to commemorate, celebrate the 250th, including an event in Dorchester near me, which has helped us plan some outings with my kids, which has been super fun. But it was actually his piece on Thomas Jefferson a few weeks back that I highly recommend to all of our friends for its bigger messages and perspectives on the state of our union then, but also the state of our union now. And so highly recommend.

And I’ll just say, Dacia, huge thank you again. This was a fantastic conversation. We’re lucky you’re working on this. And for all of you, our listeners, keep the feedback coming both publicly and privately, and we’ll see you next time on Class Disrupted.

This episode is sponsored by LearnerStudio.

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

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

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

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

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

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

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

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

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

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

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

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.

Traditional end-of-year exams were intended primarily for accountability purposes. While useful, they were never built to provide a real-time understanding of student growth or inform instruction. But despite technological advances, they’ve largely defined state assessment systems for more than two decades.

First enshrined in federal policy through The No Child Left Behind Act, these rear-facing tests offer a snapshot of performance after learning has occurred, giving educators little opportunity to adjust teaching or address gaps in real time. Preparation for these exams eats into valuable classroom learning time, and the cost of purchasing and administering them represents a major line item on state . At times, their shortcomings have for high standards.

Back when these exams were developed, schools had few options for addressing the staggering and unacceptable achievement gaps facing students. And, in places like Massachusetts, where I helped craft and implement education reforms three decades ago, the balanced carrot-and-stick approach did lead to improvements for some time. 

The Massachusetts Comprehensive Assessment System, which I helped steward, was one of the nation’s first statewide testing programs to quantify academic performance. The MCAS was critical in driving achievement across the state, and Massachusetts students scored at the highest levels in math and English on the . Our state was named a national model by the U.S. Department of Education, and the MCAS served as a blueprint for other states. 

This was in the mid-1990s, and statewide assessments — albeit clunky, expensive and time-consuming — were as revolutionary as the car phones being installed in center consoles. For the first time, states could provide parents, educators and policymakers with objective data for identifying failing schools and specific student needs.

But while mobile phones and Blackberries have long since replaced car phones, the world of education has clung to the same, outdated approach to testing. What’s worse, rather than replace the exams, states and districts — faced with both an appetite for data and the limitations of end-of-year testing — layered in more assessments in the form of interim exams, diagnostic tests and progress-monitoring tools. The goal was to generate more information that could be used immediately to inform instruction. The result, too often, has simply been more testing. At the risk of extending my car phone analogy too far, they bought and used fancy new iPhones while still paying for and insisting on using that old car phone when making calls on the road. 

Today, states and schools have an opportunity to do this differently. AI-enabled assessments, for example, can listen as students read aloud during normal classroom practice, continuously gauging fluency, accuracy and comprehension in real time — so the assessment is happening while learning is happening, not taking its place. This, in turn, can equip educators with immediate feedback to make lessons more effective and ensure that what’s being tested aligns with state standards.

In the past, efforts to adopt new measures have been thwarted by federal regulatory constraints and limited technological capabilities. But many of those longstanding barriers to innovation are disappearing. Today, artificial intelligence makes the need for repositories of test questions a vestige of the past, instead automatically adjusting difficulty levels based on a student’s answer.  And speech recognition allows a once unimaginable capture of early literacy progress — even for children who cannot yet take a test. 

Perhaps most importantly, the current administration has made clear that states now have the flexibility to explore new options. The Every Student Succeeds Act still requires annual testing, but it also permits states to choose between administering a large-scale, end-of-year exam or multiple interim tests that combine their results into a single score.

states are already using this flexibility. recently adopted a model that assesses students at the beginning, middle and end of the school year to measure progress. recently received federal approval to pilot a similar model that gives students multiple opportunities to demonstrate mastery and provides educators with frequent insights about where their students might need support. are exploring similar approaches. 

With modern technology, these models can go even further. Computer-based assessments can now capture the voice of a student reading aloud, solving a math problem or completing a writing task to generate meaningful data without pausing learning for a separate exam. The result is not simply more information, but more useful information — timely, aligned to core math and reading standards, and capable of providing timely feedback about where students are succeeding and falling behind.

These assessment models are already operating in classrooms across the country, and they can produce reliable measures of growth while correlating strongly with traditional benchmarks. More importantly, they give teachers insight when it can still change outcomes for students. That distinction matters — especially now.

National data show troubling declines in math and reading performance. Achievement gaps are . High school students are performing below levels seen two decades ago. In this environment, diagnosing learning loss in June is insufficient. Educators and students need systems that accelerate learning in October.

An assessment should not be an autopsy. It should be a compass.

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. 

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 . 

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.”

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.

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.

After taking a break from social media, Orange County student Elise Choi helped write a bill that would mandate California schools teach digital wellness — a response to growing concerns about how technology is affecting students’ mental health.

Assembly Bill 2071 would require California schools to include digital wellness in health classes, teaching students how social media and AI affect their mental health and behavior. Supporters say the bill focuses not on limiting access, but on teaching students how to use technology responsibly. 

Elise, a junior at the Orange County School of the Arts and a member of the student coalition, GenUp, said a bill that serves students — not simply alleviates parent anxieties — has been long overdue. 

“It’s powerful to have students at the center of policy change when it comes to education legislation,” Elise said. “It’s important because we are the ultimate stakeholders, and these issues affect us and our future.”

The bill follows landmark court verdicts that found social media companies Meta and Google liable for designing “addictive” features and endangering children online. Elise said it also responds to what experts describe as a growing , fueled in part by  about social media use. 

If the bill is passed, the California Department of Education must develop by January 2028 a plan to teach students about topics such as healthy screen habits, algorithms and AI and safe interactions on social media. The proposal passed a committee hearing last week and is expected to pass in the Legislature with bipartisan support. 

State Assemblymember Josh Hoover, R-Folsom, who introduced the bill in the Legislature, said the idea of digital wellness instruction was born out of student pushback against the Phone Free Schools Act, which would require all public school districts to create policies to ban or prohibit mobile phone use starting in July. 

“Now, students are realizing how much the screen time and the social media use really does impact their well-being,” Hoover said. “And they’re actually getting excited about making changes and helping their peers actually improve their health as well.”

Where cellphone bans fall short

For many digital wellness advocates like Kelly Mendoza, a senior education leader at ���Ķ���vlog Education Lab who served as an expert consultant on the bill, digital wellness education picks up where California schools’ cellphone bans fall short. 

“Phone-free schools can reduce screen time or potentially reduce behavioral issues that can happen at school, but that doesn’t teach students healthy media use, decision-making and self-regulation,” Mendoza said. “Students are still not offered the opportunity to learn these skills in school in a structured and valuable way.”

Mendoza said she regularly sees students who are cyberbullied, experience depression and suicidal thoughts, are unhealthily attached to social media or struggle with loneliness in her work at a phone-free high school. A digital wellness course, she said, would teach students that they have control over their relationship to their phones.

Students would learn practical skills such as adjusting account settings, disabling notifications and managing algorithms to limit harmful or addictive content. They would also work through scenarios such as cyberbullying, body image pressure and misinformation to develop healthier behaviors online.   

Elise said she would like the curriculum to include families, particularly those from low-income and under-resourced communities. She recently attended a digital wellness workshop at a private school in San Diego, where parents and students learned to create a screen time agreement.

“Digital wellness instruction is very inconsistent, and it depends a lot on the resources of the school,” Elise said. “I also envision digital wellness to be an equitable subject that hopefully all students can have access to.”

Social media can be ‘good’ but ‘inescapable’ 

Elise said social media also served as an essential “tool” for building connections after she switched to a different high school. She met students online who had launched social impact clubs and helped her sister recruit volunteers to teach dance classes for people with disabilities. 

“We’re not anti-tech,” Elise said. “We’re for education, and we have to be balanced with technology, because it can be good and also inescapable.”

Elise said she met with representatives from Google last week, who she said generally supported “the course of safety (for) children and youth online” and expressed support for the bill. 

Hoover, however, emphasized that the bill is not meant to shield social media companies from regulation.  

“We cannot count on these companies to police themselves when it comes to child safety, so it’s important that we’re educating students, but also putting the right rules and regulations in place,” he said.

Hoover has introduced additional bills to regulate children’s use of social media, including one that would prohibit children under 16 from creating social media accounts — similar to Australia’s blanket ban — and another that would establish an e-safety commission to enforce age compliance. 

“Tech companies have a responsibility to be regulated to make sure that they’re not entrapping kids into a very addictive technology,” Hoover said.

Mendoza, a parent of a teenager, said her daughter uses social media to share and receive feedback on her art, where she has connected with a community of artists. She said the course could also teach students how to reap the “rewards and opportunities” of social media. 

The course would examine “What are the healthy communities that you connect to that are really fostering your growth and your development as a person? And how can you change your algorithm to connect more with those things?” Mendoza said. 

Before she got her first phone, Elise said she spent her time solving Rubik’s cubes, baking and reading. She said she is now spending time on those hobbies when she gets home from school. 

“The cellphone ban only gets us halfway — it doesn’t change our relationship with our devices,” Elise said. “We need to teach kids and give us skills for what happens when we get our phones back at the end of the day.”

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.

“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.

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.

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.”&�Բ�����;

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 The 74 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.”

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 The 74.

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.

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?

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.

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.

In this one-hour webinar, hosted by AERA and The 74, leading education researchers will explore how AI is being used in assessment today, what evidence we have about its effectiveness and what risks demand careful attention. The conversation will balance promise with caution, highlighting both cutting-edge research and the policy and ethical considerations shaping the future of student assessment.

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Related coverage on The 74: 

• Two New Reports Urge ‘Human-Centered’ School AI Adoption
  
• AI Optimization’s Impact on Use of Time, Space and Resources in Schools
  
• 10 Useful Tech Tools for Educators in 2026: A Practical Guide – The 74

Updated March 27, 2026:��In response to this story, YouTube terminated six channels for violating the platform’s terms of service and one channel for violating its spam policy.

In a video that has been played almost 50,000 times since it was posted five months ago, two cartoon children sing along as they guide viewers through the experience of riding in a car amid a vividly colored, utopian backdrop. 

At first, the seems harmless. The song is upbeat and informative. The animation aligns with the promised subject. 

Except, hold on a second, did those lyrics just say, “Red means stop, and green means right”? And why are the characters changing in every frame — different hairstyles and colors, slightly different outfits for the girl and boy? 

Worst of all, for a video that purports to be “educational,” the visuals are sending precisely the wrong message about riding in a car. 

The video opens with the children riding, without seatbelts, in the front row of a moving vehicle. The next scene shows the girl defying physics, floating alongside a moving car, while the boy is seated in what appears to be the hood of the vehicle as it travels backward down a busy street. The third and fourth scenes show the children walking in the middle of the road with moving cars behind them. 

It’s not hard to imagine how the video could have gotten so many views. 

Maybe a parent needs to complete a task — fold some laundry, get dinner ready, hop in the shower — and is searching for an age-appropriate video on YouTube to entertain their toddler during that short time. Perhaps that toddler, increasingly independent and prone to running off, needs a better grasp of road safety. “Vroom Vroom! Car Ride Song | Educational Nursery Rhyme for Kids” presents itself as a win-win solution. 

But children’s media experts say this is AI-generated “slop,” and that it has infiltrated the internet, preying on young children and their unsuspecting caregivers. 

“We’re at the beginning of a monster problem, and we have to get hold of it quickly,” said Kathy Hirsh-Pasek, a professor of psychology and neuroscience at Temple University and senior fellow at Brookings Institution who studies child development. 

She and other researchers, including Dr. Dana Suskind, a professor of surgery and pediatrics at the University of Chicago, have that AI-derived products for babies and children need to be reined in. 

“This is not neutral content,” said Suskind, author of the forthcoming book . “I think of this as toddler AI misinformation at an industrial scale. It’s very risky for the developing brain.”

It’s hard to say just how pervasive this type of content is, but it’s clear the problem is widespread and getting worse. One published by video-editing company Kapwing in November 2025 found that about 21% of YouTube’s feed consists of low-quality, AI-generated videos. 

, the creator of the “Vroom Vroom! Car Ride Song,” has posted more than 10,000 videos since its first release just seven months ago, in August 2025. That’s an average of about 50 new videos each day. , meanwhile, has published about 3,900 videos to YouTube in its entire 20 years on the platform. 

The cognitive decline associated with the consumption of AI slop — such as a shortened attention span, decreased focus and mental fog — is sometimes referred to as “brainrot.” But when the audience is children, there’s not much to rot, Suskind said. Because a child’s brain is still in its early development, still being built, what you get instead, she said, is “brain stunt.”

“Every experience is building a million new neural connections,” Suskind said of children who are still in their early years. “You will be unintentionally wiring the brain in incorrect ways.”

That comes at a cost. A child may absorb the implicit messages of something like the Vroom Vroom video and end up mimicking the “downright dangerous” behaviors they saw depicted there, said Carla Engelbrecht, who has created digital experiences for children’s media brands such as Sesame Street, PBS Kids and Highlights for Children and considers herself an AI educator and creator.

Engelbrecht is also when it comes to child-targeted AI slop. She has found countless examples of AI-generated videos that could cause real physical harm.

“The more content I find,” she said, “the more horrified I get.”

They include videos of a being chased by a T-Rex; a crawling biting into an apple that appears bloody, swallowing whole grapes (a major) and eating honey (which carries the potentially fatal risk of ); and a eating raw elderberries (which are toxic when uncooked).

But there’s another category of AI slop in kids’ media, she said, with consequences that are more difficult to capture. These videos claim to pertain to learning and development, focusing on topics like literacy and numeracy, but due to the speed with which they are produced and the lack of quality checks, they end up introducing or enforcing the wrong lessons. And sometimes, the errors don’t come until midway through the content. That means if a parent previews the first few seconds of a video, they may miss the unreliable information that appears later in the clip.

A about vowels includes visuals of consonants. It also depicts letters on screen that don’t align with the audio overlay. A promising to teach about the 50 U.S. states sings along as butchered state names appear in text at the bottom of the screen — Ribio Island, Conmecticut, Oklolodia, Louggisslia. A about the seven continents frequently shows a compass with more than four points and indecipherable symbols where the “N,” “S,” “E” and “W” should be.

These may seem like silly slips from a machine, but for a child, every “input” is part of their learning process, Engelbrecht explained. “Mixed signals means you are delaying them learning the cause and effect of a thing,” she said. “If you learn that red is blue and blue is red, that’s a delay.”

“If you’re inconsistent, it takes that much longer to learn,” she added. “Every delay they have means everything else gets pushed back. That’s taking their executive function offline to go learn nonsense.”

Amid all of this internet muck, the question of responsibility is a tricky one.

“Fundamentally, everybody has a responsibility,” Engelbrecht said, including platforms like YouTube; companies that operate large-language models, like OpenAI, Google and Anthropic; the people creating and publishing these poor-quality videos intended to reach kids; and parents. 

YouTube’s current requires creators to disclose videos that have been generated by or altered with AI when that content “seems realistic.” This does not apply to cartoons and — which seems to be the majority of what’s reaching children — because it has long been assumed to be fictional content, Engelbrecht explained. 

The platform does have stricter “” for content targeting children than it does for its general viewership, said Boot Bullwinkle, a YouTube spokesperson, in a statement. It also has a “.” (These web pages, however, do not specifically address the use of AI.)

Due to the volume of content on the platform, YouTube does not catch every video that violates its policies. (It did take action against at least seven channels on the platform in response to The 74’s reporting, including terminating two.) 

“The trust that parents and families put in YouTube is a responsibility we take very seriously, and we’ve invested deeply in age-appropriate environments that empower parents,” Bullwinkle wrote in the statement. “YouTube Kids, for instance, offers industry-leading parental controls and rigorous designed to provide a safer experience for families.”

YouTube Kids is a distinct version of the platform with content that has been curated for children from birth to 12. Many families continue to use the main YouTube platform to view children’s content, though, which means many creators still have an audience and earning opportunities there. None of the AI-generated videos reviewed for this story were found on YouTube Kids, although recent in The New York Times found AI videos had penetrated that space as well.

Sierra Boone, executive producer of Boone Productions, a children’s media production company that makes original content for children ages 2 to 6, noted that kid-friendly competitors to YouTube, such as by Common Sense ���Ķ���vlog and , do exist. But they have struggled to break through to families. 

“Overcoming that juggernaut is extremely difficult,” Engelbrecht said of YouTube. “There’s a graveyard full of failed attempts to create a safe YouTube alternative.”

Boone suggested that some effective labeling would go a long way, not unlike the “” LinkedIn is phasing in, which aim to disclose when media has been created or edited by AI, in part or in whole. 

Engelbrecht thinks labels are a good idea, not least because they would be important for AI literacy, but she also believes they would penalize creators like her who use AI “thoughtfully” in their work. (She is , among other projects, an AI tool that detects AI slop in children’s videos on YouTube.)

As for who’s behind the videos, some of it originates overseas, but plenty is home-grown, created by Americans with access to phones or computers who are just trying to “make a quick buck,” as Boone put it. 

These people are often using AI at every step of the process — to develop themes and scripts for children’s videos, to generate the videos, and to automate the process of publishing the content regularly on “, in which the creator is anonymous and has no on-camera presence, Engelbrecht explained.

A little over a year ago, a popular content creator posted a video to YouTube in which she raves about a “huge opportunity” that would lead to “many millionaires.” The opportunity? AI-generated animated videos that inexperienced users could create with a simple prompt in just minutes. The target audience? Young children. 

That video has been viewed more than 335,000 times. 

“AI in general isn’t inherently good or bad, but it exposes people’s intentions,” said Boone, whose production studio is responsible for . 

The flood of AI-generated content, she added, reveals how many people have “no regard for children or how they’re impacted,” as long as it benefits them. 

For Boone, who works painstakingly with her team on every episode of The Naptime Show — researching, writing the script, editing the script, placing props, doing table reads, going to set, filming, editing the video, publishing and promoting the final product — creating children’s media is an “honor” that should be taken seriously. 

“The very foundation of creating children’s media is you are creating something that a child, in their core developmental years, is going to be consuming,” Boone said. “So what is the level of intention that you’re bringing to that? I think we need to be holding the people who are uploading this content more accountable.”

Ultimately, though, in the absence of more regulation or content moderation, the burden falls on parents. 

Parents are likely putting YouTube videos in front of their children in the first place because “they are already so stretched,” said Suskind, who still sees patients in her pediatric practice and interacts with families often. So it’s inherently challenging to ask them to more closely monitor the content that is coming through their children’s screens. 

Yet that is what must be done, Hirsh-Pasek said. Until a better solution emerges, the onus is on parents to separate the slop from “the good stuff.”

“We owe it to our kids to protect them,” said Hirsh-Pasek. “That’s what they look to parents for, to keep them in safe spaces. If we don’t deal with that or do anything about that, we’ve absconded [from] our responsibility.”

What distinguishes places like Fred Hutch is not just advanced science, but disciplined systems: shared clinical protocols, team-based decision-making and constant feedback between research and practice. These are the hallmarks of precision medicine, fueled by advanced diagnostics, data and generative artificial intelligence, and they are delivering transformative results in treating diabetes, heart disease and cancer. AI-assisted screenings are catching aggressive cancers earlier, as new models can analyze previously unexplained genetic mutations to forecast health risks.

Of course, education is not medicine. Learning is not governed by biology alone, student outcomes are harder to define and schools have nothing like the professional norms or accountability structures of clinical care. But that is the point: Precision medicine began with a refusal to accept broad variations in care when better evidence and tools were available.

AI is already in classrooms across the country, but mostly to help teachers save time or give extra support to children with disabilities or language barriers. What if all students could attend schools that said, “We treat learning like it’s your learning,” offering precision education: a supportive environment harnessing human expertise and technology to deliver truly customized solutions for every child?

That reality is closer than we think. AI gives educators the potential to understand, diagnose and respond to students’ learning needs with a specificity that was previously impractical at scale. It can rapidly surface a child’s learning gaps and strengths in math and recommend targeted interventions. But that information alone does little; AI’s power lies in being embedded in professional workflows, guiding adults toward specific, evidence-based actions and tracking whether those measures improve learning over time. To effect genuine change, AI must be accompanied by a reevaluation of the systems that contain it.

AI could improve instructional quality, for example, but only alongside a broader rethinking of the teacher’s role. Rather than incrementally improving one educator’s ability to reach every student, AI could serve a team of instructional professionals, each with specialized expertise. AI tutoring tools can help students fill learning gaps, but even the best have low persistence rates. Their effectiveness depends on student motivation or intensive adult oversight. Without structural change, AI risks exacerbating achievement gaps rather than closing them.

And perhaps most importantly: Far too many students simply don’t like school. They find it boring and irrelevant, struggle with mental health and lack strong adult mentors. Families, educators and policymakers are calling for something more joyful. All of this points to a fundamental design choice: whether AI will reinforce the existing classroom model or become the backbone of a genuinely different support system for young people.

Personalized learning was intended to accomplish this. But despite its popularity, it too often amounts to little more than self-paced software or playlists of digital content, mired in low expectations and disconnected from evidence-based teaching. CRPE’s of personalized learning schools show how easily these efforts become convoluted, mushy and unmoored from rigor. 

Precision learning is fundamentally different. It would enable educators to use technology, data and evidence to identify exactly where a student is struggling, which interventions are most likely to work and how to deliver them effectively and equitably. This is a commitment to evidence over intuition, to shared professional standards over individual preference, to accountability for results rather than good intentions. Personalization asks educators to adapt and give students more choices. Precision demands that state, district and school leaders change how decisions are made, implemented and evaluated. 

Rather than ed tech and personalized learning initiatives that fail because they aren’t grounded in evidence and continuous improvement, education needs an accountability infrastructure that looks more like medicine’s standard of care: a shared professional and ethical baseline for which treatments must be offered. In medicine, deviating from those standards can mean malpractice. Education has no comparable expectation, and introducing one would be uncomfortable. It would force hard conversations about professional autonomy, preparation and responsibility when students fail to learn. But avoiding those discussions carries costs: persistent inequity, uneven instructional quality and the normalization of low achievement.

The effort must start with defining what precision learning means and holding educators and developers accountable for its implementation. Ed tech developers should embed decades of learning science into their designs, just as medical software embeds clinical guidelines. Schools of education should lead the field in conducting and disseminating state-of-the-art research and training educators to use it, much as medical schools run clinical trials and keep practitioners current. And just as the federal government once seeded the Human Genome Project, a reimagined Institute for Education Sciences could lead a national effort to map the “learning genome” — a shared, continuously updated knowledge base of what works, for whom and under what conditions.

States have a unique role in creating the conditions for precision learning at scale. Specifically, they can:

Build precision learning consortia that bring together educators, researchers and ed tech companies to develop and test solutions and share results publicly, These consortia should make targeted investments in organizations with a proven track record of designing and implementing these approaches.

Align incentives and accountability systems so precision learning becomes a professional expectation, not an option. Just as medical boards define best practices for care, states could convene researchers, practitioners and technologists to establish precision learning protocols, perhaps starting with reading and math, where the evidence base is strongest.

Rethink the role of the teacher. In a precision learning model, “the teacher” would no longer be a single role expected to diagnose, design, deliver, remediate, counsel and motivate simultaneously. Schools would instead deploy differentiated teams, with some adults specializing in diagnostics and data interpretation and others in instruction, mentorship or intervention, all supported by AI systems that surface evidence and guide decisions. This is more a labor redesign than a technological shift, requiring that states fundamentally rethink the role of the teacher, including certification requirements and salary schedules. Precision learning would replace the one-teacher-does-it-all model with specialized teams, backed by AI that surfaces insights and supports better decisions. 

Ensure all schools have the resources, devices and staff training needed for participation in precision learning. The greatest risk of AI-driven precision learning is that it deepens divides if access is limited to affluent schools. In medicine, precision treatments began as elite offerings before standards and insurance systems made them broadly available. Education must skip that inequitable phase entirely.

If a patient were dying and a proven treatment existed, it would be unthinkable for a doctor to withhold it. Yet in classrooms, students fall further behind every day, even when research-based solutions exist to help them succeed.

In medicine, good intentions are not enough. They must be paired with evidence, standards and accountability. Education deserves the same seriousness, because the stakes are just as high. Precision learning is not about replacing teachers or chasing the next shiny technology. It is about building the professional, moral and structural capacity to deliver what we already know works for every student.

We have much of the science. We have the technology. What we need is the will, and the infrastructure, to bring them together.

AI can’t fix education on its own. But it can provide the precision educators have always needed and never had. If we get this right, we’ll look back on this era as the moment we began treating learning like what it truly is: a vital, individual and human process worthy of the same precision, urgency and care that doctors bring to saving lives.

Keynote speakers this year include Monica J. Sutton, creator and host of the children’s education series Circle Time with Ms. Monica, Yale psychology professor and Happiness Lab podcast host Dr. Laurie Santos, appearing alongside Common Sense ���Ķ���vlog’s Bruce Reed, and bestselling author Jennifer B. Wallace, whose work centers on the human need to feel valued — and to add value. 

Also featured: former Presidential Science Advisor Arati Prabhakar, who will join a panel on “moonshot” thinking and the future of AI-driven learning. And a new documentary traces the career of longtime Sesame Street star Sonia Manzano.

Artificial intelligence this year plays a bigger role than ever. Dozens of sessions examine AI’s expanding role in classrooms, from adaptive tutoring and authentic assessment to teacher burnout, algorithmic bias and what it means to be literate in an age when machines can write, reason and create.

This year, the Austin Convention Center, which typically hosts the event, is under construction. So sessions will be held at four venues around downtown Austin. Organizers are also planning a “SXSW EDU Clubhouse” at the historic , which will host daily performances, keynote livestreams and social events each night.

Because of the event’s multiple venues, space may be limited, so organizers recommend booking reservations for keynotes, featured sessions and workshops. They’ve provided an with details. 

To help guide attendees, we’ve scoured the 2026 to highlight 26 of the most significant presenters, topics and panels:

Monday, March 9:��

9 a.m. — : Researchers, district leaders and family engagement specialists examine the chronic absenteeism epidemic that has left millions of American students disconnected from school since the COVID pandemic. This panel presents the latest data on what is actually driving absenteeism — from housing instability and health crises to school climate and whether students feel they matter. It’ll explore which interventions are producing genuine, sustained improvement.

11 a.m. — : This panel presents evidence that score inflation on standardized tests, state-level proficiency standards and the federal retreat from accountability are making it harder than ever for families to get an accurate picture of their child’s true academic standing — and what policymakers can do about it.

1:30 p.m. — : This Opening Keynote features Monica J. Sutton, educator, entrepreneur and creator of Circle Time with Ms. Monica, who traces her journey from preschool classroom to digital learning spaces reaching millions of families worldwide. Sutton challenges educators to evaluate every innovation through a developmental lens, asking: Does this technology honor how young children learn, grow and thrive, while protecting curiosity and connection?

2 p.m. — : What do real students think about AI? How do they want to learn about it? This session, by MIT ���Ķ���vlog Lab’s Jaleesa Trapp and LEGO Education’s Jenny Nash, explores strategies for building AI literacy through hands-on computer science that fosters critical thinking and ensures safe, responsible AI use.

2 p.m. — : Civics teachers, researchers and policy advocates will examine how teachers are navigating the nearly impossible task of teaching democracy, elections and civic participation in classrooms where students and families often hold deeply opposed political views. The panel shares new findings from America’s Promise Alliance’s State of Young People research and explores strategies for creating classrooms where hard but evidence-based conversations happen productively — and where students develop the civic skills needed to participate in and repair a fractured democratic system.

4 p.m. — : Child development experts offer a science-backed framework for evaluating AI for young learners without compromising the play, exploration and human attachment that are foundational to healthy development. This session offers an “urgent exploration” of AI’s impact on brain architecture and what educators, parents and policymakers must know to protect young minds.

4 p.m. — : A panel of educators explores the causes of low student engagement, absenteeism and cheating, sharing classroom-tested solutions for creating assignments that are cheat-resistant by design. Rather than relying on cheat-detection software and pedagogy that punishes students for cheating, panelists will share how to foster a culture of academic integrity based on student agency, purpose and ownership of learning.

4 p.m. — : In this featured panel, Rep. Jim McGovern (D-Mass.), Chef Ann Foundation CEO Mara Fleishman, University of Pennsylvania student Maya Miller and Duke World Food Policy Center Director Norbert Wilson make an evidence-based case that school nutrition is an educational issue, not merely a logistical one. Panelists connect chronic hunger and poor nutrition directly to cognitive function, attendance, behavior and academic performance, and present district-level models that have transformed school meals into assets for learning.

Tuesday, March 10:

9 a.m. — : This featured session stars Roya Mahboob, CEO of the Digital Citizen Fund, who will draw on her experience growing up in Afghanistan to trace how exclusion compounds across the pipeline from K–12 classrooms to corporate boardrooms. Mahboob offers evidence-based interventions that have demonstrated real impact on girls’ participation and persistence in tech, as well as a vision for education that is inclusive, practical and full of possibility.

9 a.m. — : A candid discussion on the science, ethical considerations and implementation challenges of using Voice AI for assessment in K–12 classrooms. Learn what’s promising, what’s problematic and what’s on the horizon as experts explore how Voice AI differs from other AI tools such as large language models (LLMs), and how it can be integrated in ways that truly support students and educators.

12:30 p.m. — : In this keynote, Bruce Reed, Head of AI at Common Sense ���Ķ���vlog, and Dr. Laurie Santos, Yale psychology professor and host of The Happiness Lab podcast, examine how rapidly evolving AI technologies and social media are shaping young people’s mental health — and how families, educators and policymakers can respond. They explore the science of well-being, the risks of algorithm-driven systems and common-sense guardrails to protect young minds. 

2 p.m. — : This panel challenges the deficit framing that has long defined how schools, families and students themselves understand dyslexia. In an interactive session, a think tank-style panel will present a strength-based model of dyslexia support and examine how AI tools are beginning to unlock academic access for students whose abilities have been systematically undervalued.

3 p.m. — : Director Anna Toomey’s feature documentary tells the story of five mothers determined to establish the first public school in New York City for children with dyslexia. Toomey follows their battle to open the South Bronx Literacy Academy, addressing a learning disability that affects about 20% of the public. A post-screening discussion connects the film’s themes to national debates about reading instruction and equitable access.

4 p.m. — : As chronic absenteeism reaches historic highs, schools are doubling down on academics, interventions and incentives. But they may be missing underlying emotional and psychological factors driving absenteeism: stress, anxiety and lack of belonging. This session looks at how rest, youth voice/choice and emotionally safe environments can re-engage students.

5:30 p.m. — : Director Ernie Bustamante’s feature-length documentary offers a portrait of Sonia Manzano, the trailblazing actress who played Maria on Sesame Street for 44 years. A conversation with Manzano herself follows the screening, exploring how public media can reach children when formal schooling often fails, and what Sesame Street’s legacy means in the age of AI-generated children’s content.

Wednesday, March 11:��

10 a.m. — : This performance offers an early look at a show in development that began as a teacher performance at a school meeting. In this Hamilton-meets-The Sound of Music-meets-Good Night and Good Luck story, set against today’s culture wars, three high school students and their teachers navigate questions of identity, purpose and what school can and cannot teach. A Q&A with Peter Nilsson, the show’s creator, follows the performance.

11 a.m. — : This solo session by Toby Fischer, an Ohio educator, offers a sweeping reimagination of literacy for the 21st century, arguing that reading and writing instruction must now encompass the ability to critically evaluate AI-generated text, recognize the hallmarks of synthetic content, prompt AI systems effectively and to understand the social and ethical contexts in which AI-generated language circulates.

12:30 p.m. — : This keynote by Adeel Khan, Founder & CEO of MagicSchool AI, makes the case that teacher expertise, relationships and professional judgment must guide technological change. Drawing on his experience building the popular platform, Khan will share unfiltered insights on what’s working and what’s not, offering a framework for evaluating AI tools through the lens of educator agency.  

2 p.m. — : This panel examines why so many school AI initiatives rely on tools that “just aren’t there yet.” Panelists share case studies of implementations that stumbled, the lessons of those failures and the educator-driven, grassroots efforts that can move schools from dabbling with AI tools to using them for real instructional transformation. 

Thursday, March 12:

10 a.m. — : This featured panel convenes former Presidential Science Advisor Arati Prabhakar, Renaissance Philanthropy President Kumar Garg, Carnegie Learning VP of R&D Jamie Sterling and Bezos Family Foundation Chief of Staff Eden Xenakis to explore how bold learning goals can accelerate AI-driven innovation in education. They’ll examine how “moonshot-centered” models can rally diverse innovators around a shared outcome and catalyze the funding needed to scale breakthroughs.

10 a.m. — : Dubbed the “toolbelt generation,” more than half of Gen Z respondents in a recent survey said they’re considering a skilled trade career. And schools are working to modernize career preparation, including by tapping immersive technology to expose students to in-demand skilled trades. This panel, moderated by The74’s Greg Toppo, will discuss how we can harness tech to engage students in learning while preparing them to successfully meet workforce demands.

11:30 a.m. — : This session offers a ground-level counternarrative to AI anxiety, presenting a community college and workforce development partnership in Cleveland that is using AI-powered tools and training to open new economic pathways for adults who were left behind by earlier rounds of technological change. Speakers will examine what equitable AI adoption looks like in a post-industrial city and what conditions made the initiative work.

11:30 a.m. — : Leaders from higher education, industry and workforce policy examine whether universities are structured to produce graduates who can thrive in a labor market being remade by AI. The panel will ask which degrees and credential pathways are producing AI-ready graduates, where institutions are falling behind, and what structural changes will move the needle most.

11:30 a.m. — : Directed by Scott Barnett, this feature-length documentary follows bestselling author James Patterson to the front lines of America’s reading crisis to examine how the Science of Reading — a vast body of evidence-based research — is changing how children are taught to read. A post-screening discussion with literacy researchers and classroom teachers will examine what the film gets right and what systemic change will actually require.

2 p.m. — : This workshop, conducted by two top officials with the Illinois-based Education Research and Development Institute, will offer practical AI tools that automate routine tasks, generate content, analyze data and simplify communication, freeing teachers to focus on students and strategy and reducing the risk of burnout.

2:30 p.m. — : This featured panel, with Martin McKay of Everway, Hello Sunshine CEO Maureen Polo and the Brookings Institution’s Rebecca Winthrop, draws on a landmark report spanning 50 countries to explore what it means to protect children’s cognitive, social and emotional development in an AI-saturated world. Speakers will move beyond the question of whether AI should be used in schools to ask how it can be designed to strengthen young people’s capacity to think, relate and thrive.

The reports, both the results of small, intensive gatherings of educators, policymakers, researchers, tech officials and students last year, share a common warning: AI in schools must serve human-centered learning that doesn’t simply push for more efficiency. To do anything else risks creating a generation of young people ill-equipped for the future.

The findings come as young people say they’re turning to generative AI more than ever: A Pew Research Center survey released last week found that more than half of teens ages 13 to 17 use chatbots to search for information or get help with schoolwork. About four in ten report using AI to summarize articles, books or videos or create or edit images or videos. And about one-in-five say they use chatbots to get news.

For the first report, a group of 18 people met in July in Phoenix. Brought together by , a training and policy organization, and , a digital curriculum company, the treats the question of how schools should view AI as a literal “Choose-Your-Own-Adventure” story: The authors lay out three possible scenarios in which educators in an imaginary school district make radically different decisions about the technology.

In the first scenario, the district retreats from AI altogether after a data breach, abandoning a previously created “Innovation Lab,” while teachers return to traditional instruction and testing.

The restrictions soon backfire. Students continue using AI at home, but without guidance, take shortcuts on homework, developing a kind of survival mechanism they privately call “school brain.” Seeing how irrelevant most lessons are, they do just enough to get by, offloading thinking to AI tools. When tested, they show shallow understanding and poor foundational skills.

Test scores plummet, college acceptances drop and 40% of graduates land on academic probation. Employers report that graduates can neither work independently nor collaborate effectively with AI. Teachers begin departing in waves.

Retreating from AI, the authors find, creates “the worst of both worlds” — students who can neither think independently nor use AI effectively.

In the second scenario, the district, facing competition from AI-driven private schools, goes all-in, adopting a comprehensive, district-wide AI platform for automated instruction. The platform promises greater efficiency via AI tutors, automated grading and behavioral monitoring. And while it initially lowers costs and produces higher test scores, teachers find that students are soon gaming the algorithms rather than learning. The auto-grader penalizes valid but unconventional answers, while multilingual learners are unfairly penalized for non-standard answers on tests.

Teachers find themselves defending grades they didn’t assign and can’t fully explain, while families that challenge grades are stopped by “proprietary algorithms” that even administrators can’t review. The system delivers “a black box” that removes human judgment: “Students could feel the difference between being evaluated by an algorithm and being understood by a teacher.”

Before long, graduates struggle with collaboration, creativity and adaptability — skills employers and colleges increasingly value.

In the report’s third choice, the district, via its Innovation Lab, redesigns its offerings to prepare students for an AI-driven future while keeping a focus on “human-centered” education. Rather than focusing solely on technology, it develops a “graduate profile” that emphasizes critical thinking, ethical reasoning and human-AI collaboration, among other indicators.

The lab shifts to flexible, project-based learning, and students soon learn to use AI as a tool that supports but doesn’t replace their thinking. While the district continues to satisfy state accountability through testing, it also pursues federal innovation grants to fund portfolio-based assessment systems based on the graduate profile.

All is not rosy, though. The redesign is expensive and hard on teachers. Enrollment suffers as political resistance builds steam. But graduates soon demonstrate an ability to critically evaluate AI tools, adapt quickly to workplace changes and develop a “learn how to learn” mindset that serves them in the long term. 

Alumni soon report that their “robust” portfolios of work are a huge advantage in competitive job markets, and employers say they are the only new hires who critically evaluate AI’s recommendations, spotting hallucinations and biases.

Amanda Bickerstaff, AI for Education’s co-founder and CEO, said the first two scenarios are what educators at the July convening said they were seeing most often in schools.

“There was a strong recognition from everyone, including the students, the two high schoolers, that the traditional methods have not worked … for decades,” she said. “But it feels safer.”

As for going “all in” on AI, she said, that point of view is inevitable in many places, given current aggressive efforts of tech giants like Google who are “pushing into schools,” going direct to students.

“There’s this real pressure from both ed tech and AI itself, because it’s such a big market that’s never really been figured out,” she said.

What makes it worse is that few tech firms employ enough teachers to ensure that their products work well for students. “They don’t have hundreds of education people,” Bickerstaff said. Their education teams are “fractions of their headcount, working on tools that are instantly in students’ hands.”

The third path, in which the district redesigns its offerings, is “the most human” of the three, she said, and the most intentional. “The third path is the one that trusts humans and educators and students and families,” Bickerstaff said.

‘Explicitly ambidextrous’ schooling

by the , a think tank at Arizona State University, also calls for a new approach to schools’ decisions about AI, saying the technology “should be a catalyst for human-centered learning, not a replacement.”

The CRPE report, the result of another gathering in November, asserts that schools are at a pivotal moment. Their AI policies could go one of two ways: They can either entrench outdated educational models or help bring about a fundamental transformation of schooling.

“One of the big things that came out of those discussions was a strong feeling among the group that AI is currently being thought of as a productivity tool for the education system that we have, rather than a tool to radically improve teaching and learning and outcomes for kids,” said Robin Lake, CRPE’s executive director.

During its meeting, the group repeatedly discussed an “efficiency paradox” that could make schools faster and cheaper without addressing students’ actual needs. To protect against it, they call for a more coherent, human-centered approach that is “explicitly ambidextrous,” improving current practices while intentionally building toward new learning models.

The problem with AI, the report alleges, is that it could simply improve the efficiency of outdated educational models. It notes that the , a time-saving testing technology, for decades reinforced low-level standardized assessments, often at the expense of improved learning.

Instead of using AI as a new kind of Scantron, it says, AI could make way for several innovations, including new assessments that capture real-time performance as students work. It could even measure key non-academic indicators such as belonging, confidence, curiosity and relationship quality.

Lake said the report’s idea of an “ambidextrous” approach to AI came from an acknowledgement by the group that “we have to attend to the kids who are in our schools right now — and the teachers,” she said. “We have to use whatever technologies are available to make things better, but we also have to make investments in big, really different whole-school designs.”

Those could include not just better assessments but ways to help teachers provide “rigorous personalization grounded in the science of learning.”

Districts could create classrooms with multiple adults working in teams based on their expertise. And AI could enable schools to match students to internships and other experiences, handling administrative tasks so humans can focus on relationships.

Lake said the group that met in November kept coming back to one idea: Keeping an eye on both the future of school and the reality of the schools we already have.

“A lot of times when we have these conversations about AI and the future of schooling, it feels very floaty and abstract,” she said. “So I really appreciated that the fellows had a vision to connect the here-and-now to what kids need to know and [should] be able to do in the future. That feels really important for us all right now.”