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Ninety-two percent of students now rely on AI in some form, and 88 percent have used generative AI for assignments, according to a new survey. Students’ AI usage can range from summarizing content to full-scale writing support, which begs the question: What can educators do if they suspect an assignment is authored by AI?

There’s no denying that AI can enhance learning and digital literacy, but some usage raises important questions about ethics and its impact on academic outcomes.

The following scenario is becoming more common for educators: You’re grading assignments, reading them one-by-one, until one of them catches your eye. You can’t place a finger on exactly what, but it doesn’t sound like your student. It sounds like AI, so you run a check with an AI detector tool, and you get the result : 99 percent AI. What do you do?

Understand what the score means

AI content detector tools are trained to pick up on signs that text is written by LLMs like ChatGPT, Gemini, and DeepSeek. But each tool arrives at its conclusion in a different way, and the features available to users can vary widely.

First, ensure you are using reliable detector technology that is backed by research. As new AI detection tools emerge, others that have been on the market longer may be less reliable. 

Second, understand the result. If the whole text (or a segment of text) gets a 99 percent AI score, that doesn’t necessarily mean the entire text was AI-generated. Rather, the tool is 99 percent confident that AI was used to generate some portion of the text.

Talk to your student

I always recommend the simple action of talking to your student.

You could ask about their writing process to try to get a sense of how well they know their own submission. Or you could simply ask if they used AI. They may admit it–they were swamped and had to choose an assignment on which to take a shortcut. Or, they wrote a first draft and weren’t happy with the result, so they asked ChatGPT to improve it.

This is a great opportunity to discuss what is and isn’t a violation of academic integrity. You can remind your student how they should handle an issue like this in the future. Should they ask for an extension? Or just turn in that bad pre-AI first draft?

Check for misunderstandings

Sometimes there’s a mismatch between what a teacher considers cheating, what a student considers cheating, and what triggers an AI detector. Here are some common ways to use AI in a way that may trigger AI detection:

• Grammar checkers like Grammarly that incorporate AI assistance in the writing process
• Translation tools–these are often built on LLMs
• Google Docs AI features like “Help me write”
• Talking to ChatGPT for brainstorm and research, and reusing phrases written by AI
• Using ChatGPT for wording advice

I recommend using an AI policy like this tier system to ensure that students and teachers are on the same page with regards to which assistive tools are allowed. This prevents misunderstandings where a teacher allows Grammarly, not realizing that Grammarly is a full AI writing assistant now, but also uses an AI detector, which would flag any students using Grammarly’s AI features.

Look at writing process artifacts

Say your student admitted to using some phrasing from ChatGPT. Or perhaps they claim that their case is a rare false positive. The best next step to clear their name and confirm that they did the work is to look at writing process artifacts. What research did they do for this assignment, and did they take notes? Do they have early drafts saved?

If they worked in Google Docs, select File -> Version history -> See version history to see a full history of their writing process. It will be clear if they just copied from ChatGPT and pasted into the file, or if they typed it in one go from top to bottom (a sign that they had AI assistance but wanted to fake the writing process). If they have a robust multi-hour writing history, then that’s some very compelling evidence that they wrote the work themselves.

Consider the stakes

Derek Newton, author of the academic integrity newsletter The Cheat Sheet, often compares AI detectors to metal detectors. When you walk through a metal detector and it goes off, you don’t immediately get arrested and sent to prison. Instead, they investigate further.

Similarly, AI detection is a great way to flag assignments, but detection warrants further investigation before any punitive measures. A nonzero false positive rate means that any positive detection could be real, or it could be the statistically anomalous situation where a reliable detector gets it wrong.

If the student has evidence of their writing process, I would be inclined to believe them. At the worst case, they learn their lesson to not use AI assistance, even lightly.

If the student has a history of their work being detected by AI, that should also be considered. They may get the benefit of the doubt once, but the more times this happens, the clearer it becomes that there is an issue.

Hopefully this is a helpful guide to anyone navigating the nuances of AI plagiarism. It’s a difficult situation to be in, which is why it’s important to have the tools and information to handle a case like this when it comes up.

Max Spero, Pangram Labs

Max is Co-Founder and CEO of Pangram Labs. He is a seasoned machine learning engineer, most recently working on autonomous vehicles at Nuro, leading their active learning effort. He has a long history of deploying successful machine learning products at Google, Two Sigma, and Yelp. Max holds a B.S. in theoretical computer science and an M.S. in artificial intelligence from Stanford University. In addition to his passion for building, he is also an active member of the Magic: the Gathering cube community.

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This post originally appeared on the Christensen Institute’s blog, and is reposted here with permission.

What if the missing ingredient in student achievement isn’t better curriculum, tech, or teachers, but better motivation? What if the key to unlocking motivation isn’t something intrinsic to students, but something found in their relationships with peers, teachers, mentors, and communities? And what if the one thing AI can’t do is the one thing students need most?

I recently read 10 to 25, a new book by David Yeager, one of the leading research psychologists in adolescent development and motivation. The book explores what young people need most between the ages of 10 and 25 to thrive. In the book, Yeager challenges the prevailing view that adolescents’ seemingly irrational choices—like taking risks, ignoring consequences, or prioritizing peer approval over academics—result from underdeveloped brains. Instead, he offers a more generous—and frankly more illuminating—framing: adolescents are evolutionarily wired to seek status and respect.

That framing resonates with what Clayton Christensen and Michael Horn wrote over a decade ago. They observed that the Jobs to Be Done that seem to motivate students center on feeling successful and having fun with friends. Yeager’s insights sharpen the developmental psychology behind that earlier articulation.

And they point to a crucial insight for education: the key to unlocking students’ motivation, especially in adolescence, is helping them see that they have value—that they are valued by the people they care about and that they are meaningful contributors to the groups where they seek belonging. That realization has implications not just for how we understand student engagement, but for how we design schools…and why AI alone can’t get us where we need to go.

Amid rapid AI advances, many in education are optimistic that AI will finally crack the student engagement puzzle. With its ability to personalize learning, tutor across subjects, and respond instantly to student input, AI promises a more adaptive and responsive learning experience. Some even argue that these capabilities will naturally lead to greater motivation and engagement—that if we just give students tools that adjust to their pace and preferences, they’ll lean in. But that vision overlooks something deeper.

Human survival has long depended on our ability to operate in groups and individuals contributing meaningfully to those groups. Adolescence, in particular, is the stage of life when we begin learning how to become valued members of a group. The challenge of learning how to earn social belonging and respect is not a distraction from human development—it is human development. And motivation, for adolescents, flows from that deep biological imperative.

Motivation is social, not just internal

For years, education reformers have tried to crack the motivation code. We’ve discussed grit, growth mindset, intrinsic motivation, relevance, autonomy, and purpose. All of those are real. But Yeager’s framing highlights something more primal: for adolescents, motivation is intensely social. They are wired to care about how they’re seen. They want to matter to people whose opinions they value.

In that light, motivation isn’t just about drive—it’s about social reward. If a learning activity helps you become someone of value in your social group—if it earns you the respect and belonging you crave—you’ll throw yourself into it. If it doesn’t, even the most thoughtfully scaffolded experience can fall flat. This is why motivation is so uneven across contexts. The same kid who’s listless in geometry class might spend hours mastering intricate choreography, modding a video game, or grinding on a skateboard trick—because those activities earn them something socially.

This is why learning becomes powerful when it’s embedded in community. It’s not enough to give students choices or tailor the pace. They have to feel that the work they’re doing helps them become someone of value to the people around them.

The myth that self-directed learning is only for a select few

A few years ago, I remember reading a critique of personalized learning that went something like this: “It works for autodidacts like Bill Gates and Mark Zuckerberg—but most kids aren’t like that.” The idea was that personalized learning assumes a level of self-direction that most students don’t have.

At the time, that critique gave me pause. But the more I’ve observed students in and out of school, the more convinced I’ve become that it’s based on a faulty assumption. Most kids are autodidacts—they just don’t learn school subjects autodidactically.

Watch what happens when a kid gets into Minecraft. Or basketball. Or Taylor Swift. Or anime. Or makeup tutorials. Or skateboarding. Or modding computers. Kids teach themselves astonishingly complex things through trial, error, iteration, and YouTube. They’ll memorize elaborate lore, master mechanics, mimic skills, and explain them to others—all without being assigned a single worksheet.

So, the problem isn’t that most kids aren’t self-directed learners. The problem is that school content often lacks any social payoff. It doesn’t help them feel valued or earn respect in the social contexts they care about. And so, understandably, they disengage.

Derek Muller, creator of the science YouTube channel Veritasium, powerfully made this point during a recent talk. After showing one of his classic street interview segments—where he asks everyday people basic science questions that many fumble to answer—he surprised the audience by pushing back against their laughter.

He said, “Our brains are designed to help us be effective in this world, which means finding food and shelter, finding a mate, integrating socially so that we’re not ostracized, being able just to hang out and have fun. All of those things are what we should be doing.” In other words, humans are wired to focus on social survival. Muller acknowledged that while scientific literacy is essential, it’s understandable that many people don’t prioritize it. They’re busy navigating social media, relationships, and real-world concerns.

“I think it’s understandable,” he said, “that a lot of people don’t focus on that, don’t know that, don’t think about it. It’s not part of the world that they exist in… because that’s about connecting with other people.”

His point aligns with Yeager’s: for most people, and especially for adolescents, the drive to connect socially is far more pressing than mastering academic content. When school doesn’t align with those social realities, it often gets deprioritized.

Why traditional schools don’t motivate most learners

That insight sheds light on why the conventional grammar of schooling fails so many students. Schools typically offer only a few narrow paths to earn status and respect: academics, athletics, and sometimes leadership roles like Associated Student Body (ASB) or student council. If you happen to be good at one of those, great—you’re in the game. But if you’re not? You’re mainly on the sidelines.

What’s worse, we’ve layered on a college-for-all narrative that subtly devalues other sources of status and contribution. Getting a job, starting a business, apprenticing, or helping your family—these are often treated as second-tier outcomes. That means for students who struggle with academic content or don’t shine in the field or on the court, school offers no credible path to mattering.

And when students can’t earn status or respect through the formal structures of school, they create alternative hierarchies. Cliques, social tribes, and student-defined status games become ways to navigate identity and belonging. Adults may not sanction these, but they serve the same deep purpose: to help students figure out where they belong and how to be valued.

To make matters worse, school structures are often explicitly ranked. In band, you’re ranked by chair. In sports, the starting lineup. In academics, by GPA and class rank. These rankings don’t just provide feedback—they confer identity. They divide students into winners and losers in the domains the school has decided to prize. That further fuels the search for alternative ways to feel successful.

So, students turn to trendiness, humor, rebellion, or other creative ways of being “seen” by peers. They may not be aiming for status in the honor roll sense, but they’re absolutely trying to be someone of value in the eyes of others.

Why AI can’t fill the gap

Enter AI. Over the past year, we’ve seen a surge of interest in how AI can personalize learning, adapt to student needs, and tutor across subjects. It can also help students design their learning journeys, generate creative ideas, or receive iterative feedback on their work. There’s real promise here.

But if we mistake AI for a complete solution to the motivation problem, we’ll be sorely disappointed.

AI can’t confer status or respect. And that matters more than most people realize.

Here’s why: human respect is scarce. People only have so much time, attention, and emotional bandwidth. They must be selective about where they invest their time, who they include, and what kind of contributions they recognize, especially in collaborative or competitive group settings. That selectivity is what gives human respect its meaning.

By contrast, AI is infinitely available and unconditionally responsive. You can talk to it all day, and it will always be supportive. But that’s precisely what makes its feedback meaningless regarding recognition and belonging. An AI doesn’t have limited time, bandwidth, and resources that force it to make tradeoffs. AIs don’t choose us to be on their teams or decide that relationships with us are worth their time. 

Unless AI technology evolves to a point where it plays a meaningful role in real social systems, where time, attention, and affirmation are scarce and must be earned, it will fall short of solving the motivational challenge. We may be approaching that kind of world—a scenario my colleague Julia Freeland Fisher warns about, where AI begins to substitute for the human relationships essential to development and belonging. But if it ever does reach that point, we’ll be living in a very different sci-fi-esque world where the rules of human connection are being redefined, and where questions about whether AI can motivate students may be the least of our concerns.

This lens might help explain what researchers call edtech’s “5% problem.” Studies have found that while many online learning programs can produce significant gains when used at recommended dosages, only about 5% of students actually use them as recommended. Why? Researchers have offered a range of explanations for this gap, from inconsistent implementation by schools to the possibility that higher-achieving students are simply more likely to stick with the programs. Of course, structural barriers—like scheduling, accountability policies, and tech access—also play a role in why usage remains low. Nonetheless, I hypothesize that motivation is the missing piece that can offer the most significant impact. These tools may be engaging and enjoyable at first, but their novelty wears off quickly. Compared to apps and games designed purely for entertainment, edtech rarely wins the attention battle on its own.

What keeps students coming back, I believe, isn’t just better software. It’s the social context around the learning. If students saw working hard in these programs as something that earned them status and respect—something that made them matter in the eyes of their peers, teachers, and parents—I think we’d see far more students using the software at levels that accelerate their achievement. Yet I suspect many teachers are disinclined to make software usage a major mechanism for conferring status and respect in their classrooms because encouraging more screen time doesn’t feel like real teaching.

This motivational lens could also help explain why high-dosage tutoring tends to outperform self-paced software, even when both offer personalized learning. Software might be better at diagnosing and targeting academic gaps. A tutor might be better at connecting learning to a student’s interests. But the biggest difference may be the power of relationships. Most students care, at least a little, about whether their tutor respects them. That creates a subtle but powerful lever: a human relationship where affirmation must be earned, not automatically given. That dynamic—the inherent scarcity of attention and regard—gives human interaction its motivational force. And it’s what software alone can’t replicate.

The real design challenge: Build full-stack motivational contexts

For decades now, improving student achievement has been one of the most persistent imperatives in education. Billions of dollars and countless reforms have gone toward efforts to raise test scores and close achievement gaps. And yet, across the board, progress has been modest and inconsistent.

Over a decade of Gallup polling, as well as research cited in Jenny Anderson and Rebecca Winthrop’s new book The Disengaged Teen, highlight a fundamental problem: most students, especially in middle and high school, are disengaged from school. And if they’re disengaged, they’re not motivated to learn. We’ve tried improving curricula. We’ve focused on teacher preparation and professional development. We’ve reformed accountability systems. But maybe what we’ve been missing all along is the fuel that makes any of those efforts matter: students’ motivation.

Motivation isn’t just another variable in the equation—it could be the multiplier. I believe unlocking student motivation could prove more powerful for boosting achievement than any levers we’ve traditionally tried, like dumping jet fuel on a campfire. None of this is to say content doesn’t matter—good curriculum is essential. But without the motivational fuel to engage with it, even the best-designed content can fall flat.

We already have examples to show that when students are motivated, the results can be explosive. At Alpha School in Austin, Texas, students complete core academic subjects in just two hours per day—and yet their academic growth rates are more than double national averages. At Khan World School, a partnership between ASU Prep and Khan Academy, students have shown learning growth as high as 2 to 5 times national norms in math, reading, and language arts. These are not marginal improvements. They are step-function leaps. 

Technology plays a key role in these models, allowing students to move at their own pace, get real-time feedback, and explore personalized learning paths. But I don’t believe it’s the technology alone that drives these outcomes. It’s the way the technology enables these schools to redesign the social learning experience—to create environments where students see learning as something that makes them more valuable contributors in the social worlds they inhabit.

That’s the deeper design challenge. It’s not just about deploying the right tools—it’s about constructing a social context where learning matters. That raises an important question: Is social motivation primarily from the school in schools like Alpha and Khan World School? Or is it relying heavily on the support structures, relationships, and cultural norms students bring from outside, especially from their families?

If it’s the latter, then scaling those results may prove difficult. But if it’s the former—if the school is generating the motivational context—then the real opportunity lies in figuring out how. How do we replicate or adapt those dynamics so more students, from more backgrounds, experience school as a place where learning leads to social value?

Answering that question could be the key to reimagining school not just as a place where learning happens, but as a place where motivation flourishes—and achievement follows.

A new lens on motivation

We’ve spent years trying to personalize learning. But maybe we’ve focused too much on tailoring content and not enough on transforming context.

The real key to motivation isn’t just autonomy or relevance. It’s the opportunity to earn status and respect, to be valued by people who matter, and to contribute meaningfully to a group. That’s what most school systems fail to provide. And that’s what AI, for all its brilliance, can’t replicate on its own.

Most kids are already autodidacts. Our job isn’t to teach them how to learn. It’s to create environments where learning helps them become someone in a world that sees and values them for it.

The next big educational innovation won’t come from more intelligent AI. It will come from communities that design AI-powered education where learning earns you respect in the real world.

For more news on AI in the classroom, visit eSN’s Digital Learning hub.

Thomas Arnett, Clayton Christensen Institute

Thomas Arnett is a senior research fellow for the Clayton Christensen Institute. His work focuses on using the Theory of Disruptive Innovation to study innovative instructional models and their potential to scale student-centered learning in K–12 education. He also studies demand for innovative resources and practices across the K–12 education system using the Jobs to Be Done Theory.

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This story was originally published by Chalkbeat. Sign up for their newsletters at ckbe.at/newsletters.

As AI dominates the education zeitgeist, I think it’s time to highlight an effective teaching tool as tried and true as a Ticonderoga No. 2 pencil: high-quality IRL relationships with students.

I learned this first–and best–from my high school Spanish teacher, who, as the matriarch of the Spanish department, went simply by “Señora.”

Señora started class with “Qué hay de nuevo?” (What’s new?) She learned what had happened at our most recent swim meet, band concert, or football game. She laughed as she gently unearthed the chisme (gossip). I loved this part of class. It seemed so useful, like I was actually learning to speak Spanish, and it involved one of my favorite things, talking.

Years later, when I became a high school Spanish teacher, I understood Señora was up to something deeper than building conversational Spanish skills.

She held court over those sessions, sitting atop her tall stool, sipping coffee from the mug with two of her favorite sayings, “Es mi mundo” (It’s my world) and “Todo es posible, nada es seguro” (Everything is possible, nothing is certain). All the while, she was busy taking mental notes on which students were uncharacteristically quiet, who hadn’t slept, who wasn’t OK.

We also learned Spanish. I remember my brother telling me how he announced to Señora’s whole class that he had taken a shower with a Japanese man, confusing Japón with jabón, the word for soap. That lesson stuck.

Some of my favorite class periods involved the slide projector. On those days, we would join Señora on her travels across Latin America, like the time she navigated border crossings as the official interpreter for an Airstream caravan tour. We’d listen, rapt, about her trip to Machu Picchu during which–you won’t believe it–she slept in the ruins. From our desks in a random Wisconsin public high school classroom, we gallivanted through Amazon waterfalls.

Senior year, having exhausted our school system’s Spanish progression, two friends and I asked Señora to teach us “Spanish 6.” Without pausing, she said “¡Sí!”, voluntarily giving up her planning period for us.

Only after becoming a teacher did I recognize Señora’s level of sacrifice.

Each day, fourth hour, we’d sit in a fishbowl cubicle in the library. On Wednesdays, we worked from the “Book of Questions,” asking and responding entirely en español. I learned Señora’s opinion about soulmates (good for frolicking through the aforementioned Amazon waterfalls, not for unloading the dishwasher or changing diapers), tattoos (always regrettable), and travel (¡por supuesto!).

Señora was ever present with us, existing in the physical world of pencils and people and spending exactly zero momentos chasing ed tech hacks that would “transform” her teaching. Her ace was building relationships with and among her students, deploying the original AI–Authentic Interactions–to forge real connections.

I was in college when cancer forced Señora to leave the profession prematurely.

More than half a dozen years into Señora’s illness, her classroom storage closet remained exactly as she left it. Por si acaso–just in case.

“For when she comes back,” the other Spanish teachers said.

I know because I got to visit it. By then, I was teaching Spanish. Señora knew she wouldn’t teach again, so we planned to meet at her classroom for the official baton pass when I was home for a visit.

But time and life being what they are, months passed before I made it home again. By then, Señora was bedridden. Señorita Jolley would let me in, she told me. And “take anything you’d like.”

I called a close friend who was also in town. I wanted una amiga with me.

In the time capsule closet, we sat amid Señora’s belongings. Piles of papers, decades of planners, and dozens of meticulously labeled manila folders with handwritten transparencies and photocopied, handwritten worksheets shared shelves with books, lesson plans, and slides. Crisscross applesauce on the closet’s green linoleum floor, we sifted through the layers.

I assembled original copies of her worksheets and transparencies, hoping the cursiveish font de Señora would buttress my confidence in a room full of discerning high school juniors. My friend assembled Señora’s books and posters of Spanish art. I added her 1998-99 planner to my stash, sentimentally marking our graduation year.

As we were leaving, I remember seeing her mug–the one with her favorite sayings on it–and I took that, too.

When I returned to my school to teach, I excavated a dust-covered overhead projector from a supplies closet. I literally typed up Señora’s worksheets and transparencies one by one. I could have Googled resources on the subjunctive mood or passive voice, but transcribing Señora’s work felt like the right thing to do. I didn’t wholesale avoid technology; I just recognized where my priorities as a real human teaching real humans should lie–with them.

I started class with “¿Qué hay de nuevo?” sipping tea from Señora’s mug while taking mental notes about who was uncharacteristically quiet, who hadn’t slept, who wasn’t OK. And I followed up with those students after class in a way no bot ever could.

Chalkbeat is a nonprofit news site covering educational change in public schools.

For more news teacher impact, visit eSN’s Innovative Teaching hub.

Becca Katz, Chalkbeat

Becca Katz is an educator, mother, friend, partner, nature-lover, writer, activist, and edupreneur. She co-founded Good Natured Learning to grow educators’ capacity to integrate nature connections into their routine teaching practices. Becca writes Learning, by Nature, a Substack about all things nature.

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Generative artificial intelligence is everywhere, including our schools–and it has the potential to transform education. These platforms offer unprecedented opportunities to personalize learning, refine curriculum development, and support teaching. But as AI adoption accelerates, one critical question remains: Will AI strengthen learning, or will it undermine it?

The future of AI in education must be a shared effort across curriculum providers, district and school leaders, and policymakers. We cannot allow the edtech fervor that peaked during the pandemic to overtake the possibilities afforded by these novel technologies: too much focus on the “tech” at the expense of the “ed.” This AI era is not solely about technological advancement–it’s about ensuring that innovation serves the needs of teachers and learners.

Without careful guardrails, introducing and implementing AI into classrooms risks undermining the very foundation of effective learning. Many AI models are built for general use and later “adapted” for education, but this retrofitting approach can introduce bias, misinformation, and misalignment with instructional goals. If AI is not purpose-built for education, it can amplify instructional inconsistency, widen learning gaps, and erode teacher trust.

AI must align with the science of learning, or it will diminish the technology’s impact on students overall.

AI is changing education–HQIM must be the anchor

Over the past decade, efforts to implement High-Quality Instructional Materials (HQIM) and evidence-based strategies in K-12 schools have begun to deliver measurable improvements in student learning. The “Mississippi Miracle”–a term used to describe the state’s dramatic gains in literacy–and promising 2024 NAEP results in Louisiana, Philadelphia, and Los Angeles illustrate that a commitment to research-backed curriculum can help narrow the achievement gap, though it requires investment and focused effort.

Research from the Johns Hopkins Institute for Education Policy found that access to high-quality instructional materials has a direct impact on student achievement, particularly for historically underserved populations. These curricula support teachers with proven lessons that are created with intention and a clear purpose. Yet, even when states and districts adopt HQIM, teachers continue to supplement the chosen curriculum with external resources–often unvetted, disparate, or misaligned.

Studies show that teachers spend 7-12 hours per week searching for or creating instructional materials, pulling from state websites, teacher-created worksheets, and free or purchased digital content. This mix-and-match approach can result in inconsistent pedagogy, gaps in standards coverage, and lost instructional time. The introduction of AI-powered solutions may also introduce inconsistency or misalignment.

There is growing enthusiasm for using AI to better support teachers–a promising shift that could save educators time and enhance student engagement. According to a 2023 RAND Corporation study, the top five AI use cases teachers identified would help them manage many of the more mundane and time-consuming aspects of their work:

• Supporting students with learning differences
• Generating quizzes and assessments
• Adjusting content to be an appropriate grade level for students
• Generating lesson plans
• Generating assignments (e.g., worksheet materials)

However, most AI tools in education today are not designed with curriculum integrity in mind. The result? Teachers may unknowingly generate content that conflicts with their district’s curriculum, eroding the consistency and effectiveness of HQIM, and unintentionally widening learning gaps rather than closing them.

Without proper safeguards, AI-generated materials could amplify, rather than reduce, instructional inconsistencies–requiring educators to spend even more time reviewing and correcting misaligned content.

To ensure AI strengthens, rather than undermines, effective teaching and learning, education leaders are demanding that implementations are grounded in trusted, research-backed curriculums and aligned with state standards and district priorities. As an example, in its Fall 2024 guidance, the Louisiana Department of Education stated: “Integrating AI technologies should maintain the integrity of high-quality instructional materials used for instruction.”

This underscores a key reality: Education leaders want AI to enhance their chosen curricula, not replace them. Districts and schools invest time and money to identify instructional materials that align with their standards and preferred pedagogical approach, and fit best with what their community, teachers, and students need. They spend valuable time training and supporting teachers to ensure the curriculum will deliver the outcomes they intend. AI-generated lesson plans, practice activities, and instructional recommendations must reinforce the effective implementation of HQIM. By ensuring AI is curriculum-informed, districts can unlock AI’s potential without sacrificing instructional quality.

To benefit K-12 teaching and learning, it is essential that we find ways to propel innovation while minimizing risk. A solid approach to responsible AI ensures generated content is safe, accurate, and academically sound.  Such an approach leverages trusted, research-backed HQIM and employs rigorous vetting for safety, accuracy, ethics, and academic integrity.

This combination helps educators minimize the risks of irrelevant content, misinformation, and bias, and aligns AI outputs with educational goals. And students benefit from a secure, controlled environment where they can safely engage with powerful AI tools and content. This also enables us to safeguard the intellectual property of our curricula and the IP of others, such as trade publishers, authors, and artists, from whom we license content, while maintaining our educational content’s integrity.

AI can help, but only if its use is intentional and aligned with curriculum. Time savings must not come at the expense of instructional integrity, and personalization must keep students on track, not lead them away from core learning objectives. Above all, educators must be empowered when they use AI-driven content, not further overwhelmed.

We are at a pivotal moment. The question is no longer whether AI will influence classrooms. It’s how we ensure it does so in service of teachers, students, and the standards that matter most. Because thoughtful design–not novelty–is what separates tools that deepen learning from those that distract from it.

The future of AI in education won’t be determined by algorithms or innovation alone. It will be shaped by the values we embed in the systems we build and the choices we make today. The opportunity is here. Let’s build AI that not only learns–it understands what learning really takes.

Sari Factor, Imagine Learning

Sari Factor is the Vice Chairman and Chief Strategy Officer at Imagine Learning.

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A year ago, I saw artificial intelligence as a shortcut to avoid deep thinking. Now, I use it to teach thinking itself.

Like many educators, I initially viewed artificial intelligence as a threat—an easy escape from rigorous analysis. But banning AI outright became a losing battle. This semester, I took a different approach: I brought it into my classroom, not as a crutch, but as an object of study. The results surprised me.

For the first time this spring, my students are not just using AI—they are reflecting on it. AI is not simply a tool; it is a mirror, exposing biases, revealing gaps in knowledge and reshaping students’ interpretive instincts. In the same way a river carves its course through stone—not by force, but by persistence—this deliberate engagement with AI has begun to alter how students approach analysis, nuance and complexity.

Rather than rendering students passive consumers of information, AI—when engaged critically—becomes a tool for sharpening analytical skills. Instead of simply producing answers, it provokes new questions. It exposes biases, forces students to reconsider assumptions and ultimately strengthens their ability to think deeply.

Yet too often, universities are focused on controlling AI rather than understanding it. Policies around AI in higher education often default to detection and enforcement, treating the technology as a problem to be contained. But this framing misses the point. The question in 2025 is not whether to use AI, but how to use it in ways that deepen, rather than dilute, learning.

AI as a Tool for Deep Engagement

This semester I’ve asked students to use AI in my seminar on Holocaust survivor testimony. At first glance, using AI to analyze these deeply human narratives seems contradictory—almost irreverent. Survivor testimony resists coherence. It is shaped by silences, contradictions and emotional truths that defy categorization. How can an AI trained on probabilities and patterns engage with stories shaped by trauma, loss and the fragility of memory?

And yet, that is precisely why I have made AI a central component of the course—not as a shortcut to comprehension, but as a challenge to it. Each week, my students use AI to transcribe, summarize and identify patterns in testimonies. But rather than treating AI’s responses as authoritative, they interrogate them. They see how AI stumbles over inconsistencies, how it misreads hesitation as omission, how it resists the fragmentation that defines survivor accounts. And in observing that resistance, something unexpected happens: students develop a deeper awareness of what it means to listen, to interpret, to bear witness.

AI’s sleek outputs conceal a deeper problem: It is not neutral. Its responses are shaped by the biases embedded in its training data, and by its relentless pursuit of coherence—even at the expense of accuracy. An algorithm will iron out inconsistencies in testimony, not because they are unimportant, but because it is designed to prioritize seamlessness over contradiction, clarity over ambiguity. But testimony is ambiguity. Memory thrives on contradiction. If left unchecked, AI’s tendency to smooth out rough edges risks erasing precisely what makes survivor narratives so powerful: their rawness, their hesitations, their refusal to conform to a clean, digestible version of history.

For educators, the question is not just how to use AI but how to resist its seductions. How do we ensure that students scrutinize AI rather than accept its outputs at face value? How do we teach them to use AI as a lens rather than a crutch? The answer lies in making AI itself an object of inquiry—pushing students to examine its failures, to challenge its confident misreadings. AI does not replace critical thinking; it demands it.

AI as Productive Friction

If AI distorts, misinterprets and overreaches, why use it at all? The easy answer would be to reject it—to bar it from the classroom, to treat it as a contaminant rather than a tool. But that would be a mistake. AI is here to stay, and higher education has a choice: either leave students to navigate its limitations on their own or make those limitations part of their education.

Rather than treating AI’s flaws as a reason for exclusion, I see them as opportunities. In my classroom, AI-generated responses are not definitive answers but objects of critique—imperfect, provisional and open to challenge. By engaging with AI critically, students learn not just from it, but about it. They see how AI struggles with ambiguity, how its summaries can be reductive, how its confidence often exceeds its accuracy. In doing so, they sharpen the very skills AI cannot replicate: skepticism, interpretation and the ability to challenge received knowledge.

This approach aligns with Marc Watkins’s observation that “learning requires friction.” AI can be a force of productive friction in the classroom. Education is not about seamlessness; it is about struggle, revision and resistance.

Teaching history—and especially the history of genocide and mass violence—often feels like standing on a threshold: one foot planted in the past, the other stepping into an uncertain future. In this space, AI does not replace the act of interpretation; it compels us to ask what it means to carry memory forward.

Used thoughtfully, AI does not erode intellectual inquiry—it deepens it. If engaged wisely, it sharpens—rather than replaces—the very skills that make us human.

Jan Burzlaff is a postdoctoral associate in the Jewish Studies program at Cornell University.

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