Tag: education

The Problem with The Anxious Generation — and What the “Ban All Screens” Movement Gets Wrong About Education

There is a real crisis in children’s mental health. I believe this because I see it daily in the schools I work in. The data supports it. The children themselves are telling us. On this point, Jonathan Haidt and I agree completely.

Where I part ways — and where I think the current panic about technology in schools is leading us somewhere counterproductive — is on the question of cause. And because cause determines response, getting this wrong has real consequences for real kids.

Let me be direct: The Anxious Generation is a compelling, well-written, emotionally resonant book built on a scientific case that is significantly weaker than Haidt presents it. The conclusions it has inspired in education policy are, in many cases, the wrong conclusions drawn from the wrong diagnosis — and I think educators and parents deserve a more honest accounting of where the evidence actually stands.

What Haidt Gets Right

Before the criticism, the credit.

Haidt is correct that something has gone badly wrong with childhood and adolescent wellbeing. He’s correct that overprotective parenting and the decline of play-based, independent childhood are serious problems. His advocacy for letting children take risks, experience failure, and develop resilience outside adult supervision — what he calls “antifragile” development — aligns closely with Peter Gray’s research and with what I see as an instructional coach working with students every day.

He’s also correct that smartphones and social media are not neutral tools for developing adolescents. The attention-capture dynamics, the social comparison mechanisms, the algorithmic amplification of outrage and anxiety — these are real design features with real effects. None of that is made up.

The problem is what he does with these legitimate observations. He builds an enormous causal argument on a foundation that the researchers who actually study this area largely reject.

The Scientific Case Against the Thesis

Candice Odgers, a developmental psychologist at UC Irvine, put it plainly in a review published in Nature: “The book’s repeated suggestion that digital technologies are rewiring our children’s brains and causing an epidemic of mental illness is not supported by science.” She added that the “bold proposal that social media is to blame might distract us from effectively responding to the real causes of the current mental-health crisis in young people.”

This isn’t one dissenting voice. The critics are numerous and credentialed. Andrew Przybylski, a professor of human behavior and technology at Oxford, describes Haidt’s approach as “vote counting” — prioritizing quantity of studies over quality, accumulating a long list of weak evidence and presenting it as a compelling case. Christopher Ferguson, a psychology professor at Stetson University who has studied media effects for decades, has pointed out that older adults in the US have experienced worse mental health deterioration than teenagers — which raises an obvious question: why would social media, used most heavily by the young, be causing problems worst in those who use it least?

One critical review examined the actual statistical rigor of the key studies Haidt relies on and found them wanting: “The book is over 400 pages long and waxes lyrical about the spiritual degradation we sustain as a result of social media… I would not have the nerve to write a several hundred page book calling for significant government intervention while summoning only five pages of statistical evidence. To make matters worse, the evidence is weak. The data quality is poor, the studies are flawed, and researchers are divided.”

The studies themselves have serious methodological problems. Many don’t study actual depressed teenage girls or heavy social media users — they study mostly adults, mostly average users, without serious psychological issues. You cannot establish the effect of heavy social media use on teenage depression unless you actually study heavy social media users who are depressed. Most of the studies Haidt cites don’t come close to that standard.

The Pattern I Keep Seeing

I grew up in the 80s and 90s. My generation was going to be ruined by television and video games. We were rotting our brains, becoming socially isolated, losing the capacity for deep attention and real connection. Parents panicked. Legislators proposed restrictions. Books were written explaining the neurological catastrophe underway.

Before my generation, it was comic books. Before that, rock music. Before that — and this is the one I find most useful to remember — novels. In the 18th and 19th centuries, novels were genuinely considered a moral hazard for young people, particularly young women. The idea that you would sit alone for hours, absorbed in a fictional world, engaging your imagination in ways that couldn’t be supervised or directed — this was seen as dangerous. Corrupting. The kind of thing that led to hysteria and bad decisions.

Every generation has a technological panic. The technology changes. The structure of the panic doesn’t. And the panic is always most persuasive to the people who didn’t grow up with the thing being panicked about. Ferguson draws a direct comparison to Seduction of the Innocent, the 1954 bestseller by psychiatrist Fredric Wertham that declared comic books had created a wave of juvenile delinquency — a book that caused enormous policy consequences before the evidence caught up with the panic.

I’m not saying social media is fine. I’m saying we’ve been here before, and the track record of these panics — as predictors of actual causal harm — is not good. The TV and video game generation didn’t turn out markedly worse than the generations before it. The novel-reading generation produced the Enlightenment.

What changes in each iteration is which thing we’ve decided is uniquely, irreversibly corrupting the youth. What doesn’t change is the confidence with which we assert it, the weakness of the actual evidence, and the policy consequences that follow before the evidence is properly interrogated.

What’s Actually Happening in Schools Right Now

The policy landscape has shifted fast. As of early 2026, some state legislators and witnesses have suggested banning 1:1 device programs in schools entirely, with calls for younger students to return to analog learning with pencil and paper. The Distraction-Free Schools Policy Project developed model legislation that would prohibit all screen technology in grades K-5 and ban school technology using generative AI at every grade level.

Parents across the country are forming networks teaching one another how to opt their children out of school-issued Chromebooks and iPads. One parent in California described pulling her children off school-issued devices as an “analog education” — framing it as a victory.

I understand the impulse. I genuinely do. Screen time management is a real issue. Distraction in the classroom is real. The feeling that technology has gotten away from us and we need to reclaim something is legitimate.

But the leap from “smartphones in pockets during class are a distraction” to “all screens in learning environments are harmful and we should return to pencil and paper” is enormous — and it’s a leap that the evidence doesn’t support.

Easier classroom management is not the same as better learning. And limiting students to pen and paper does little to prepare them for a world in which thinking, writing, and collaboration increasingly happen through digital tools.

There’s also an equity issue that gets papered over in these conversations. The children of affluent parents who are choosing analog education for their kids will still encounter a fully digital professional world. They’ll learn to navigate it eventually — at home, through tutors, through the social capital their families provide. The students who most need schools to close the digital literacy gap are the ones who will lose the most if we strip that from their education.

The Right Diagnosis, the Wrong Villain

Here’s what I think is actually happening, and why Gray’s framework matters more than Haidt’s for understanding it.

The mental health crisis in children is real and has been building since roughly the 1950s — decades before smartphones, social media, or the internet. Gray’s longitudinal data makes this undeniable. The primary driver, in Gray’s reading, is the progressive elimination of children’s independent, unstructured time: the reduction of recess, the increase in adult supervision, the overscheduling of childhood, the cultural shift toward treating independent children as negligent parenting.

Smartphones accelerated some of these dynamics and added new ones. But they arrived into a childhood that was already significantly impoverished of independent developmental experience. Children who have no free time, no unstructured outdoor play, no practice at self-regulation and conflict resolution — those children are developmentally primed for anxiety. Of course they reach for the nearest source of stimulation, connection, and escape. Of course the smartphone fills the vacuum.

The phone is a symptom as much as a cause. Taking the phone without restoring what the phone replaced is treating the symptom.

This is why I find the pencil-and-paper movement in education so frustrating. It’s addressing the wrong variable. A student who sits at a desk for six hours a day, goes home to an overscheduled afternoon of structured activities, and has never had two consecutive hours of genuinely unstructured time is not going to develop resilience because their school gave them a pencil instead of a Chromebook. The problem runs deeper than the device.

What Schools Should Actually Do

This is where I land, after years in the classroom and coaching teachers, watching students, and reading the research:

Cellphones during instructional time are a legitimate problem. Personal smartphones in pockets during class are a distraction issue, not a technology issue. Addressing that specifically — with clear policies, consistently enforced — is reasonable and has some evidence behind it.

1:1 device programs deserve scrutiny, but not abolition. The question isn’t whether devices belong in schools. It’s whether the learning design built around devices is pedagogically sound. The problem was never laptops. The real issue is the learning model we built around laptops. Bad technology implementation is a professional development and curriculum problem, not a technology problem.

The equity argument matters. Any policy that removes digital tools from schools disproportionately disadvantages students whose families can’t provide those tools and experiences at home.

Unstructured time is the real deficit. If we genuinely want to address the root causes of the mental health crisis Gray’s research describes, we need to give children back their unstructured time — at school and at home. More recess. Fewer scheduled activities. More space for boredom, conflict, and self-direction. That’s the intervention the data supports.

Teaching students to use technology critically is education, not capitulation. We live in a world saturated with algorithms designed to capture attention. The answer is not to pretend that world doesn’t exist or to seal children off from it until they turn 16 and then release them into it untrained. The answer is to help students develop the critical capacities to navigate it. That’s what education is for.

A Final Thought on Haidt

I’m not saying don’t read The Anxious Generation. It’s a book worth engaging with, and the parts of it that align with Gray’s research on free play and independent childhood are genuinely valuable. Haidt is a smart person thinking hard about a real problem.

But read it skeptically. Read the critics. Notice how much of the emotional weight of the book rests on anecdote and moral argument rather than the statistical case. Notice that the researchers who spend their careers studying this specific question — screen time and adolescent mental health — largely disagree with his conclusions.

And notice, most importantly, what the book makes it easy to avoid thinking about: the choices adults make about how to structure children’s time, how to design schools, how to build neighborhoods, how to value childhood independence. Those are harder conversations because they implicate us directly. Blaming the phone is easier. It usually is.

Further Reading

Free to Learn by Peter Gray — Start here. Gray’s full argument, written for a general audience, is rooted in decades of evolutionary psychology research. More compelling, better supported, and more actionable than anything else on this list.

Growing Up in Public: Coming of Age in a Digital World by Devorah Heitner — Published in 2023, this is the most current and most practically useful book on kids and technology that I’ve found. Heitner, a former media studies professor with a PhD from Northwestern, explicitly rejects the fear-based framing that dominates this conversation. Her core argument: the answer is mentoring, not monitoring. She draws on hundreds of interviews with kids, parents, and educators rather than extrapolating from weak correlational studies. A direct and well-earned counterweight to Haidt.

The Anxious Generation by Jonathan Haidt — Read it. Engage with the parts that align with Gray’s research on play deprivation. Push back hard on the causal claims about smartphones. It’s worth reading because it’s driving policy — and understanding the argument you’re pushing back against requires having read it.

Reclaiming Conversation by Sherry Turkle — Turkle is an MIT sociologist who has spent decades doing actual long-form qualitative research with students and families about technology and attention. More careful than Haidt, more specific about the mechanisms, and more interested in nuance than in producing a villain. Published in 2016, but holds up.

How Children Learn by John Holt — First published in 1967. Holt sat in classrooms, observed children learning — or not learning — and drew conclusions that the education system has ignored ever since. Gray cites him approvingly. The arguments about how children develop intrinsic motivation, curiosity, and self-direction are as relevant now as they were sixty years ago, possibly more so.


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Yes, We Need to Get Rid of AP Courses

classmates doing studies for exam together
Photo by Armin Rimoldi on Pexels.com

And the College Board’s recent score inflation just made the argument stronger.

There, I said it. Let me make the case.

I’ve worked in public education long enough to watch AP courses go from a program for genuinely advanced students to a college admissions arms race to, now, something so thoroughly gamed by the College Board itself that universities are quietly questioning whether AP scores mean anything at all. We have spent the better part of two decades pushing AP as an equity solution — offering the best, most rigorous content to every student, regardless of background. That framing is correct. The vehicle we chose to deliver it is wrong.

Let me explain why, and what we should do instead.

The Equity Problem Has Never Been Solved

The original argument for expanding AP access was simple and appealing: if we give more students — low-income students, students of color, first-generation college students — access to rigorous coursework, we close the opportunity gap. More challenge equals better preparation equals better outcomes.

The data has never supported this at scale. A 2023 New York Times investigation found that roughly 60 percent of AP exams taken by low-income students scored too low for college credit — a 1 or 2 out of 5 — and that this number has barely moved in twenty years. Two decades of expanded access. Same failure rate. That’s not a pipeline problem. That’s a systemic problem with the model.

The barriers are layered and often invisible. Nationally, about 30 percent of Black and Hispanic students enrolled in AP courses never take the corresponding exam at all, compared to roughly 15 percent of Asian students. The reasons aren’t mysterious: scheduling conflicts, unofficial prerequisites, being steered toward “more appropriate” classes by counselors who read demographics rather than ability. Getting into the course doesn’t mean the course is actually accessible — or that success in it is equitably distributed.

This is the AP equity promise: a credential that most of the students it’s supposed to serve can’t access in any meaningful way.

The College Board’s Response: Change the Score, Not the System

Here’s where the story gets genuinely infuriating. After that NYT investigation put the failure rates for low-income and minority students into the national conversation, the College Board didn’t redesign courses, improve teacher training, or address structural barriers to preparation. They changed the scoring.

In 2022, the College Board quietly introduced what it calls “Evidence-Based Standard Setting” — a new methodology for scoring its most popular AP exams. The results were extraordinary, in the worst possible way.

AP U.S. History: students earning 4s and 5s jumped from 25 percent in 2023 to 46 percent in 2024. AP U.S. Government and Politics: top scores leapt from 24 percent to 49 percent in a single year. AP English Literature’s pass rate went from 44 percent in 2021 to 78 percent in 2022, the first year EBSS was applied.

Were students suddenly twice as prepared? Were teachers twice as effective? Did something happen in American high schools that would justify this kind of jump in a single year — while NAEP scores in 8th grade math and reading continued to decline and PISA scores showed stagnation or decline for American 15-year-olds?

No. The College Board changed the scoring system under pressure, and more students passed because passing got easier.

The financial context matters here. In 2024, over 86 percent of College Board revenue came from fees — nearly half of that from the basic AP exam fee alone. More than 1.3 million students paid $99 per exam for over 4.8 million AP exams in 2025. Total revenues exceeded $1.17 billion, and the organization held reserves of over $2 billion. The CEO received $2.3 million in total compensation in 2024 — comparable to the president of Stanford, whose institution operates on a budget roughly ten times larger.

The College Board has a direct financial incentive to keep AP attractive to students. If competitors like dual enrollment are growing, AP scores need to look competitive. The solution they chose wasn’t to improve the product. It was to make the grades better. Some elite universities are now quietly developing their own assessments to supplement AP data, having lost confidence in what AP scores actually signal.

What AP Courses Actually Do — and Don’t Do

Here’s the core problem, and it isn’t really about the College Board’s financial incentives, though those matter. It’s about what AP courses were designed to accomplish and what we’ve asked them to do instead.

AP courses were designed as an exam-prep system. The course exists to prepare students for the AP test. The test exists so students can demonstrate college-level knowledge and potentially earn college credit. That’s the whole loop. There’s nothing in that loop about authentic inquiry, personalized learning, or developing the kind of curiosity and self-direction that actually prepares people for college and life.

I’ve seen good teachers do extraordinary things inside AP courses. The structure doesn’t prevent great teaching — it just doesn’t require it, reward it, or build toward it. What it requires is covering the material on the exam. And teachers in underfunded schools, with overcrowded classrooms, serving students who haven’t had the preparation advantages their suburban peers have had, are left trying to jam college-level content into students who are already behind — while the clock ticks toward the May exam.

This is what we’ve decided counts as equity.

No one takes an AP course because it sounds exciting. Students take it because they need the credential, the weighted GPA boost, or the college credit — in roughly that order of priority. The course has become a box to check in a game nobody designed for the students who need the most from their education.

The Alternative That’s Already Working

Here’s what the advocates of the current system don’t want to talk about: dual enrollment is quietly eating AP’s lunch, and for good reason.

Dual enrollment allows high school students to take actual college courses — usually through community colleges or state universities — and earn real, transferable college credits before they graduate. Not maybe-credits that depend on a May exam score. Actual college credits that appear on an actual college transcript.

The numbers tell the story. In the 2024-25 school year, an estimated 2.8 million high school students were enrolled in dual enrollment courses — up from 2.5 million just two years earlier. Ninety percent of U.S. high schools now offer dual enrollment as of 2026. Studies consistently show that dual enrollment students are more likely to complete a bachelor’s degree, and the effect is particularly pronounced for first-generation college students.

Dual enrollment has real limitations. Quality varies by institution. Credit transfer isn’t guaranteed everywhere, particularly at highly selective universities. Some rural districts struggle with access to college partners. These are real problems worth solving.

But the structural difference matters enormously: in dual enrollment, the credit is earned by doing the work, not by performing on a single high-stakes exam in May. For students who’ve struggled all year and finally understood the material in April, AP rewards the exam. Dual enrollment rewards the semester.

What I Actually Want

I’m not just interested in replacing one credential with another. The deeper argument isn’t that dual enrollment is perfect — it’s that the entire framing of AP as an equity solution has distracted us from the real work.

The real work is redesigning Tier 1 instruction in every classroom for every student.

Not advanced placement for some. Not rigor for those who can access it through the right course label. Authentic, engaging, challenging learning environments for all students — where the goal isn’t coverage for an exam, but genuine intellectual development. Where teachers are supported and trained to create learning experiences that develop curiosity, critical thinking, and the capacity to learn independently. Where students who need more support get more support rather than being filtered into different tracks based on teacher recommendations and parental advocacy.

AP courses didn’t create tracking. But they reinforce it, give it a credential, and let us feel like we’ve addressed equity when the data says we haven’t.

As an instructional coach, I’ve watched schools celebrate expanding AP enrollment while the students enrolled in those courses received content coverage without the preparation, context, or support that would make it meaningful. The number of AP course offerings became a proxy for school quality. The number of students enrolled became a proxy for equity. The pass rates told a different story that nobody wanted to hear.

The College Board’s recent decision to fix that story by softening the scoring didn’t solve the problem. It made it harder to see.

The Hard Conversation

I know this argument is unpopular in certain circles. Parents who have watched their children use AP courses to build transcripts and earn college credit have real, concrete reasons to value the system. Teachers who’ve designed genuinely excellent AP courses have real, legitimate grievances with the suggestion that the whole structure should go.

I’m not saying those courses aren’t valuable. I’m saying the architecture around them — the College Board’s monopoly, the single high-stakes exam as the sole measure of learning, the financial incentives that led to score inflation, the equity promise that was never delivered — is worth being honest about.

We can do better. We should demand better. And the first step is being willing to say that a system that has failed its stated purpose for twenty years doesn’t deserve another twenty years of the benefit of the doubt.

Related on this site: The problem with The Anxious Generation and the “ban all screens” movement — a related argument about how education policy gets driven by compelling narratives rather than honest data.


The Eclectic Educator is a free resource for everyone passionate about education and creativity. If you enjoy the content and want to support the newsletter, consider becoming a paid subscriber. Your support helps keep the insights and inspiration coming!

The Best Books for Understanding AI — A Reading List for Educators and Curious Humans

elderly man thinking while looking at a chessboard
Photo by Pavel Danilyuk on Pexels.com

A quick note before the list: I’ve been living in this space for a while now — as an instructional coach, a Google Certified Innovator, a doctoral student, and someone who uses AI tools daily in my actual work. The books I’m recommending here are ones I’d press into the hands of a thoughtful educator or a curious non-technical reader. This is not a developer’s reading list. If you want to build LLMs from scratch, you’re reading the wrong blog.

What I care about: understanding what these systems actually are, what they can and can’t do, what they mean for teaching and learning, and how to think clearly about the cultural and ethical questions they raise. The AI book market has exploded with hype, doom, and everything in between. Most of it isn’t worth your time. Here’s what is.

Where to Start

Co-Intelligence: Living and Working with AI — Ethan Mollick (2024)

This is the book I recommend first to every educator asking me where to begin, and it’s not particularly close. Mollick is a Wharton professor who has been using AI in his classroom since the day ChatGPT launched and writing about it — honestly and with genuine curiosity — at his Substack ever since. Unlike most AI books, this one was written by someone with actual daily practice rather than theoretical distance.

The central argument is in the title: AI as co-intelligence, not replacement intelligence. Mollick’s four rules for working with AI are practical enough to start using today and deep enough to keep thinking about. His concept of the “jagged frontier” — that AI is weirdly capable at things we’d consider hard and oddly bad at things we’d consider easy — is the single most useful mental model I’ve found for calibrating what to expect.

For educators specifically, Chapter 7 on AI in schools is worth the price of the book alone. Mollick is genuinely thoughtful about the implications for assessment, expertise development, and what we’re actually asking students to do when we assign traditional work in an era of capable AI tools. He doesn’t hand you easy answers. He asks better questions.

Worth noting: some readers already deep in this space find it a bit surface-level, and it was written in 2023, so some specifics are already dated. Read it for the framework, not the technical details.

Get Co-Intelligence

Understanding What AI Actually Is

Artificial Intelligence: A Guide for Thinking Humans — Melanie Mitchell (2019)

Still the best accessible introduction to what AI fundamentally is and isn’t. Mitchell is a computational complexity researcher at the Santa Fe Institute, and she brings real intellectual rigor to a topic that attracts an unusual amount of noise. This book predates the LLM explosion, which is actually part of what makes it valuable — it gives you the conceptual foundation to understand why systems like GPT surprised even the researchers who built them.

Mitchell is especially good on the gap between narrow AI capability and what we loosely call “understanding.” If you want to have an informed opinion about whether AI is “really” thinking, read this first.

Get Artificial Intelligence: A Guide for Thinking Humans

The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman (2023)

This is the big-picture book. Suleyman co-founded DeepMind and Inflection AI before becoming CEO of Microsoft AI — he is, in other words, someone who has spent his career at the center of this thing. The Coming Wave is his argument that we are facing a genuine civilizational inflection point with AI (and synthetic biology), and that the window to build appropriate containment structures around these technologies is narrowing rapidly.

What distinguishes it from most AI doom-or-boom books is specificity. Suleyman doesn’t deal in vague anxieties — he makes concrete arguments about the concentration of power, economic disruption, and the structural problems of trying to regulate technology that spreads faster than governance can follow. Readable, serious, and useful for understanding why AI isn’t just a productivity story.

Get The Coming Wave

The Ethics and Alignment Problem

The Alignment Problem: Machine Learning and Human Values — Brian Christian (2020)

If you want to understand why making AI systems that reliably do what we want them to do is genuinely hard — technically, philosophically, and ethically — this is the book. Christian spent years interviewing researchers at the leading AI labs and built a rigorous, human-readable account of the problem at the center of AI safety.

The alignment problem isn’t abstract. It shows up in recommendation systems that optimize for engagement and produce radicalization. It shows up in hiring algorithms that encode historical discrimination. It shows up every time a system is optimized for a measurable proxy of what we actually want, rather than the thing itself. Christian is excellent on how this happens, why it’s hard to fix, and what the researchers working on it are actually doing.

This book complements Mollick’s more optimistic framing well. Read both.

Get The Alignment Problem

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence — Kate Crawford (2021)

The critical perspective this list needs. Crawford, a researcher at USC and co-founder of the AI Now Institute, makes a compelling argument that AI systems are not software abstractions — they are material, political, and economic objects with real costs and embedded power dynamics. The rare earths in the hardware, the data center energy consumption, the contract workers’ labeling training data in difficult conditions, and the labor displacement — Crawford maps all of it.

I don’t agree with everything in this book, and Crawford’s perspective is explicitly critical rather than balanced. But the questions she raises are important and underrepresented in the mainstream AI conversation. If you’ve read Mollick and want a counterweight, this is it.

Get Atlas of AI

The History and the People

Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World — Cade Metz (2021)

The best narrative history of the deep learning revolution. Metz is a New York Times technology reporter who covers this beat obsessively, and he had remarkable access to the key figures: Geoffrey Hinton, Yann LeCun, Demis Hassabis, and the others who turned decades of dormant theory into the technology now reshaping every industry.

This is the book if you want to understand why everything changed so fast after 2012, what the competitive dynamics between labs looked like, and how the researchers themselves thought about what they were building. Reads like a thriller — the science is real, the rivalries are real, and the ethical stakes land harder when you know the people involved.

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For Educators Specifically

Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing) — Salman Khan (2024)

Sal Khan founded Khan Academy. He’s also an optimist, which comes through clearly in this book. Brave New Words makes the case for AI as tutor, mentor, and educational equalizer — arguing that tools like Khanmigo can bring the one-on-one tutoring advantage (Bloom’s famous “two sigma” finding, that individual tutoring improves outcomes dramatically over classroom instruction) to every student who needs it.

I read this more critically than I read Mollick, because the institutional interests are more directly aligned with the argument. But the core vision — that AI could close genuine equity gaps in access to high-quality educational support — is worth taking seriously, and the specific examples from Khan Academy’s work are compelling. Read it alongside the Crawford book for balance.

Get Brave New Words

The Short Version

If you read only one: Mollick’s Co-Intelligence. It’s the most practical and most directly relevant to anyone working in education or doing knowledge work of any kind.

If you want the big picture: Suleyman’s The Coming Wave. The most serious argument about what’s actually at stake.

If you want the history: Metz’s Genius Makers. The best story of how we got here.

If you want the ethics: Christian’s The Alignment Problem for the technical/philosophical dimension, Crawford’s Atlas of AI for the political/material dimension.

These books sit alongside my broader reading on technology and education — if you’re interested in that context, the Zettelkasten post covers the note-taking system I use to actually hold onto what I read across all of this.


The Eclectic Educator is a free resource for everyone passionate about education and creativity. If you enjoy the content and want to support the newsletter, consider becoming a paid subscriber. Your support helps keep the insights and inspiration coming!

2,178 Digitized Occult Books: Strange Treasures for Authentic Learning

Curiosa Physica

I want to tell you about a library in Amsterdam housed in a 17th-century building called the House with the Heads, funded in part by the author of The Da Vinci Code, with a collection that was granted UNESCO Memory of the World status in 2022, and whose digital archive you can browse for free from your couch right now.

The Bibliotheca Philosophica Hermetica — the Ritman Library, now housed at the Embassy of the Free Mind — contains roughly 30,000 titles on Western esotericism, mysticism, alchemy, astrology, Kabbalah, Rosicrucianism, and related traditions. In 2018, after Dan Brown donated €300,000 to fund the digitization project (he’d visited the library multiple times while researching The Lost Symbol and Inferno), the library launched what they called, with genuine wit, Hermetically Open: a free, publicly accessible digital archive of its rarest pre-1900 texts. As of 2025, 2,178 books are fully scanned and available online.

The collection includes the Corpus Hermeticum from 1472, Giordano Bruno’s work from 1584, the first printed visual representation of the Kabbalistic Tree of Life from 1516, alchemical manuscripts with intricate hand-drawn diagrams, and hundreds of texts in Latin, Dutch, German, French, and English that blur every boundary we’ve drawn between science, philosophy, theology, medicine, and magic.

My first thought when I found this collection was: this is exactly what I want students to encounter.

Why “Occult” Is the Wrong Frame for This

The word does its work on us. “Occult” conjures Halloween aesthetics and conspiracy theories, and it’s easy to dismiss the whole thing as fringe material with no serious application in a classroom.

That reaction, though, says more about our current assumptions about knowledge than it says about these texts.

For several centuries of Western intellectual history, there was no clean dividing line between alchemy and chemistry, between astrology and astronomy, between hermetic philosophy and natural science. Isaac Newton — who gave us calculus, the laws of motion, and the theory of universal gravitation — spent at least as much of his intellectual energy on alchemy and Biblical prophecy as he did on physics. His alchemical manuscripts are available online too, through Cambridge’s digital library. The man who arguably launched the scientific revolution was also, by any contemporary definition, deeply engaged in occult practice.

This isn’t an embarrassing footnote. It’s actually essential context for understanding how scientific knowledge develops — through the messy, often wrong, often ideologically entangled process of humans trying to make sense of the world with the conceptual tools they have available. The Ritman collection is a primary source archive for that story.

As a doctoral student who has spent years reading about how knowledge is constructed, organized, and transmitted, I find this collection genuinely thrilling. These books are where the medieval and the modern collide. They’re where you can see what people got wrong and what they got surprisingly right, often in the same text, often for reasons that have nothing to do with intelligence and everything to do with the conceptual frameworks available to them.

That’s exactly what I want students to sit with.

What Makes This Useful for Teachers

The collection isn’t neat. It’s multilingual, dense, and built for scholars. That’s part of the point — it’s not pre-digested curriculum content, it’s actual historical material that requires work to interpret. For teachers who believe students should wrestle with primary sources rather than always receiving polished summaries of them, this is a goldmine.

A few ways I’d use this across disciplines:

History and Social Studies — Trace how alchemy became chemistry. Look at how astrology shaped political decisions in early modern Europe. Ask students why the intellectual tradition represented here was systematically excluded from what we now call the history of science, and what that exclusion says about how we decide what counts as legitimate knowledge.

English and Literature — The visual and linguistic strangeness of these texts is remarkable. The archaic spellings, the “long s” that looks like an f, the allegorical imagery, the blend of Latin and vernacular — all of it offers material for close reading and for connecting to the Gothic, Romantic, and magical realist traditions that drew heavily from this well.

Science — Contrast alchemical “recipes” with modern chemical procedures. Examine how flawed models of the cosmos were still generative — the people using them weren’t stupid, they were working at the edge of what was knowable. What does that say about our own current models?

Art and Design — The illuminated manuscripts and alchemical diagrams in this collection are extraordinary visual objects. The symbolic language is dense and codified and genuinely beautiful. There’s serious material here for design history, visual communication, and semiotics.

Philosophy — The Hermetic tradition represents a sustained attempt to synthesize Greek philosophy, early Christian theology, Jewish mysticism, and natural observation into a unified account of reality. That synthesis didn’t work out the way its practitioners hoped. But the attempt itself raises questions about knowledge, interpretation, and the limits of any single framework for understanding the world — questions that don’t go away.

The cross-disciplinary angle is what I find most powerful. One of the things that frustrated me most in my years as an educator before moving into instructional coaching is how thoroughly we’ve siloed knowledge. Students take chemistry, history, and English as separate things, as if the history of chemistry weren’t fascinating, as if the literary history of science didn’t exist. The Ritman collection doesn’t respect those boundaries because it predates our drawing of them.

The Resource

The collection is free, fully accessible online, and searchable — though the search interface takes some patience. The direct link to the digital catalog is here. I’d recommend starting with the “Digital collection” page, which gives you some orientation before you dive in.

A few things worth knowing:

  • The majority of texts are in Latin, Dutch, German, or French. English-language texts exist, but aren’t the majority. For classroom use, this is actually an opportunity — translation, context-building, and working with unfamiliar material are valuable skills.
  • The image quality varies, but the rare and fragile items were prioritized for digitization, so many of the most valuable texts are well scanned.
  • The broader collection, which includes 30,000 titles and continues to grow, is housed at the Embassy of the Free Mind in Amsterdam. If you’re ever there, it’s worth visiting.

The collection earned UNESCO Memory of the World status in 2022, a designation UNESCO does not hand out lightly. This is genuinely important cultural heritage, now freely available to anyone with internet access. That’s remarkable.

Dan Brown’s novels that led him to the Ritman Library — The Lost Symbol and Inferno — both draw heavily on the kind of Hermetic and esoteric tradition documented in this collection. If you want a somewhat lurid but surprisingly well-researched tour of the ideas, they’re a decent starting point. Brown is not a subtle writer, but he did his homework.

Related on this site: the AI books post covers how knowledge evolves and what it means to think critically about the tools we use — a thread that runs directly through what this collection makes visible.

What Teachers Need to Understand About AI and the Economy — A Reading List

man using laptop wit chat gpt
Photo by Matheus Bertelli on Pexels.com

Here’s something that should be keeping school leaders up at night: 55% of recent graduates report that their academic programs didn’t prepare them to use generative AI tools in the workforce. Not just use AI well — use it at all. We are preparing students for an economy that is reorganizing itself faster than our curriculum review cycles can keep up with, and most schools are responding with either panic or denial.

The World Economic Forum’s Future of Jobs Report 2025 projects that AI will displace 92 million jobs while creating 170 million new ones — a net gain on paper, but that math only works if the people losing the 92 million jobs can access the 170 million new ones. That transition requires education, retraining, and policy infrastructure that does not currently exist at the scale needed. Young workers in AI-exposed occupations are already experiencing shifts in employment. The college wage premium has flattened. Jobs requiring AI skills now command a 56% wage premium over those that don’t — up from 25% just the year before.

This is not an abstract future problem. It is the context in which our students will graduate.

I don’t write primarily about business or economics — this site is about education, technology, and the ideas that shape both. But understanding how AI is disrupting the economy is part of understanding what we are actually preparing students for. The books below are the ones I’d put in front of any educator or school leader who wants to think more seriously about this.

The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma — Mustafa Suleyman

Get it on Amazon

Suleyman co-founded DeepMind (later acquired by Google) and Inflection AI before becoming CEO of Microsoft AI. He is, in other words, someone who has been building this technology from the ground up and who has had to think carefully about what he was building.

The Coming Wave is his argument that we are facing a genuine inflection point: AI and synthetic biology are advancing faster than governance structures can keep pace with, and the window to build appropriate containment mechanisms is closing. His central concern isn’t that AI is malevolent — it’s that the concentration of power that comes with controlling transformative technology is itself the problem, whether that power sits with corporations, governments, or both.

For educators: the chapter on economic disruption is essential reading. Suleyman doesn’t pretend the transition will be smooth. He takes seriously the question of what happens to people and communities during the displacement phase, which is precisely the phase our current students are entering.

AI Superpowers: China, Silicon Valley, and the New World Order — Kai-Fu Lee

Get it on Amazon

Lee has a unique vantage point: he’s worked at Apple, Microsoft, and Google, and then moved to Beijing to lead Google China before becoming one of China’s leading AI investors. AI Superpowers was published in 2018, and some of the specific competitive dynamics have shifted, but the core argument holds: we are in a global race for AI dominance between two different models of how AI development should work, and the outcomes of that race will have profound economic consequences at every level.

The section on job displacement is where this book becomes most directly relevant to educators. Lee argues that routine cognitive work is the most vulnerable to automation — not just manual labor — and that the categories of work that will be protected are those requiring creativity, empathy, and complex human judgment. That framing has direct implications for what we teach and why.

Read this alongside The Coming Wave for a richer picture of the geopolitical and economic forces shaping the AI landscape.

Prediction Machines: The Simple Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

Get it on Amazon

Three economists from the University of Toronto built their framework around a deceptively simple claim: AI is, fundamentally, a technology that makes prediction cheaper. When prediction gets cheaper, the value of the things that complement prediction — judgment, action, data — increases. When prediction gets cheaper, the value of things that substitute for prediction — routine rule-following, low-stakes decision-making — decreases.

This framework is useful for educators because it maps directly onto a question we should be asking about curriculum: what are we teaching students that will be substituted by cheap AI prediction, and what are we teaching them that will be complemented by it? The answer has real implications for what genuinely rigorous education looks like in an AI economy. Prediction Machines is the most analytically useful book on this list for thinking through those questions.

The Age of AI: And Our Human Future — Henry Kissinger, Eric Schmidt & Daniel Huttenlocher

Get it on Amazon

An unusual collaboration: a former Secretary of State, a former Google CEO, and an MIT computer scientist thinking together about what AI means for how human societies understand the world. The book is less about the economic disruption and more about the epistemological one — the way AI systems generate outputs that humans can use without understanding how those outputs were produced, and what that does to decision-making in business, government, and education.

The argument that lands hardest for me as an educator: we have spent centuries building institutions of learning around the transmission and evaluation of human knowledge. AI is producing a new kind of knowledge — statistical, pattern-based, extraordinarily capable, and fundamentally alien to how human minds work. What does education mean in that context? This book doesn’t fully answer the question, but it asks it more precisely than most.

Power and Prediction: The Disruptive Economics of Artificial Intelligence — Ajay Agrawal, Joshua Gans & Avi Goldfarb

Get it on Amazon

The follow-up to Prediction Machines, published in 2022, moves from “here’s what AI does to economics” to “here’s how organizations and institutions will be restructured by it.” The core new argument: AI doesn’t just automate tasks; it disrupts the decision-making systems in which those tasks are embedded. That disruption creates power shifts — between professions, between institutions, between incumbents and challengers.

The education implications are direct. The authors discuss healthcare and legal services as sectors being restructured by AI-driven prediction, and the analysis applies equally to education. What happens to the teacher’s role when AI can provide personalized feedback faster and at greater scale? What happens to credentialing when AI can assess competencies that diplomas approximate? These aren’t comfortable questions, but they’re the right ones to be asking now rather than after the disruption has already happened.

The Question Underneath All of These Books

The books above are written primarily for business leaders, policymakers, and economists. That’s who they were designed for. But they all circle around a fundamentally educational question: what kind of people do we need to develop, and what do we need to prepare them for, in an economy being reorganized by AI?

Self-Determination Theory gives us part of the answer — humans are most resilient and most capable when they have genuine autonomy, a sense of competence, and meaningful connection. Those psychological needs don’t get automated. They get more important as the tasks around them do.

The Connectivist framing that the network is where knowledge lives is also useful here: in an economy where AI can provide information faster than any human, the competitive advantage lies in the quality of your connections — to ideas, to people, to problems worth solving — and in your capacity to navigate those networks with judgment. That’s what education in an AI economy should be building.

These books don’t answer those questions for us. But they describe the problem with enough precision that we can start asking the right ones.

Related on this site: the AI books post covers the books I’d recommend for understanding what AI actually is — how it works, what it can and can’t do, and what the most credible researchers think about its implications. That’s a companion list to this one.


The Eclectic Educator is a free resource for everyone passionate about education and creativity. If you enjoy the content and want to support the newsletter, consider becoming a paid subscriber. Your support helps keep the insights and inspiration coming!

What If Every Teacher Could Build an AI Tutor? David Wiley’s Generative Textbooks Idea Is Worth Your Attention

generative textbooks

There’s a particular kind of idea that shows up in education technology every few years — one that sounds almost too obvious once you hear it, but that nobody had quite put together that way before. David Wiley‘s work on generative textbooks is one such idea.

I’ve been following Wiley for a long time. If you’ve ever used an open textbook in a course or benefited from freely available educational materials online, there’s a good chance his fingerprints are on the infrastructure that made that possible. He’s one of the founders of the open educational resources movement — the effort to create, share, and freely adapt teaching and learning materials under open licenses. It’s unglamorous, important work that has saved students billions of dollars in textbook costs and given teachers genuine tools they can actually modify.

So when Wiley started applying that same philosophy to AI, I paid attention.

The Problem He’s Solving

The standard AI-in-education conversation goes like this: here are some tools (ChatGPT, Gemini, Claude, take your pick), and here are some ways teachers can use them. The tools belong to the companies. The teachers are users. If the company changes pricing, changes policy, or shuts down, the teacher starts over.

Wiley’s question is different: what if the instructional logic — the pedagogical intelligence built into an AI learning experience — belonged to the teacher? What if any educator could author an AI-powered learning tool without writing code, without a budget, and without surrendering control to a platform?

That’s what generative textbooks are attempting to answer.

How It Actually Works

The architecture is simpler than it sounds. A generative textbook isn’t a document — it’s a structured collection of inputs that, when assembled, tell an AI model exactly how to behave as a learning tool for a specific subject.

Here’s what an author creates:

  • A book-level prompt stub — the template that sets the AI’s voice, tone, format, and overall behavior. Think of this as the personality and ground rules of the learning experience.
  • Learning objectives — one per chapter or topic, short statements about what a learner should understand or be able to do.
  • Topic summaries — accurate, context-rich summaries written for the AI, not for students. These are what the model uses to stay grounded in accurate content rather than hallucinating.
  • Activity templates — the types of interactions available: flashcards, explanations, quiz questions, Socratic dialogue, whatever the author builds in.

When a student picks a topic and an activity type, the system assembles the relevant pieces into a single prompt and sends it to the language model, which generates a fresh, tailored learning experience — not retrieved from a database, but generated in the moment based on the author’s pedagogical structure.

As Wiley puts it: in this model, prompt engineering is instructional design. The authoring isn’t code — it’s curriculum work. That’s a meaningful distinction for teachers.

The Clever Pivot on Cost

The original prototype sent prompts through an API to open-weight language models hosted on Groq. Clean, seamless, technically elegant. Also not free — API calls cost money at scale, and Wiley found that most educators he consulted weren’t particularly concerned with whether the underlying model was “open” in the ideological sense. They were concerned with whether it was free for students.

So he made a pragmatic call: rather than routing prompts through a back-end service, the tool now assembles the prompt and copies it to the student’s clipboard. The student pastes it into whatever AI interface they already have access to — ChatGPT’s free tier, Gemini, a school-licensed model, whatever.

This is inelegant in the user-experience sense. There’s a copy-paste step that breaks the flow. Analytics become difficult. Student privacy depends on whatever tool they choose to use. Wiley is honest about all of this — he describes the project explicitly as a tech demonstration, not a finished product.

But there’s something worth noticing in the pragmatism. The decision prioritizes actual access over technical elegance. For students in districts that can’t afford platform licenses and teachers who don’t control their school’s technology budget, a tool that works with the free tier of a consumer AI product is more useful than a seamless experience behind a paywall.

Where Wiley Has Taken This Since

The generative textbook prototype was a starting point, and Wiley has kept building. His more recent thinking has evolved toward what he calls OELMs — Open Educational Language Models — a framework that combines open-licensed content with AI in a more sophisticated way.

The key addition is retrieval-augmented generation (RAG): rather than just grounding the AI’s behavior in a few paragraph-length topic summaries, an OELM includes a curated collection of OER content that the model actively retrieves from when generating responses. This makes the outputs more accurate, more traceable to specific source materials, and more trustworthy for educational use — one of the genuine limitations of relying on a general-purpose language model that might confabulate confidently.

The broader argument Wiley is making — that generative AI is the logical successor to OER — is worth sitting with. His claim isn’t that AI replaces open textbooks, but that the principles that made OER valuable (open licensing, participatory creation, the ability to adapt and remix) need to be extended into the AI space. As the educational materials market shifts toward AI-powered products, the question of who owns the instructional logic matters enormously for equity and access.

What This Means for Teachers

I want to be careful not to oversell where this project currently is. The generative textbooks site is live and explorable, but this is genuinely early-stage work. The copy-paste workflow has real friction. The quality of the learning experience depends heavily on the quality of the inputs a teacher creates, which means the authoring itself requires genuine pedagogical thought — garbage in, garbage out applies acutely here.

But the underlying question Wiley is raising is one I think about a lot as an instructional coach: who gets to design the learning experience, and on whose terms?

The dominant model in AI-powered education right now is platform-centric. A company builds an AI tool, schools license it, teachers become users. This mirrors exactly what happened with traditional educational technology — districts buy the LMS, teachers work inside it, the pedagogical architecture belongs to the vendor. We know how that story tends to go: cost escalation, lock-in, tools that don’t quite fit what teachers actually need because they were designed generically.

Wiley’s generative textbooks project is asking whether there’s another path — one where educators are architects rather than users. Where the instructional intelligence lives in open, adaptable, teacher-created structures rather than in proprietary platforms. Where a teacher in a school with limited resources can build a learning tool that’s as good as anything a well-funded district is paying for.

That’s not a modest ambition. And it’s not finished yet. But it’s the kind of work that tends to matter more than it seems to when it starts.

Go explore:

Related reading: my AI books post covers Ethan Mollick’s Co-Intelligence, which has useful framing for educators thinking about AI as a co-teacher rather than a replacement — a theme that runs directly through Wiley’s work.

Book Review: The Shift to Student-Led by Catlin Tucker & Katie Novak

Reimagining the Classroom: The Shift to Student-Led with UDL & Blended Learning
Version 1.0.0

Here’s the thing nobody in education wants to say out loud: a significant portion of what we call “teaching” is actually just teachers doing the work that students should be doing.

Teachers write the summaries. Teachers generate the discussion questions. Teachers create the study materials. Teachers provide all the feedback. Teachers design all the reflection prompts. And then we wonder why students are passive, why engagement is low, and why teachers are burning out at alarming rates.

The Shift to Student-Led: Reimagining Classroom Workflows with UDL and Blended Learning by Catlin Tucker and Katie Novak is a direct response to this problem. As an instructional coach, I find myself recommending this book regularly — not because it’s revelatory, but because it articulates something that’s very hard to put into words in a 50-minute faculty meeting and then hands you tools to actually do something about it.

What the Book Is Actually About

Tucker and Novak are explicit about their starting point: they’ve worked with too many exhausted teachers. The context is post-pandemic education, where teachers who were already stretched thin absorbed years of additional uncertainty, disruption, and grief — and are now expected to simply resume as if none of that happened. The book isn’t optimistic about the status quo. It explicitly states that the current model isn’t sustainable and makes a structural argument for why.

The structural argument is this: when teachers are the primary workers in a classroom — the ones generating content, facilitating discussion, providing feedback, assessing progress — they create passive learners and exhausted professionals. The labor is distributed entirely wrong. Students are spectators in their own education, and teachers do a job that can’t be done by one person for 30 students without someone getting shortchanged. Usually, someone is the teacher.

The solution Tucker and Novak offer is to redistribute that labor through what they call student-led workflows — specific, structured shifts that move each of those teacher-dominated tasks back to students. Ten shifts in total, one per chapter, each paired with Universal Design for Learning (UDL) principles and blended learning strategies that make the shift manageable across a diverse classroom.

UDL and Blended Learning — Why These Two

The combination isn’t arbitrary. UDL addresses the persistent challenge of designing learning for the full range of students in a classroom without creating 30 different lesson plans. Its core principle — build flexibility and choice into the design from the start rather than retrofitting accommodations afterward — directly enables student agency. When multiple means of engagement, representation, and expression are built in, students can direct more of their learning because the options are available.

Blended learning addresses the logistics. Technology, when used intentionally, creates the structures that enable student-led workflows at scale. Not technology as a substitute for teaching, but technology as the infrastructure that lets students access content, track their own progress, collaborate asynchronously, and document their thinking in ways that a purely analog classroom can’t sustain.

Neither of these ideas is new. What Tucker and Novak do is show specifically how they work together to shift who does the work, which is a more practical frame than either concept provides on its own.

The Ten Shifts

The book’s ten workflows move through five areas: lessons, assessments, practice, feedback, and discussions. In each area, Tucker and Novak show what the teacher-led version looks like, what problems it creates, what the research suggests, and what a student-led version looks like with concrete examples and implementation tools.

A few that land particularly well in the coaching conversations I have:

From teacher-provided feedback to student self-assessment. This is the shift most teachers resist hardest, and most students need most. The book makes a compelling case that waiting for teacher feedback creates learned helplessness — students who can’t evaluate their own work are dependent on external validation in ways that don’t serve them in college, career, or life. The practical tools for building student capacity to assess their own work are among the most immediately usable in the book.

From teacher-led discussion to student-facilitated conversation. Whole-class discussions in which the teacher asks questions and students respond are a remarkably inefficient way to build thinking. Tucker and Novak offer structures — including protocols that can run entirely without teacher direction — that shift the facilitation to students. This one requires patience to implement; students who have been in teacher-led discussions their whole lives don’t immediately know how to facilitate for each other. But the payoff is substantial.

From teacher-created practice to peer-generated learning resources. When students create flashcards, summaries, or quiz questions for each other, they’re doing the cognitive work that actually builds retention. The teacher’s job shifts from resource creator to quality reviewer, which is a genuinely different and more sustainable role.

What It Gets Right

The book earns its positive reputation with practitioners primarily because it doesn’t just describe what student-led learning looks like — it walks through the implementation with enough specificity to actually try it. The templates and protocols are real, the scenarios are recognizable, and Tucker and Novak are honest that these shifts take time and that students will push back initially because passive learning is more comfortable in the short term.

The framing of teacher sustainability is also well handled. This isn’t positioned as “here’s how to do more for students” — it’s positioned as “here’s how to stop doing work that isn’t yours to do,” which is a meaningfully different message for a profession that has normalized unsustainable self-sacrifice.

What to Watch For

A couple of honest caveats from the coaching side of this.

The book is designed primarily for secondary and post-secondary classrooms, though the principles extend further. Elementary teachers will find more adaptation required.

As with most professional development books, the gap between reading the ideas and actually implementing them in the classroom is real. The templates help, but student-led workflows require significant upfront investment in building the routines and student capacity that make them work. The book is clear about this, but it’s easy to underestimate when reading.

And the blended learning components assume a level of access to technology and reliability that isn’t universal. The ideas hold without the technology, but the specific digital strategies require some translation for under-resourced classrooms.

Who Should Read This

Teachers who feel like they’re carrying their classrooms on their backs — this book is written directly for you, and the framing will be immediately recognizable.

Instructional coaches supporting teachers in designing more student-centered practice — I’d use this as a book study anchor and the companion resources as coaching tools.

School leaders thinking about what sustainable teaching practice actually looks like — the structural argument in the opening chapters is worth your time, even if you don’t go chapter by chapter through the workflows.

Get The Shift to Student-Led

Free resources from the authors:

Related on this site: the free play and Peter Gray post makes a parallel argument about who does the work of learning — and what happens to kids when adults take over tasks that should belong to them.

AI Schools and the Illusion of Efficiency

close up photo of an abstract art
Photo by Marek Piwnicki on Pexels.com

A recent investigation into Alpha School, a high-tuition “AI-powered” private school, revealed faulty AI-generated lessons, hallucinated questions, scraped curriculum materials, and heavy student surveillance. Former employees described students as “guinea pigs.”

That’s the headline.

But the real issue isn’t whether one school deployed AI sloppily.

The real issue is whether we are confusing technological acceleration with educational progress.

The Seduction of the Two-Hour School Day

Alpha’s pitch is simple and powerful: compress academic learning into two hyper-efficient hours using AI tutors, then free the rest of the day for creativity and passion projects.

If you believe traditional schooling wastes time, that promise is intoxicating.

But here’s the problem:

Efficiency is not the same thing as development.

From a Science of Learning and Development (SoLD) perspective, learning is not merely the transmission of content. It is a process that integrates cognition, emotion, identity, and social context. Durable learning requires safety, belonging, agency, and meaning-making.

You cannot compress belonging into a two-hour block.

You cannot automate identity formation.

And you cannot hallucinate your way to deep understanding.

Connectivism Is Not Automation

Some defenders of AI-heavy schooling argue that we are simply witnessing the next phase of networked learning. Knowledge is distributed. AI becomes a node in the network. Personalized pathways replace one-size-fits-all instruction.

That language sounds connectivist.

But Connectivism is not about replacing human nodes with machine ones.

It concerns the expansion of networks of meaning.

In a connectivist system:

  • Learning happens across relationships.
  • Knowledge flows through dynamic connections.
  • Judgment matters more than memorization.
  • Pattern recognition and critical filtering are essential skills.

AI can participate in that network.

But when AI becomes the primary instructional authority — generating content, generating assessments, evaluating its own outputs — the network collapses into a closed loop.

AI checking AI is not distributed intelligence.

It is recursive automation.

Connectivism requires diversity of nodes.

Not monoculture.

Surveillance Is Not Personalization

The investigation also described extensive monitoring: screen recording, webcam footage, mouse tracking, and behavioral nudges.

This is framed as personalization.

It is not.

It is optimization.

SoLD research clarifies that psychological safety and autonomy are foundational to learning. When students feel constantly watched, agency erodes. Compliance increases. Anxiety increases.

You can nudge behavior with surveillance.

You cannot cultivate intrinsic motivation that way.

If our model of learning begins to resemble corporate productivity software, we should pause.

Education is not a workflow dashboard.

The Hidden Variable: Selection Bias

To be fair, Alpha School reportedly produces strong test scores.

However, high-tuition schools serve families with financial, cultural, and educational capital. Research consistently shows that standardized test performance correlates strongly with income.

If affluent students succeed in an AI-heavy environment, that does not prove that the AI caused the success.

It may simply mean the students would succeed almost anywhere. I often say those students would succeed with a ham sandwich for a teacher.

The question is not whether AI can serve already advantaged learners.

The question is whether AI, deployed without deep pedagogical grounding, strengthens or weakens human development.

The Real Design Question

The danger is not AI itself.

The danger is designing educational systems around what AI does well.

AI does well at:

  • Drafting content
  • Generating practice questions
  • Scaling feedback
  • Recognizing surface patterns

AI does not do well at:

  • Reading emotional context
  • Building trust
  • Modeling intellectual humility
  • Navigating moral ambiguity
  • Forming identity

SoLD reminds us that learning is relational and developmental.

Connectivism reminds us that learning is networked and distributed.

If we optimize for what AI does well and marginalize what humans do uniquely well, we create a system that is efficient — but thin.

Fast — but shallow.

Impressive — but fragile.

What This Means for Public Education

This story is not merely about a private school engaging in aggressive experimentation.

It is a preview.

Every district will face pressure to:

  • Automate instruction
  • Replace textbooks with AI tutors
  • Compress seat time
  • Increase data capture

The answer cannot be a blanket rejection.

Nor can it be an uncritical adoption.

The answer is design discipline.

We should use AI to:

  • Reduce administrative drag
  • Prototype lessons
  • Support differentiated feedback
  • Expand access to expertise

But we should anchor every AI decision in two non-negotiables:

  1. Does this strengthen human relationships?
  2. Does this expand student agency and meaning-making?

If the answer is no, we are not innovating.

We are optimizing the wrong variable.

The Choice in Front of Us

We stand at a fork.

We can design AI systems around human development.

Or we can redesign human development around AI systems.

One path amplifies Connectivism, relational trust, and whole-child growth.

The other path creates compliant, monitored, hyper-efficient learners who score well but lack deep agency.

Technology will not make that choice for us.

We will.


The Eclectic Educator is a free resource for everyone passionate about education and creativity. If you enjoy the content and want to support the newsletter, consider becoming a paid subscriber. Your support helps keep the insights and inspiration coming!

Engagement Is the Outcome, Not the Goal

For years, we’ve treated engagement like something teachers should be able to manufacture on demand.

If students aren’t engaged, the assumption is often that the lesson wasn’t exciting enough, interactive enough, or energetic enough. So we add activities. We add movement. We add tools. We add noise.

And then we’re surprised when it still doesn’t work.

Here’s the hard truth I’ve learned as an instructional coach:

Engagement isn’t something you plan for. It’s something you earn.

Why Planning for Engagement Often Backfires

When engagement becomes the primary goal of lesson planning, we usually end up designing around surface-level behaviors:

  • Are students busy?
  • Are they moving?
  • Are they talking?
  • Are they smiling?

But none of those things guarantees learning.

In fact, classrooms can look highly engaged while very little meaningful thinking is happening. Students comply. They complete. They perform school.

And teachers feel frustrated because they did everything “right.”

What the Research Actually Tells Us

Research connected to the Science of Learning and Development (SoLD) consistently points to the same conclusion:

Engagement follows meaning.

Students are more likely to engage when:

  • The task feels relevant to their lives or the world around them
  • They have some sense of ownership or choice
  • The thinking required actually matters

When those conditions are present, engagement emerges naturally. When they’re missing, no amount of energy can save the lesson.

This is why gimmicks don’t scale—and why they exhaust teachers.

Shifting the Planning Question

Instead of starting with:

“How do I make this engaging?”

Try starting with:

“Why would this matter to a student?”

That single question forces a different kind of design thinking:

  • What problem is being explored?
  • What decisions are students being asked to make?
  • Who or what is this work for?
  • Where does student thinking actually show up?

When lessons are built around those questions, engagement becomes a byproduct—not a burden.

What This Means for Teachers

This shift doesn’t require abandoning structure, rigor, or content. It requires recentering the work on meaningful thinking rather than performance.

It also reduces burnout.

When students carry more cognitive load, teachers don’t have to bring all the energy. The work itself does more of the heavy lifting.

That’s not about doing less—it’s about doing different.

A Coaching Note from the Field

When teachers tell me, “My students just aren’t engaged,” my response is rarely about strategies.

It’s usually about the task.

Fix the task, and engagement often surprises you.

If this way of thinking resonates, I write a short weekly newsletter for teachers and instructional leaders focused on authentic learning, instructional coaching, and designing school in ways that actually work.

No spam. No gimmicks. Just clear thinking from the field.

You can subscribe here.

New Tools I’m Trying in 2026

black and red headphones beside black smartphone and white earbuds
Photo by Tara Winstead on Pexels.com

I’m revisiting some of my everyday tools as we head into 2026. Why? Because… reasons…

Mostly, I’m thinking about how I move through my days and how I combine analog and digital tools to keep my monkey brain moving and productive.

Tool 1: I’ve moved away from Google Search. Face it, friends: it’s trash. Whether beset by so many ads you can’t find actual sites or that actual, worthwhile sites are pushed further and further down the page because of the ongoing enshittification of Google and other services, I’ve switched to Kagi.

I won’t go into all the details of why here (soon), but suffice it to say that Kagi just works like a good search engine should. Yes, I now pay for the privilege of decent web searches. Or, I ask ChatGPT for an awful lot of things before I try any searches at all.

Tool 2: I’m abandoning Notion for all but one thing, and that’s tracking my reading. I’ve got a database for all my books (read, TBR, and want to buy) in a Notion database and using a tool called NotionReads, I can easily add books to the database, pulling necessary data for each book.

I thought about just using a Google Sheet for this purpose, but Notion works well for this process. For my daily note capture and digital Zettelkasten, I’m moving to Obsidian. I’ve had it for a few years but initially went with Notion for note-taking. However, after dealing with more software bloat than I wanted and only seeing more of it on the horizon for Notion – why do we always want a tool to do everything rather than just doing one thing really well? – I’m jumping ship to Obsidian.

I’m still using Readwise to capture highlights from the web and the few remaining Kindle books I’ve yet to read – more on my shift from digital to physical books soon – and I can import those highlights seamlessly into Obsidian. I’m using Steph Ango’s usage strategy for setting up my Obsidian vault since it makes the most sense to my seeing-all-things-as-an-interconnected-web brain. More on how that progresses soon, too.

Pulling into the final year of my dissertation journey, there’s more to come from me this year. Besides, this year marks 20 years of publishing web content, so we’ll see what that brings.


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