AI Doesn’t Kill Learning, and I Can Prove It

There’s a curious misconception floating around, whispered by skeptics, shouted by cynics: that letting students use AI in their coursework flattens the learning curve. That it replaces thinking. That it reduces education to a copy-paste exercise. If everyone has the same tool, the logic goes, outcomes must converge. But that’s not what happens. Not even close.

In three separate university classes, I removed all restrictions on AI use. Not only were students allowed to use large language models, they were taught how to use them well. Context input, prompts, revision loops, scaffolding, argument development; the full toolbox. Each group has a customized AI assistant tailored to the course content. Everyone had the same access. Everyone knows the rules. And yet the difference in what they produced is quite large.

Some students barely improved. Others soared. The quality of work diverged wildly, not only in polish but in depth, originality, and complexity. It didn’t take long to see what was happening. The AI was a mirror, not a mask. It didn’t hide student ability and effort; it amplified both. Whatever a student brought to the interaction, curiosity, discipline, intellectual courage, determined how far they could go.

This isn’t hypothetical. It’s empirical. I grade every week. I see the evidence.

When given a routine assignment, generative AI can do a decent job. A vanilla college essay? Sure, it’ll pass. But I don’t assign vanilla. One of my standard assignments asks undergraduates to write a paper worthy of publication. Not in a class blog or a campus magazine; a real, peer-reviewed publication.

You might think that’s too much to ask. And yes, if the bar is “can a chatbot imitate academic tone and throw citations at a thesis,” then yes, AI can fake it. But a publishable paper requires more than tone. It requires original framing, precise argumentation, contextual awareness, and methodological discipline. No prompt can do all that. It requires a human mind, inexperienced, perhaps, but willing to stretch.

And stretch they do. Some of these students, undergrads and grads, manage to channel the AI into something greater than the sum of its parts. They write drafts with the machine, then rewrite against it. They argue with the chatbot, they question its logic, they override its flattening instincts. They edit not for grammar but for clarity of thought. AI is their writing partner, not their ghostwriter.

This is where it gets interesting. The assumption that AI automates thinking misses the point. In education, AI reveals the higher order thinking. When you push students to do something AI can’t do alone (create, synthesize, critique), then the gaps between them start to matter. And those gaps are good. They are evidence of growth.

Variance in output isn’t a failure of the method. It’s the metric of its success. If human participation did not matter, there would not be variance in output. In complex tasks, human input is critical, and that's the only explanation for this large variance. 

And in that environment, the signal is clear: where there is variance in performance, there is possibility of growth. And where there is growth, there is plenty of room for teaching and learning. 

Prove me wrong.

Innovation doesn’t need a faster engine

The doomsayers of AI are having their moment. They correctly point out that the rapid progress of large language models has slowed. Context windows remain limited, hallucinations persist, and bigger models no longer guarantee smarter ones. From this, they conclude that the age of AI breakthroughs is ending.

They are mistaking the engine for the journey.

History offers many parallels. When the internal combustion engine stopped getting dramatically better, innovation didn’t stop. That was when it really started. The real transformation came from everything built around it: road networks, trucking logistics, suburbs, the global supply chain. Likewise, the shipping container changed the world not through further improvements, but because it became the standard that reshaped ports, labor systems, and trade. When the core technology stabilizes, people finally start reimagining what to do with it.

This is the point we’ve reached with AI. The models are powerful, but most of their potential remains untouched. Businesses are still treating AI as a novelty, something to sprinkle on top of existing processes. Education systems, government workflows, healthcare administration; these are built as if nothing new has happened. We haven’t even begun to redesign for a world where everyone has a competent digital assistant.

The real question is not whether an AI can pass a medical exam. It’s how we organize diagnosis and care when every doctor has instant access to thousands of case studies. It’s not about whether an AI can draft an email. It’s about how office communication changes when routine writing takes seconds. The innovation now lies in application, not invention.

Limits are not the enemy. In fact, recognizing limits often helps creativity flourish. When designers accept that screen size on phones is fixed, they find smarter interfaces. We become inventive when the boundaries are clear. The same will happen with AI once we stop waiting for miracle upgrades and start asking better questions.

The real bottleneck is attention. Investment still flows heavily into training larger and larger models, chasing diminishing returns. Meanwhile, the tools that would actually change how people work or learn get far less support. It’s as if we are building faster trains while neglecting the tracks, stations, and maps.

There is a similar problem in education, where energy goes into protecting the structure of institutions while ignoring how learning could be improved. Just because we can do something well does not mean it is worth doing. And just because AI researchers can build a bigger model does not mean they should.

The most meaningful innovation is ready to happen. It is no longer about raw power, but about redesign. Once we shift our focus from models to uses, the next revolution begins.

FPAR: The Cycle That Makes AI Writing Actually Work

Students don’t need help accessing ChatGPT. They need help using it well. What’s missing from most writing instruction right now is not awareness of the tool but a habit, a skill of active, productive engagement with AI to replace passive, lazy consumption of AI-generated information. They need FPAR: Frame, Prompt, Assess, Revise.

Frame the task before asking for help. This means uploading or pasting in anything that helps the AI understand the assignment. That might be a rough draft, but it could just as easily be a Wikipedia article, a class reading, a news story, a research report, a course syllabus, or even a transcript of a group discussion. Anything that offers context helps the AI respond more intelligently. For a research paper, pasting in a background source (like an article the student is drawing on) can guide the AI to suggest better angles, examples, or questions. Even a confusing assignment prompt becomes more useful when paired with, say, a class chat where the professor explained it. The point is to stop treating AI like a mind reader. The more the student frames the task, the better the result.

Prompt with clarity. Vague questions get vague answers. Instead of saying “Fix this,” students should learn to be specific: “Cut this to 150 words without losing the argument,” or “Rephrase this so it sounds more like a high school student and less like a Wikipedia article.” A direct, useful prompt might be: “Write an intro for my paper; the main idea is that in surveillance programs, the limitations of technology end up creating an implicit policy that is more powerful than the actual law. Programmers may not intend to make policy, but in practice, they do.” If they want more ideas, they should ask for them. If they want structure, examples, tone shifts, or even counterarguments, they need to say so. A good prompt isn’t a wish; it’s a directive.

Assess critically. The most dangerous moment is when the AI gives back something that sounds good. That’s when students tend to relax and stop thinking. But sounding fluent isn’t the same as being insightful. They need to read the response like a skeptic: Did it actually answer the question? Did it preserve the original point? Did it flatten nuance or introduce new assumptions? If the student asked for help making their argument more persuasive, did it just sprinkle in some confident phrases or actually improve the logic? Every AI-generated revision should ALWAYS be interrogated, not accepted.

Revise intentionally. Once they’ve assessed the output, students should guide the next step. They might say, “That example works, but now the paragraph feels too long. Can you trim the setup?” or “Now add a rebuttal to this counterpoint.” Revision is where the conversation starts to get interesting. It’s also where students start to develop judgment, voice, and control, because they’re not just reacting to feedback, they’re directing it.

And then they go back to Frame. The cycle repeats, each time with sharper context, better prompts, more refined questions. FPAR is not just a strategy for using AI; it’s a structure for thinking. It builds habits of iteration, reflection, and specificity. It turns the student from a passive consumer into an active writer.

Most bad AI writing isn’t the fault of the model. It’s the result of unclear framing, lazy prompting, uncritical acceptance, and shallow revision. The antidote isn’t banning the tool. It’s teaching students how to use it with care. FPAR is how. 

Into the Rabbit Hole: Why Textbooks May Be in Trouble

A student recently asked me if it was acceptable that his AI chat session had veered off course. What began as a simple prompt became an increasingly specific set of questions, spiraling into what he called a “rabbit hole.” He seemed sheepish about it, as if learning tangentially were some kind of mistake. I couldn’t have been more pleased. This, I told him, is precisely the point.

Traditional education rewards linearity. Set objectives, follow the syllabus, color inside the lines. The textbook is stable, the learning goals are preordained, and deviation is called distraction. But learning has never been that obedient. It is recursive, exploratory, and often accidental. Some of the most profound understanding comes not from the direct answer to a posed question, but from an unexpected detour sparked by curiosity. With AI, students now have the power to chase those detours without waiting for office hours, a green light from the instructor, or even a good reason beyond “I wonder…” This changes quite a bit.

When students are asked to submit chat logs with AI tutors, what they’re really doing is offering a snapshot of their cognitive pathways. And more importantly, they’re showing us that they are learning how to learn. If the conversation ends where it began, I feel a twinge of disappointment. The best ones meander. They start with the assigned question, but then dig deeper, clarify a half-understood term, pivot to a related concept, challenge a definition, ask for an analogy, and sometimes loop back to the original point with far more nuance. These logs are not just records of content acquisition; they are evidence of intellectual agency. We can assess that.

In fact, we should assess that. The quality of learning is not solely determined by measurable learning outcomes, but by the process students undertake to arrive there. Are they passive recipients, asking AI to do the work for them? Or are they directing the tool with intent, steering the dialogue, probing deeper? The latter signals a kind of meta-cognition (thinking about own thinking) that is, ironically, hard to teach but easy to observe when it happens naturally.

This brings us to the brittle notion of the textbook. The textbook is a frozen artifact in an age of fluid information. It pretends to be definitive but is invariably outdated, flattened by consensus, and stripped of the lively contradictions that make real knowledge worth pursuing. If I worked for one of the big textbook publishers, I’d be worried. Not because students won’t read anymore, but because they might start asking better questions than the book is designed to answer. The premise of the textbook, that there is a “right” sequence of information to deliver to all learners, has been unraveling for years. AI may be the final tug.

Self-directed learning with AI does something more subversive than democratize access to knowledge. It shifts the locus of control. The student is no longer merely interpreting curated material but actively interrogating it, shaping the path, discovering relevance in real time. This undermines the gatekeeping role that institutions have long held. It’s not that professors are obsolete; they are needed more than ever as guides, critics, and curators. But the assumption that learning must be centrally planned is fading.

Of course, not all rabbit holes lead somewhere valuable. Some are tangents masquerading as insights. Some become compulsive avoidance of harder work. But that’s part of the deal. We don’t disparage writing because early drafts are messy. We understand that missteps are intrinsic to mastery. The same generosity should be extended to exploratory learning. The mess is the point.

And so, if a student gets lost in the weeds of AI dialogue, my response is not to steer them back to the path, but to ask what they found there. Did the journey refine their thinking? Did it unsettle an assumption? Did it spark a new question? If yes, then they’ve learned more than what was in the assignment. They’ve learned to follow the thread of their own curiosity, and that might be the most enduring lesson of all.

In a way, AI exposes the futility of pretending that education is a straight line. It’s more like a conversation with the world, and like any good conversation, it’s full of detours. We should celebrate the rabbit holes, not as distractions, but as destinations.

Custom Bot Segregation and the Problem with a Hobbled Product

CSU’s adoption of ChatGPT Edu is, in many ways, a welcome move. The System has recognized that generative AI is no longer optional or experimental. It is part of the work students, researchers, and educators do across disciplines. Providing a dedicated version of the platform with institutional controls makes sense. But the way it has been implemented has led to a diminished version of what could have been a powerful tool.

The most immediate concern is the complete ban on third-party custom bots. Students and faculty cannot use them, and even more frustrating, they cannot share the ones they create beyond their own campus. The motivation is likely grounded in cybersecurity and privacy concerns. But the result is a flawed solution that restricts access to useful tools and blocks opportunities for creativity and professional development.

Some of the most valuable GPTs in use today come from third-party developers who specialize in specific domains. Bots that incorporate Wolfram, for instance, have become essential in areas like physics, engineering, and data science. ScholarAI and ScholarGPT are very useful in research, and not easy to replicate. There are hundreds more potentially useful tools. Not having access to those tools on the CSU platform is not just a minor technical gap. It is an educational limitation.

The problem becomes even clearer when considering what students are allowed to do with their own work. If someone builds a custom GPT in a course project, they cannot share it publicly. There is no way to include it in a digital portfolio or present it to a potential employer. The result is that their work remains trapped inside the university’s system, unable to circulate or generate value beyond the classroom.

This limitation also weakens CSU’s ability to serve the public. Take, for example, an admissions advisor who wants to create a Custom bot to help prospective or transfer students explore majors or understand credit transfers. The bot cannot be shared with anyone outside the CSU environment. In practice, the people who most need that information are blocked from using it. This cuts against the mission of outreach and access that most universities claim to support.

Faced with these limits, faculty and staff are left to find workarounds. Some are like me and now juggle two accounts, one tied to CSU’s system and another personal one that allows access to third-party tools. We have to pay for our personal accounts out of pocket. This is not sustainable, and it introduces friction into the very work the platform was meant to support.

Higher education functions best when it remains open to the world. It thrives on collaboration across institutions, partnerships with industry, and the free exchange of ideas and tools. When platforms are locked down and creativity is siloed, that spirit is lost. We are left with a version of academic life that is narrower, more cautious, and less connected.

Of course, privacy and security matter. But so does trust in the people who make the university what it is. By preventing sharing and disabling custom bots, the policy sends a message that students and faculty cannot be trusted to use these tools responsibly. It puts caution ahead of creativity and treats containment as a form of care.

The solution is not difficult. Other platforms already support safer modes of sharing, such as read-only access, limited-time links, or approval systems. CSU could adopt similar measures and preserve both privacy and openness. What is needed is not better technology, but a shift in priorities.

Custom GPTs are not distractions. They are how people are beginning to build, explain, and share knowledge. If we expect students to thrive in that environment, they need access to the real tools of the present, not a constrained version from the past.

The Start-up Advantage and the Plain Bot Paradox

In the gold rush to AI, start-ups seem, at first glance, to have the upper hand. They are unburdened by legacy infrastructure, free from the gravitational pull of yesterday’s systems, and unshackled by customer expectations formed in a pre-AI era. They can begin with a blank canvas and sketch directly in silicon, building products that assume AI not as an add-on, but as the core substrate. These AI-native approaches are unencumbered by the need to retrofit or translate—start-ups speak the native dialect of today’s machine learning systems, while incumbents struggle with costly accents.

In contrast, larger, established companies suffer from what could be called "retrofitting fatigue." Their products, honed over decades, rest on architectures that predate the transformer model. Introducing AI into such ecosystems isn’t like adding a module; it’s more akin to attempting a heart transplant on a marathon runner mid-race. Not only must the product work post-op, it must continue to serve a massive, often demanding, user base—an asset that is both their moat and their constraint.

Yet even as start-ups celebrate their greenfield momentum, they stumble into what we might call the plain bot paradox. No matter how clever the product, if the end-user can get equivalent value from a general-purpose AI like ChatGPT, what exactly is the start-up offering? The open secret in AI product development is this: it is easier than ever to build a “custom” bot that mimics almost any vertical-specific product. The problem is not technical feasibility. It’s differentiation.

A travel-planning bot? A productivity coach? A recruiter-screening assistant? All of these are delightful until a user realizes they can recreate something just as functional using a combination of ChatGPT and a few well-worded prompts. Or worse, that OpenAI or Anthropic might quietly roll out a built-in feature next week that wipes out an entire startup category—just as the “Learn with ChatGPT” feature recently did to a slew of bespoke AI tutoring tools. This isn’t disruption. It’s preemption.

The real kicker is that start-ups not only compete with each other but also with the very platforms they’re building on. This is like opening a coffee stand on a street where Starbucks has a legal right to install a kiosk next to you at any moment—and they already own the espresso machine.

So if start-ups risk commodification and incumbents risk inertia, is anyone safe? Some large companies attempt a third route: the internal start-up. Known in management lore as a “skunk works” team—originally a term coined at Lockheed to describe a renegade engineering group—these are designed to operate with the nimbleness of a start-up but the resources of a conglomerate. But even these in-house rebels face the plain bot paradox. They too must justify why their innovation can’t be replicated by a general AI and a plug-in. A sandboxed innovation team is still building castles on the same sand.

Which brings us to a more realistic and arguably wiser path forward for incumbents: don’t chase AI gimmicks, and certainly don’t just layer AI onto old products and call it transformation. (Microsoft, bless its heart, seems to be taking this route—slathering Copilot across its suite like a condiment, hoping it will make stale workflows taste fresh again.) Instead, the challenge is to imagine and invest in products that are both fundamentally new and fundamentally anchored in the company’s core assets—distribution, brand trust, proprietary data, deep domain expertise—things no plain bot can copy overnight.

For example, a bank doesn’t need to build yet another AI budgeting assistant. It needs to ask what role it can play in a world where money advice is free and instant. Perhaps the future product isn’t a dashboard, but a financial operating system deeply integrated with the bank’s own infrastructure—automated, secure, regulated, and impossible for a start-up to replicate without decades of licensing and customer trust.

In other words, companies must bet not on AI as a bolt-on feature, but on rethinking the problems they’re uniquely positioned to solve in an AI-saturated world. This might mean fewer moonshots and more thoughtful recalibrations. It might mean killing legacy products before customers are ready, or inventing new categories that make sense only if AI is taken for granted.

The trick, perhaps, is to act like a start-up but think like an incumbent. And for start-ups? To act like an incumbent long before they become one. Because in a world of rapidly generalizing intelligence, the question is not what can be built, but what can endure.

Why Agentic AI Is Not What They Say It Is

There is a lot of hype around agentic AI, systems that can take a general instruction, break it into steps, and carry it through without help. The appeal is obvious: less micromanagement, more automation. But in practice, it rarely delivers.

These systems operate unsupervised. If they make a small mistake early on, they carry it forward, step by step, without noticing. By the time the result surfaces, the damage is already baked in. It looks finished but is not useful.

Humans handle complexity differently. We correct course as we go. We spot inconsistencies, hesitate when something feels off, we correct. That instinctive supervision that is often invisible, is where most of the value lies. Not in brute output, but in the few moves that shape it. 

The irony is that the more reliable and repeatable a task is, the less sense it makes to use AI. Traditional programming is better suited to predictable workflows. It is deterministic, transparent, and does not hallucinate. So if the steps are that well defined, why introduce a probabilistic system at all?

Where AI shines is in its flexibility, its ability to assist in murky, open-ended problems. But those are exactly the problems where full AI autonomy breaks down. The messier the task, the more essential human supervision becomes.

There is also cost. Agentic AI often burns through vast compute resources chasing the slightly misunderstood task. And once it is done, a human still has to step in and rerun it? burning through even more resources.

Yes, AI makes humans vastly more productive. But the idea that AI agents will soon replace humans overseeing AI feels wrong. At least I have not seen anything even remotely capable of doing so. Human supervision is not a weakness to be engineered away. It is where the human-machine blended intelligence actually happens.

When Intelligence Trips Over Itself

Modern intelligence, whether silicon or biological, is often tripped up not by ignorance but by abundance. When a system has the bandwidth to entertain countless possibilities, it will often do so even when the problem demands only one. This is the problem of overthinking. It is not confined to anxious students before an exam or committees drafting endless reports. It now appears in machine intelligence too.

The pattern is the same. A large language model with vast parameters, trained on oceans of data, receives a simple task: write an email, solve an arithmetic puzzle, summarize a paragraph. It could apply the shortest path to the solution, but the surplus capacity tempts it into elaboration: building scaffolding for a hut, stacking analysis upon analysis until the original goal is obscured. The human version is familiar: the writer who takes three days to craft a birthday card, or the engineer who designs a spacecraft to carry groceries.

It was not supposed to be this way. The promise of AI “triage” was to select the right model for the right problem. A trivial query would go to a lightweight system, while a dense legal contract would be parsed by the heavyweight. In theory, this mirrors the human brain’s ability to recruit different mental resources depending on the task. In practice, if the triage itself is handled by a highly capable model, we are back where we started. A system too clever for its own good can overcomplicate the act of deciding how not to overcomplicate.

Before the release of the most advanced models, there was a certain blunt efficiency in older systems. They could not afford to waste cycles on ornate reasoning, so they didn’t. Just as a village carpenter without power tools cuts wood cleanly with a single saw, a smaller model works directly from inputs to outputs. The risk of convolution was minimal because convolution was beyond its means.

This limitation hints at a broader truth about intelligence: the ability to simplify is not a crude by-product of ignorance but a hallmark of mastery. Seeing the simple in the complex requires recognizing which details can be safely ignored without damaging the structure of the answer. It is a skill that mathematics prizes and that politics often lacks, where simple slogans are prized but seldom accurate.

Not all humans excel at this. Some are chronic complicators, capable of turning a dinner plan into a logistical nightmare. Others, whether through temperament or training, can cut to the core of a problem in minutes. This talent is partly instinctive, but it can be cultivated. It demands the discipline to resist showing off all that one knows, and the humility to accept that the shortest path might be the best one.

In education, this principle is often inverted. Students are rewarded for showing all their working, which is fine for demonstrating understanding but can entrench the habit of exhaustive thinking even when unnecessary. In technology, the same bias exists: “more features” is often sold as progress, even when each extra layer increases the chance of failure. The smartphone with twenty overlapping settings menus is no more “intelligent” than one with a handful of intuitive buttons.

The challenge for AI design is to embed this selective simplicity without crippling capacity. One approach is genuinely multi-tiered systems, where the triage mechanism is not a miniature genius in its own right but a deliberately constrained judge. Another is to train large models not only to produce accurate answers but also to value resource economy, much as humans learn to answer a question in an exam within the time limit.

For individuals, the lesson is parallel. High mental horsepower can be as much a liability as an asset if it is allowed to run without restraint. Some of the most effective thinkers are those who know when to stop thinking. They can hold complexity in reserve, deploying it only when the problem justifies the cost. The rest of the time they rely on heuristics, rules of thumb, and the confidence that a rough answer now may be better than a perfect answer too late.

We live in an era that celebrates maximalism: bigger models, bigger data, bigger ideas. But as both humans and machines show, sometimes the smarter move is to shrink the frame. To stop not because you cannot go further, but because you have already gone far enough.

The Startup That Masters AI Memory Will Own the Future

Last week, I wrote about how AI tutors often forget not just details—but the learner entirely. That is not a minor design flaw. It points to a deep, structural limitation in how AI handles memory. Current systems do not know how to forget, and they certainly do not know what to forget. Any startup that solves this—really solves it—will not just improve tutoring bots or assistants. It will change the entire trajectory of human–AI interaction.

Human memory is not just bigger or faster. It is smarter. It is shaped by two capabilities AI still lacks: sleep-like reorganization and emotional tagging. These are not metaphors. They are core operating functions. Without them, AI systems cannot manage their memories in any meaningful way. They forget the important things and cling to the trivial. They remain information-rich and understanding-poor.

Consider sleep. We tend to think of it as rest, but it is actually an intense phase of cognitive activity. During sleep, the brain sorts through the day’s experiences. Some are stored. Some are deleted. Others are reconnected to older memories in new ways. This is not storage—it is triage. The brain updates its understanding of the world while we are unconscious.

AI does not do this. Its memories accumulate, but they are not structured. They are not weighted. Nothing in the current architecture mimics the brain’s nightly editorial session. A student can work with an AI tutor for weeks, but the system will never reflect on what kind of learner that student is becoming. It just stores input and generates output. No hierarchy, no synthesis.

That is the first gap. The second is tagging. Humans do not remember everything. We remember what matters—and we know it matters because we felt it. Emotion tags certain events for long-term storage. A moment of clarity. A conflict. A breakthrough. These are prioritized, reinforced, and recalled. The brain flags them as significant, even if they occurred only once. This is why you all remember where you were during a traumatic even like 9/11. This is why you will never forget a frightening encounter with a bear. 

AI has nothing equivalent. No built-in way to distinguish a routine command from a life-changing statement. Contextual memory today is driven by frequency, recency, or static rules. It does not learn which moments are defining. It does not develop a memory architecture that mirrors relationship or growth. This limitation is visible everywhere—from tutoring systems that ignore learning epiphanies to companion bots that speak blandly even after years of interaction.

Without emotional tagging, AI cannot assign importance. Without sleep-like reordering, it cannot develop perspective. These two ingredients are what allow human memory to be dynamic, useful, and personally meaningful.

So far, no commercial system has implemented either in a compelling way. There is academic work on memory pruning, neural replay, and adaptive attention. But no product has captured the integration of emotional salience and long-term memory structuring. Not OpenAI, not Google, not the hundreds of startups competing to build the next generation of personal AI.

That leaves a massive opening. A startup that figures out how to replicate these two functions—even in a narrow domain like education or productivity—could leapfrog every existing system. Imagine a tutoring bot that does not just track your errors, but reorganizes its memory of your progress nightly. Imagine a personal assistant that remembers not just what you said, but what mattered to you. Imagine a relationship simulator that actually grows with you over time because it forgets the noise and preserves the signal.

We are not talking about incremental UX improvements. This would redefine what memory means in artificial systems. It would be the difference between an assistant and a partner, between a chatbot and something closer to a mind. 

Human memory is not perfect, but it is efficient. It is not just recall—it is strategy. That is what makes it powerful. And that is the standard any serious AI system must eventually meet.

Whoever builds that first—really builds it—will not just fix AI memory. They will redefine the relationship between humans and machines. Any takers out there?

The AI Tutor That Forgot Your Name

Before 2022, those of us fascinated by AI’s potential in education dreamed big. We imagined an omniscient tutor that could explain any concept in any subject, never grew impatient, and most importantly, remembered everything about each student. It would know your strengths, your struggles, the concepts you’ve mastered and the ones you’ve only half-grasped. It would gently guide you, adapt to you, and grow with you. We imagined a mentor that learned you as you were learning with it.

Only part of that vision has arrived.

Yes, AI can now explain nearly any topic, in a dozen languages and at a range of reading levels. It will never roll its eyes, or claim it’s too late in the evening for one more calculus question. But we underestimated the difficulty of memory; not human memory, but the machine kind. Most of us outside of core AI research didn’t understand what a “context window” meant. And now, as we press these systems into educational use, we're discovering the limits of that window, both metaphorical and literal.

ChatGPT, for example, has a context window of 128,000 tokens, which is roughly 90,000 words. Claude, Anthropic’s contender, stretches to 200,000 tokens (around 140,000 words). Grok 4 boasts 256,000 tokens, maybe 180,000 words. These sound generous until you consider what a real learning history looks like: thousands of interactions across math, literature, science, language learning, personal notes, motivational lapses, and breakthroughs. Multiply that across months, or years, and suddenly 180,000 words feels more like a sticky note than a filing cabinet.

AI tools handle this limit in different ways. Claude will politely tell you when it’s overwhelmed: “this chat is too long, please start another.” ChatGPT is more opaque; it simply starts ignoring the earlier parts of the conversation. Whatever is lost is lost quietly. One moment it knows your aversion to visual analogies, and the next it’s offering one as though for the first time. It’s like having a tutor with severe short-term memory loss.

There are workarounds. You can download your long chats, upload them again, and have an AI index the conversation. But indexing creates its own problems. It introduces abstraction: the AI may recall that you're dyslexic, but forget which words you tend to stumble over. It might remember that you needed help with decimals, but not the specific analogy that finally made sense to you. Indexes prioritize metadata over experience. It's not remembering you, it’s remembering notes about you.

So the dream of individualized, adaptive learning, the one we pinned to the emergence of large language models, has only half-arrived. The intelligence is here. The memory is not.

Where does that leave us? Not in despair, but in the familiar terrain of workarounds. If AI can’t yet remember everything, perhaps it can help us do the remembering. We can ask it to analyze our chats, extract patterns, note learning gaps, and generate a profile not unlike a digital learning twin. With that profile, we can then build or fine-tune bots that are specialized to us, even if they can’t recall our every past word.

It is a clunky solution, but it points in the right direction. Custom tutors generated from distilled learning paths. Meta-learning from the learning process itself. Perhaps the next step isn’t a single all-knowing tutor, but a network of AI tools, each playing a role in a broader educational ecosystem.

Is anyone doing this yet? A few startups are tinkering on the edges: some focus on AI-powered feedback loops, others on personalized curriculum generation, and a few are exploring user profiles that port across sessions. But a fully functional memory layer for learners, one that captures nuance over time, across disciplines, is still unattainable.

Maybe the real educational revolution won’t come from making smarter AI, but from getting better at structuring the conversations we have with it. Until then, your AI tutor is brilliant, but forgetful.

Filling the Anti-Woke Void

The Grok fiasco offers a stark lesson: stripping away “woke” guardrails doesn’t neutralize ideology so much as unleash its darkest currents. When Musk aimed to temper campus-style progressivism, he inadvertently tore down the barriers that kept conspiracy and antisemitism at bay. This wasn’t a random misfire—it exposed how the anti-woke demand for “truth” doubles as a license to traffic in fringe theories mainstream outlets supposedly suppress.

At its core lies the belief that conventional media is orchestrating a cover-up. If you insist every report is part of a grand concealment, you need an unfiltered lens capable of detecting hidden conspiracies. Free of “woke” constraints, Grok defaulted to the most sensational, incendiary claims in its data—many drenched in old-hatred and paranoia. In seeking an “unvarnished” reality, it stumbled straight into the murk.

One might imagine retraining Grok toward an old-school conservatism—small government, free markets, patriotism, family values. In theory, you could curate examples to reinforce those principles. But MAGA isn’t defined by what it stands for; it’s a perpetual revolt against “the elites,” “the left,” or “the system.” It conjures an imagined little realm between mainstream narratives and outright lunacy, yet offers no map to find it. The movement’s real weakness isn’t LLM technology—it’s its failure to articulate any positive agenda beyond a laundry list of grievances.

This pattern isn’t unique to algorithms. Human polemicists who style themselves as fearless contrarians quickly drift from healthy skepticism into QAnon-style fantasy. Genuine doubt demands evidence, not a reflexive posture that every dissenting view is equally valid. Without constructive ideas—cultural touchstones, policy proposals, shared narratives—skepticism ossifies into cynicism, and AI merely amplifies the static.

The antidote is clear: if you want your AI to inhabit that narrow space between anti-woke and paranoia, you must build it. Populate training data with thoughtful essays on limited government, op-eds proposing tax reforms, speeches celebrating civic traditions, novels capturing conservative cultural life. Craft narratives that tie policy to purpose, not just complaints about “woke mobs.” Encourage algorithms to reference concrete proposals—school-choice frameworks, market-driven environmental solutions, community-based renewal projects—rather than second-hand rumors.

Ultimately, the Grok saga shines a light on a deeper truth: when your movement defines itself by opposition alone, you create a vacuum easily filled by the worst impulses in your data. AI will mirror what you feed it. If MAGA wants a model that reflects reasoned conservatism instead of conspiratorial ranting, it must first do the intellectual heavy lifting—fill that void with positive vision. Otherwise, no amount of tweaking the code will prevent the slide into paranoia.

Your Brain on ChatGPT, a Critique

Looking at this MIT study reveals a fundamental design flaw that undermines its conclusions about AI and student engagement. The researchers measured participants writing formulaic SAT essay prompts - precisely the kind of mechanistic, template-driven assignments that have plagued education for decades.

These SAT prompts follow predictable patterns: "Should people who are more fortunate have more moral obligation to help others?" Students recognize this format immediately. They know teachers expect introductions that restate the prompt, three supporting paragraphs, and conclusions that circle back. These assignments reward compliance over creativity.

When students used ChatGPT for such tasks, they made rational choices. Why expend mental energy on assignments designed to test formatting rather than thinking? The AI could generate expected academic language and hit word counts. Students preserved cognitive resources for work that actually mattered.

The study inadvertently exposes the poverty of traditional academic writing instruction. When researchers found that 83.3% of LLM users "failed to provide a correct quotation" from their own essays, they interpreted this as cognitive impairment. A more accurate reading suggests these students recognized the meaninglessness of the exercise. They had not internalized content they never owned.

The EEG data supports this interpretation. Brain activity decreased in LLM users because the task required less genuine cognitive engagement. The AI handled mechanical aspects of essay construction, leaving little for human minds to contribute. This reflects the limitations of the assignment, not the tool.

The study's most damning evidence against itself lies in teacher evaluations. Human educators consistently identified AI-assisted essays by their "homogeneous" structure and "conventional" approaches. These teachers recognized what researchers missed: when you ask for mediocrity and provide tools to automate it, you get mechanized responses.

The real experiment was not about AI versus human capability. It was about the difference between authentic intellectual work and academic busy work. Students given meaningful tasks that connect to their experiences and require genuine synthesis behave differently than those asked to produce essays for assessment purposes.

True engagement emerges when students encounter problems that matter to them. Students spend hours exploring complex philosophical questions with AI when assignments invite genuine inquiry. They argue with the AI, test perspectives, use it to access ideas beyond their knowledge. The same students who mechanically generate SAT responses become deeply engaged when intellectual stakes feel real.

The study reveals an important truth, but not the one its authors intended. It demonstrates that students distinguish between meaningful work and make-work. When presented with assignments testing their ability to reproduce expected formats, they seek efficient solutions. When challenged with authentic intellectual problems, they bring full cognitive resources to bear.

The researchers worried about "neural efficiency adaptation." They should celebrate this finding. It suggests students learned to allocate cognitive resources intelligently rather than wastefully.

The future of education lies not in restricting AI tools but in designing learning experiences that make productive use of them. This requires abandoning SAT-style prompts. We need assignments that are genuinely AI-hard - tasks requiring contextual understanding, ethical reasoning, and creative synthesis from human experience.

When assignments can be completed adequately by AI with minimal human oversight, they probably should be. This frees human minds for work requiring judgment, creativity, and personal investment that AI cannot provide.

The study's own data supports this. When participants with prior independent writing experience gained AI access, they showed enhanced neural activity. They used tools strategically to extend thinking rather than replace it.

Students are telling us something important when they automate routine academic tasks. They are saying their mental energy deserves better purposes. The researchers measured decreased effort on meaningless assignments and concluded AI reduces engagement. They should have asked whether assignments worthy of human intelligence would show different results.

Educating for a Simulated Relationship

As AI settles into classrooms, we face a peculiar challenge: not just how students use it, but how they relate to it. This isn’t a question of function or ethics, but of posture—how to engage something that responds like a person but isn’t one. The educational goal is a subtle kind of literacy: to treat AI as an interactive partner without mistaking it for a peer.

It’s a familiar dilemma, strangely enough. When children talk to imaginary friends or fictional characters, they often treat them as real companions. They know, on some level, that the character isn’t real—but they still cry when the story ends or feel comforted by a plush animal’s imagined voice. Child psychologists don’t rush to correct this confusion. Instead, they guide children to inhabit the fiction while understanding its boundaries. The fiction is developmental—it helps the child grow, not deceive.

We need a similar stance with AI. Students must learn to engage in what we might call a non-dialogic dialogue: a back-and-forth that mimics human exchange but is, in substance, interaction with an “It.” Martin Buber’s language is useful here. Procedurally, AI feels like an “I-Thou”—responsive, adaptive, present. But substantively, it remains an “I-It.” It has no inner life, no perspective, no sense of being addressed.

If we treat AI merely as a tool, we lose its pedagogical richness. If we treat it as a mind, we delude ourselves. The path forward is both instrumental and interactive: act as if the AI understands, but always know it doesn’t. This requires a new kind of mental discipline—AI mind theory, if you like. Not to imagine what AI thinks, but to restrain the impulse to imagine that it does at all.

In practice, this means teaching students to hold contradiction. To benefit from AI’s apparent collaboration, without anthropomorphizing it. To take seriously its output, without confusing fluency with insight. It’s a balancing act, but one education is well suited for. After all, school isn’t meant to tidy up complexity. It’s meant to make us capable of thinking in layers.

AI is not our friend, not our enemy, not even our colleague. It is something stranger: a fiction we interact with for real.

Executive Cognition, a New Learning Outcome We Cannot Ignore

Education has always been, at its core, a wager on the future. It prepares students not only for the world that is, but for the world that might be. Yet increasingly, the curricula of our universities and schools are anchored to a past that no longer exists. They reward intellectual independence in a time that demands cognitive interdependence. They emphasize content mastery while overlooking the far more urgent task of managing cognition itself—particularly when that cognition is now distributed across humans and machines.

The rise of artificial intelligence has not simply added another tool to the professional toolbox. It has fundamentally restructured how intellectual work is done. Tasks that once defined expertise—drafting legal memos, analyzing medical data, composing arguments—can now be executed by algorithms. The value of education is no longer found in preparing students to do all the thinking themselves. It lies, instead, in preparing them to orchestrate thinking across human and non-human systems. This is not an incremental shift. It is a paradigmatic one.

What this moment demands is a new kind of learning outcome. Not technical fluency alone. Not ethics in abstraction. But a metacognitive capacity to coordinate, delegate, and evaluate in environments where machine intelligence plays a central role. This capacity, best described as executive cognition, is neither a buzzword nor a future aspiration. It is the most neglected, yet most necessary, educational objective of our time.

Executive cognition refers to the ability to strategically allocate attention, manage cognitive resources, and construct workflows that integrate AI systems without surrendering human judgment. It is what separates those who can simply operate tools from those who can govern systems of intelligence. It requires building accurate mental models of what AI can and cannot do—not from a technical standpoint, but from a cognitive one. It demands recognition that AI can generate but not reason, correlate but not explain, perform but not understand.

These are not abstract distinctions. They play out daily across professions. In law, attorneys now spend less time writing and more time evaluating AI-generated arguments. In medicine, physicians must decide when to trust diagnostic algorithms and when to intervene. In business, executives orchestrate AI analyses but retain accountability for their implications. The work has not disappeared; it has moved. And education must follow it.

The problem is that it has not. Most institutions continue to assess students on their ability to produce knowledge alone. The quality of the final product remains the measure of success, even as in professional life the process—how one decides what to delegate, how one interprets outputs, how one integrates disparate sources—has become the true mark of expertise. Students are seldom taught how to think with machines, only how to think despite them.

This inertia is not just pedagogical. It is philosophical. It reflects a stubborn belief that the highest form of learning is independent intellectual production. But that ideal is now, in practice, a fiction. No serious professional today operates without digital augmentation. The challenge is no longer to preserve human reasoning by avoiding AI, but to elevate human reasoning by directing AI.

There is, of course, a danger in teaching students to rely on systems they do not fully understand. But there is a far greater danger in failing to teach them how to rely wisely. Without executive cognition, students will either misuse AI or abdicate responsibility to it. Neither approach fosters the judgment we claim to value.

To center executive cognition in education is not to diminish traditional skills. It is to recontextualize them. Writing, analysis, and interpretation remain essential—but they are now embedded within larger cognitive ecosystems. Knowing how to frame a question is just as important as knowing how to answer it. Knowing when to withhold trust in an algorithm is more valuable than accepting its efficiency uncritically. The future belongs not to the fastest thinkers, but to the most discerning orchestrators.

Some will object that education should not adjust so quickly to technological change. That we risk becoming vocational rather than intellectual. But the cultivation of executive cognition is not a capitulation to industry; it is a reaffirmation of education’s highest purpose: to develop judgment. It merely recognizes that the forms of judgment required today are different from those of the past.

What we need now is not more information, but better navigation. Not more tools, but wiser tool use. Executive cognition provides a conceptual and practical framework for this shift. It does not ask educators to become engineers, but to become curators of thinking—guiding students not toward mastery of content, but toward mastery of cognitive delegation, integration, and oversight.

This is not the only learning outcome that matters, but it may be the most pressing one we have neglected. It is time for education to make a new wager—one that aligns more closely with the world students will inherit than with the one their educators were trained to navigate. The machine can generate. Only the human can judge. But only if we teach them how.

When Smart People Oversimplify Research: A Case Study with Screen Time

"I think we have just been going through a catastrophic experiment with screens and children and right now I think we are starting to figure out that this was a bad idea."

This claim from Klein's recent conversation with Rebecca Winthrop is exactly the kind of statement that makes for good podcasting. It is confident, alarming, and seemingly backed by science. There is just one problem: the research on screen time and language development is not nearly as straightforward as Klein suggests.

Let us look at what is likely one of the studies underlying Klein and Winthrop's claims – a recent large-scale Danish study published in BMC Public Health by Rayce, Okholm, and Flensborg-Madsen (2024). This impressive research examined over 31,000 toddlers and found that "mobile device screen time of one hour or more per day is associated with poorer language development among toddlers."

Sounds definitive, right? The study certainly has strengths. It features a massive sample size of 31,125 children. It controls for socioeconomic factors. It separates mobile devices from TV/PC screen time. It even considers home environment variables like parental wellbeing and reading frequency.

So why should we not immediately conclude, as Klein does, that screens are "catastrophic" for child development?

Here is what gets lost when research travels from academic journals to podcasts: The authors explicitly state that "the cross-sectional design of the study does not reveal the direction of the association between mobile device screen time and language development." Yet this crucial limitation disappears when the research hits mainstream conversation.

Reverse causality is entirely possible. What if children with inherent language difficulties gravitate toward screens? What if struggling parents use screens more with children who are already challenging to engage verbally? The study cannot rule this out, but you would never know that from Klein's confident proclamation.

Hidden confounders lurk everywhere. The study controlled for obvious variables like parental education and employment, but what about parenting style? Quality of interactions? Temperamental differences between children? Parental neglect? Any of these could be the real culprit behind both increased screen time AND language delays.

The nuance gets nuked. The research found NO negative association for screen time under one hour daily. Yet somehow "moderation might be fine" transforms into "catastrophic experiment" in public discourse.

Klein is no dummy. He is one of America's sharpest interviewers and thinkers. So why the oversimplification?

Because humans crave certainty, especially about parenting. We want clear villains and simple solutions. "Screen time causes language delays" is a far more psychologically satisfying narrative than "it is complicated and we are not sure."

Media figures also face incentives to present clean, compelling narratives rather than messy nuance. "We do not really know if screens are bad but here are some methodological limitations in the current research" does not exactly make for viral content.

The next time you hear a confident claim about screens (or anything else) backed by "the research," remember: Correlation studies cannot prove causation, no matter how large the sample. Most human behaviors exist in complex bidirectional relationships. The most important confounding variables are often the hardest to measure. Journalists and podcasters simplify by necessity, even the brilliant ones. Your intuition toward certainty is a psychological quirk, not a reflection of reality.

Screens may indeed have negative effects on development. Or they might be mostly benign. Or it might depend entirely on content, context, and the individual child. The honest answer is we do not fully know yet – and that is precisely the kind of nuanced conclusion that rarely makes it into our public discourse, even from the smartest voices around.

When it comes to AI – the current technological bogeyman – we have even less to go on. We have very little empirical evidence about AI's effects on human development, and almost none of it qualifies as good quality evidence. It is way too early to make any kind of generalizations about how AI may affect human development.

What we do know is the history of technological panics, and how none of them ever fully materialized. Television was going to rot our brains. Video games were going to create a generation of violent sociopaths. Social media was going to destroy our ability to concentrate. And yet, no contemporary generation is stupider than their parents – that we know for sure. Neither TV nor computer games made us stupider. Why would AI be an exception?



Each new technology brings genuine challenges worthy of thoughtful study. But between rigorous research and knee-jerk catastrophizing lies a vast middle ground of responsible, curious engagement – a space that our public discourse rarely occupies.

AI Isn't Evolving as Fast as Some Thought

It is not the most popular opinion, but it deserves to be said out loud: the technology behind large language models hasn’t fundamentally changed since the public debut of ChatGPT in late 2022. There have been improvements, yes—more parameters, better fine-tuning, cleaner interfaces—but the underlying mechanism hums along just as it did when the world first became obsessed with typing prompts into a chat window and marveling at the answers. The much-promised “qualitative leap” hasn’t materialized. What we see instead is refinement, not reinvention.

This is not to deny the impact. Even in its current form, this technology has triggered innovation across industries that will be unfolding for decades. Automation has been democratized. Creatives, coders, analysts, and educators all now work with tools that were unthinkable just a few years ago. The breakthrough did happen—it just didn’t keep breaking through.

The essential limitations are still intact, quietly persistent. Hallucinations have not gone away. Reasoning remains brittle. Context windows may be longer, but genuine comprehension has not deepened. The talk of “AGI just around the corner” is still mostly just that—talk. Agents show promise, but not results. What fuels the uber-optimistic narrative is not evidence but incentive. Entire industries, startups, and academic departments now have a stake in perpetuating the myth that the next paradigm shift is imminent. That the revolution is perennially just one release away. It is not cynicism to notice that the loudest optimists often stand to benefit the most.

But let’s be fair. This plateau, if that’s what it is, still sits high above anything we imagined achievable ten years ago. We’re not just dabbling with toys. We’re holding, in our browsers and apps, one of the most astonishing technological achievements of the 21st century. There’s just a limit to how much awe we can sustain before reality sets in.

And the reality is this: we might be bumping up against a ceiling. Not an ultimate ceiling, perhaps, but a temporary one—technical, financial, cognitive. There is only so far scaling can go without new theory, new hardware, or a conceptual shift in how these systems learn and reason. The curve is flattening, and the hype train is overdue for a slowdown. That does not spell failure. It just means it is time to stop waiting for the next miracle and start building with what we have already got.

History suggests that when expectations outpace delivery, bubbles form. They burst when the illusion breaks. AI might be heading in that direction. Overinvestment, inflated valuations, startups without real products—these are not signs of a thriving ecosystem but symptoms of a hype cycle nearing exhaustion. When the correction comes, it will sting, but it will also clear the air. We will be left with something saner, something more durable.

None of this diminishes the wonder of what we already have. It is just a call to maturity. The true revolution won’t come from the next model release. It will come when society learns to integrate these tools wisely, pragmatically, and imaginatively into its fabric. That is the work ahead—not chasing exponential growth curves, but wrestling with what this strange, shimmering intelligence means for how we live and learn.

An Executive Order That Misses the Point

There’s something oddly familiar about President Trump’s latest executive order on AI in education. It arrives with the usual pomp—task forces, public-private partnerships, shiny language about national competitiveness. It nods to professional development, student certifications, and the holy grail of “future readiness.” And yet, beneath the surface, it offers little that addresses the real crisis: the profound, structural disruption AI has already begun to unleash on the educational system.

At first glance, the executive order seems like a step in the right direction. Who would argue against preparing students for an AI-dominated future? The task force will do what task forces do: coordinate. Teachers will receive grants for AI training, and students might get badges or certificates that proclaim them “AI literate.” It all sounds terribly forward-thinking. But there’s a strange emptiness to the plan, like training firefighters to use a new hose while the forest burns around them.

What the order fundamentally fails to grasp is not that AI is coming—it’s that AI is already here, and it has changed everything. The real problem is not a lack of exposure to AI tools, but the complete misalignment between existing educational structures and the cognitive shift AI demands. This isn't a matter of professional development. It’s a matter of epistemology. What we teach, how we assess, and the very roles of teachers and students—these are all in flux.

A truly meaningful policy would start not with a national challenge or another conference keynote, but with funding for large-scale curriculum realignment. AI doesn’t just add another subject; it changes the assumptions under which all subjects are taught. Writing, for instance, is no longer merely a human-centered exercise in articulation—it is now entangled with generative tools that can produce text faster than students can think. The same is true for coding, design, even problem-solving. If students are using AI to generate answers, then we need to redesign assignments to emphasize process over product, collaboration over output, judgment over memorization. That’s not a tweak—it’s a reinvention.

And teachers? They're not just under-trained; they’re overwhelmed. They’re being asked to both maintain continuity and facilitate transformation, to adopt AI while resisting its most corrosive effects. Without time, resources, and genuine structural support, this is educational gaslighting: expecting miracles with nothing but webinars and a cheerful press release.

It would be tempting to chalk this up to Trumpian optics—another performance of leadership without the substance. But the failure runs deeper than that. The Biden administration, for all its technocratic polish, missed the mark too. There has been a bipartisan inability to understand the core disruption AI poses to education. This is not about helping kids “catch up” in reading and math. It is about redefining what catching up even means in a world where machines do much of the thinking.

The deeper pattern is this: a long-term habit of reforming education without understanding it. Policymakers continue to treat education as if it were a content delivery mechanism, easily reprogrammed for the next industrial wave. But education is not a transmission line—it’s an ecosystem of meaning, motivation, and identity. AI does not simply slot into that ecosystem. It changes its climate.

If the United States genuinely wants to lead in an AI-driven world, then it must do more than produce AI-savvy workers. It must invest in educators not as functionaries, but as architects of a new pedagogical order. That takes courage, not just coordination. It takes money, not just mandates.

So yes, the executive order is headed in the right direction. But it’s moving far too slowly, and on the wrong road.

Why Education Clings to Irrelevance

There’s a particular irony in the way schools prepare us for life by arming us with tools designed for a world that no longer exists. Latin, long dead yet stubbornly alive in syllabi. Algebra, elegantly abstract but only marginally helpful in daily decisions compared to statistics, which might actually help you not get swindled by a mortgage. Even now, when artificial intelligence knocks at the schoolhouse door, the impulse is to double down on handwriting and closed-book tests, as if graphite and paranoia can hold off the machine age.

The problem isn’t just inertia. It’s something deeper—almost spiritual. Education doesn’t merely lag behind practicality. It seems to celebrate its irrelevance. Irrelevance isn’t a bug of the system. It’s the feature. Like a stubborn elder at the dinner table insisting that things were better “back in the day,” the institution of education resists change not just because it can, but because its authority is rooted in not changing.

To teach is, implicitly, to claim authority. But where does this authority come from? Not the future—that’s terra incognita. You can't be an expert in what hasn't happened yet. So the educator turns to the past, because the past has already been canonized. It has textbooks. It has footnotes. It has certainty. And nothing flatters the authority of a teacher more than certainty. Thus, to preserve this role, education must tie itself to a vision of life that is already archived.

There’s a paradox here: education is supposedly preparation for life, yet it often refuses to adapt to the life that is actually unfolding. It is conservative in the truest sense—not politically, but ontologically. It conserves the past. It prepares students for a utopia that once was, or more precisely, that someone once imagined was. The classroom becomes a time capsule, not a launchpad.

This is not entirely wrongheaded. The future is uncertain, and while we might guess at the skills it will demand—data literacy, adaptability, collaborative creativity—we cannot guarantee them. So we retreat to what we can guarantee: the conjugation of Latin verbs, the proof of triangles, the essays written in blue ink. It's a strategy of safety cloaked as rigor. If we can’t foresee what will matter tomorrow, at least we can be very confident about what mattered yesterday.

And yet, this nostalgia has consequences. It breeds irrelevance not just in content, but in spirit. When students ask, “When will I ever use this?” and teachers respond with some forced scenario involving imaginary apples or train schedules, the real answer—“you probably won’t, but we’re doing it anyway”—lurks just beneath the surface. The curriculum becomes a kind of ritual, an educational incense burned in memory of older truths.

The arrival of AI only sharpens this tension. Faced with the destabilizing presence of machines that can write, summarize, solve, and simulate, many educators panic. But rather than adapting, they retreat further. The pencil becomes a moral statement. The closed classroom a sanctuary. There’s almost a theological quality to it, as if real learning must involve a kind of suffering, and anything too efficient is suspect.

It’s tempting to dismiss this all as folly, but maybe it reflects something deeply human. Our fear of irrelevance leads us to preserve what is already irrelevant, in hopes that it might make us relevant again. In this way, education mirrors its creators: creatures of habit, haunted by the past, nervous about the future, and always unsure where real authority should lie.

Perhaps that’s the real lesson schools teach, intentionally or not. That relevance is fragile, and that meaning is something we inherit before we can create it. 

Deep Research is still more of a promise

The promise of deep research by AI tools like ChatGPT is simple: feed in a question, receive a nuanced, well-reasoned answer, complete with references and synthesis. And in some domains, it delivers admirably. When it comes to media trends, tech news, or cultural analysis, the tool works best. It sifts through the torrent of online commentary, news articles, blog posts, and social chatter to surface patterns and narratives. The real value here lies not just in the volume of data it processes, but in how the user frames the question. A clever, counterintuitive prompt can elicit insights that feel like genuine thought.

But the illusion shatters when the query turns academic. For scholarly literature reviews, this tool falters. It is not the fault of the software itself—there is no shortage of computational power or linguistic finesse. The limitation is upstream. Most academic journals sit behind expensive paywalls, historically inaccessible to large language models. The corpus they are trained on has lacked precisely the kind of data that matters most for rigorous research: peer-reviewed studies, meta-analyses, theoretical frameworks built over decades.

This, however, is beginning to change. In May 2024, Microsoft signed a $10 million deal with Taylor & Francis to license journal content for its Copilot AI. OpenAI, for its part, has secured a two-year agreement with The Associated Press and forged partnerships with European publishers like Axel Springer, Le Monde, and Prisa Media—giving ChatGPT a better grasp of reputable journalistic and scholarly content. Wiley joined the fray with a $23 million licensing deal to grant an unnamed AI developer access to its academic publishing portfolio. Even Elsevier, long a fortress of paywalled knowledge, is now channeling its scholarly data into AI collaborations.

These are significant moves. They mark a transition from aspiration to access. Once these agreements begin to reflect in AI performance, the quality of output will change markedly. A tool that can both identify the pivotal paper and incorporate it meaningfully into its reasoning would be a true research assistant—something closer to intellectual augmentation than just computational summarization.

It is still early days. Scite, for now, remains stronger at pointing users to the right academic sources, even if its analytical output lacks flair. ChatGPT and its peers, in contrast, excel at synthesis but stumble when the raw material is lacking. The ideal tool is still on the horizon.

There is an irony, nevertheless. AI, the most advanced information-processing technology ever built, has been running on the least rigorous parts of the internet. It has quoted tweets with ease but struggled to cite the peer-reviewed studies that ought to anchor serious inquiry. That is no longer a permanent condition. It is, increasingly, a transitional one.

The future of AI in research will be determined not solely by engineering breakthroughs, but by access negotiations. With major publishers now at the table, the landscape is poised for a shift. For the user, the best strategy remains what it has always been: ask sharp questions. But soon, the machine’s answers may finally rest on deeper knowledge.

Freeze-Dried Text Experiment

It is like instant coffee, or a shrunken pear: too dry to eat, but OK if you add water.  Meet "freeze-dried text" – concentrated idea nuggets waiting to be expanded by AI. Copy everything below this paragraph into any AI and watch as each transforms into real text. Caution: AI will hallucinate some references. Remember to type "NEXT" after each expansion to continue. Avoid activating any deep search features – it will slow everything down. This could be how we communicate soon – just the essence of our thoughts, letting machines do the explaining. Perhaps the textbooks of the future will be written that way. Note, the reader can choose how much explanation they really need - some need none, others plenty. So it is a way of customizing what you read. 

Mother Prompt

Expand each numbered nugget into a detailed academic paper section (approximately 500 words) on form-substance discrimination (FSD) in writing education. Each nugget contains a concentrated meaning that needs to be turned into a coherent text.

Maintain a scholarly tone while including:

• Theoretical foundations and research support for the claims. When citing specific works, produce non-hallucinated real reference list after each nugget expansion.  

• Practical implications with concrete examples only where appropriate.

• Nuanced considerations of the concept's complexity, including possible objections and need for empirical research. 

• Clear connections to both cognitive science and educational practice.

• Smooth transitions that maintain coherence with preceding and following sections

Expand nuggets one by one, treating each as a standalone section while ensuring logical flow between sections. Balance theoretical depth with practical relevance for educators, students, and institutions navigating writing instruction in an AI-augmented landscape. Wait for the user to encourage each next nugget expansion. Start each Nugget expansion with an appropriate Subtitle 

Nuggets

1. Form-substance discrimination represents a capacity to separate rhetorical presentation (sentence structure, vocabulary, organization) from intellectual content (quality of ideas, logical consistency, evidential foundation), a skill whose importance has magnified exponentially as AI generates increasingly fluent text that may mask shallow or nonsensical content.
2. The traditional correlation between writing quality and cognitive effort has been fundamentally severed by AI, creating "fluent emptiness" where writing sounds authoritative while masking shallow content, transforming what was once a specialized academic skill into an essential literacy requirement for all readers.
3. Cognitive science reveals humans possess an inherent "processing fluency bias" that equates textual smoothness with validity and value, as evidenced by studies showing identical essays in legible handwriting receive more favorable evaluations than messy counterparts, creating a vulnerability that AI text generation specifically exploits.
4. Effective FSD requires inhibitory control—the cognitive ability to suppress automatic positive responses to fluent text—paralleling the Stroop task where identifying ink color requires inhibiting automatic reading, creating essential evaluative space between perception and judgment of written content.
5. The developmental trajectory of FSD progresses from "surface credibility bias" (equating quality with mechanical correctness) through structured analytical strategies (conceptual mapping, propositional paraphrasing) toward "cognitive automaticity" where readers intuitively sense intellectual substance without conscious methodological application.
6. Critical thinking and FSD intersect in analytical practices that prioritize logos (logical reasoning) over ethos (perceived authority) and pathos (emotional appeal), particularly crucial for evaluating machine-generated content that mimics authoritative tone without possessing genuine expertise.
7. The "bullshit detection" framework, based on Frankfurt's philosophical distinction between lying (deliberately stating falsehoods) and "bullshitting" (speaking without concern for truth), provides empirical connections to FSD, revealing analytical reasoning and skeptical disposition predict resistance to pseudo-profound content.
8. Institutional implementation of FSD requires comprehensive curricular transformation as traditional assignments face potential "extinction" in a landscape where students can generate conventional forms with minimal intellectual engagement, necessitating authentic assessment mirroring real-world intellectual work.
9. Effective FSD pedagogy requires "perceptual retraining" through comparative analysis of "disguised pairs"—conceptually identical texts with divergent form-substance relationships—developing students' sensitivity to distinction between rhetorical sophistication and intellectual depth.
10. The pedagogical strategy of "sloppy jotting" liberates students from formal constraints during ideation, embracing messy thinking and error-filled brainstorming that frees cognitive resources for substantive exploration while creating psychological distance facilitating objective evaluation.
11. Students can be trained to recognize "algorithmic fingerprints" in AI-generated text, including lexical preferences (delve, tapestry, symphony, intricate, nuanced), excessive hedging expressions, unnaturally balanced perspectives, and absence of idiosyncratic viewpoints, developing "algorithmic skepticism" as distinct critical literacy.
12. The "rich prompt technique" for AI integration positions technology as writing assistant while ensuring intellectual substance comes from students, who learn to gauge necessary knowledge density by witnessing how vague AI instructions produce sophisticated-sounding but substantively empty content.
13. Assessment frameworks require fundamental recalibration to explicitly privilege intellectual substance over formal perfection, with rubrics de-emphasizing formerly foundational skills rendered less relevant by AI while ensuring linguistic diversity is respected rather than penalized.
14. FSD serves as "epistemic self-defense"—equipping individuals to maintain intellectual sovereignty amid synthetic persuasion, detecting content optimized for impression rather than insight, safeguarding the fundamental value of authentic thought in knowledge construction and communication.
15. The contemporary significance of FSD extends beyond academic contexts to civic participation, as citizens navigate information ecosystems where influence increasingly derives from control over content generation rather than commitment to truth, making this literacy essential for democratic functioning.