Last fact-check: May 25, 2026
The risk that doesn’t get enough airtime in the debate over AI in education isn’t cheating, and it isn’t hallucination. Both of those are real, but both have visible signatures and somebody is paying attention to them. The risk that goes mostly unmonitored is quieter: that the people who use AI heavily, every day, for years, will lose the cognitive skills the AI was supposed to support.
This isn’t a moral panic. It’s a structural observation about how skills are maintained. Anything you outsource for long enough, you get worse at. People who never write longhand have worse handwriting. People who never do mental math get slower at it. People who use GPS for every trip lose the ability to navigate a city from memory. These are not catastrophes. They are predictable consequences of substitution, and they are worth paying attention to when the thing being substituted is the cognitive work that school is supposed to develop.
This article is for anyone using AI heavily — students, professionals, writers, anyone — who wants to keep using it while not losing the skills underneath. It’s part of Tygart Media’s free AI Literacy curriculum. The foundational articles are here, here, and here.
The specific cognitive risk
The skills most at risk of erosion from heavy AI use are the ones the model is best at. Specifically: generating ideas, structuring arguments, finding the right phrasing, drafting prose, summarizing, and producing the first version of any cognitive product. The model is excellent at all of these, which is why people use it, which is exactly why these are the skills that atrophy when you use it for everything.
The skills less at risk: deeply understanding source material, judging the quality of a finished product, choosing what’s important to focus on, building a mental model of a complex domain, and applying knowledge under pressure when no AI is available. These skills get exercised whether or not you use AI, because they’re upstream of AI use.
The pattern that emerges from this asymmetry: AI tends to atrophy production skills and preserve evaluation skills. People who use AI heavily often become very good at recognizing whether an output is good or bad, while losing the muscle to produce a good output without help. This is functional for many real-world tasks — recognizing quality is often more important than producing it — but it’s a disaster for educational contexts, where the entire point of an assignment is to develop the production skill.
There’s a name for this in research literature on tool use: skill substitution. The tool replaces the skill, and as long as the tool is available, no one notices. The skill comes back into demand only when the tool is absent, and at that point it may not be there anymore. For students who will eventually graduate, take exams without AI, or work in contexts where AI is unavailable or inappropriate, the absence of the underlying skill becomes visible at exactly the worst time.
Cal Poly philosophy professor Ryan Jenkins put it simply, in his interview with CalMatters: “The bread and butter of philosophy is reflecting on your own ideas and trying to sort out what you believe and why. If you have a tool that does that for you, then you’re being denied an opportunity to practice that skill.” Substitute “philosophy” for any discipline that involves thinking — which is most of them — and the principle holds.
The trap of mistaking recognition for understanding
One of the most useful and most dangerous things AI does is make complex topics feel approachable. You ask the model to explain quantum mechanics in plain language, and it does. You feel like you understood. You probably did understand the surface explanation. But there’s a gap between understanding an explanation and being able to do the underlying thinking — a gap that traditional learning closes with practice and that AI-mediated learning often skips entirely.
This shows up most clearly when you try to apply the knowledge. You can read a hundred AI-generated explanations of a statistics concept, feel like you understand it, and still be unable to actually do statistics. The recognition was real. The competence isn’t.
The dynamic is similar to watching cooking videos. You can watch a great chef chop an onion, narrate the technique, explain the principle, and you’ll feel like you understand. Then you pick up a knife and discover that watching wasn’t doing. The hand has to learn separately.
AI-assisted learning has this same gap. The fix is not to stop using AI. The fix is to do the work yourself often enough that the muscle stays. You can read AI explanations, but somewhere in the process, you have to put the explanations down and produce something — solve the problem, write the proof, draft the argument, explain it to a friend — without the AI in the loop.
The asymmetry between writing and reading
One specific case worth naming: writing. Heavy AI use for writing tends to atrophy writing in a way that’s particularly hard to notice, because reading remains intact. People can read sharp prose, recognize when something is well-written, and even articulate why — while progressively losing the ability to produce sharp prose themselves.
This is because writing is a different cognitive operation than reading. Reading is recognition: you’re parsing structure that already exists. Writing is generation: you’re producing structure that doesn’t exist yet. The model does the generation for you when you use it for drafting. The reading part — evaluating what came out — stays exercised. The writing part doesn’t.
You can verify this in yourself. Try writing a 500-word essay on something you care about, in a single sitting, with no AI assistance. If you do this regularly and it stays easy, your writing muscles are fine. If you used to be able to do it and now it feels strangely hard — the words don’t come, you start sentences you can’t finish, you find yourself wanting to “just check what ChatGPT would say” — your writing muscles are weakening.
This is recoverable. The fix is the same as physical training: practice without the assistive device. Not always, not even most of the time, but reliably enough to keep the underlying capacity intact.
The category of “no-AI time”
A practice that works for many heavy users: designate explicit time in which AI is not used. Not as a moral position, but as a maintenance practice. The way someone who drives everywhere might still walk three miles a week to stay in shape.
What this looks like varies. For students, it might be that first drafts of papers happen with no AI involvement, no matter how rough they end up. Editing can use AI. Final polish can use AI. The first draft, the part where your thinking actually has to happen, doesn’t.
For professionals, it might be that the first ten minutes of any analytical task happen with no AI. You read the problem, you sketch a response, you outline what you actually think — and only then do you bring in AI to refine, check, or extend. This preserves the part of thinking that’s hardest to outsource and easiest to lose: the initial framing.
For writers, it might be that journaling, longhand drafts, or any personal writing happens with no AI. The professional output can use AI. The practice that maintains the underlying capacity does not.
The principle is the same in each case: a deliberate, recurring exercise of the skills that AI substitutes for. Not as a return to a pre-AI world. As a way of staying capable in the AI world.
The specific problem of difficulty avoidance
One of the subtle harms of constant AI use is that it makes difficulty avoidable. You’re stuck on a problem. The friction is real. You don’t want to feel it. AI removes the friction in three seconds. Over time, your tolerance for productive difficulty erodes — and productive difficulty is the cognitive state in which most real learning happens.
This is the same dynamic as physical fitness. The body adapts to the load it’s regularly given. Take away the load and the adaptation reverses. Cognitive load is the same. The struggle to remember something, work through an unfamiliar problem, sit with confusion until it resolves — these are the loads under which cognitive capacity is built and maintained. Removing them feels like a kindness in the moment. Sustained, it’s an injury.
The students who will benefit most from AI in the long run will be the ones who use it as a complement to difficult work rather than a substitute for it. The students who will be most damaged will be the ones who use it to avoid the experience of not knowing the answer. Both groups will have used AI extensively. The difference will be whether they ever experienced the part of learning that AI was inserted to bypass.
The reading problem
One more specific case, because it’s getting common: using AI to read for you.
The pattern: a student is assigned a 40-page chapter. They paste it into ChatGPT. They ask for a summary. They read the summary. They consider themselves to have done the reading.
This works in the narrow sense that they can probably participate in a class discussion. It fails in the deeper sense that they didn’t read the chapter. Reading isn’t just acquiring the propositional content of a text. It’s the experience of moving through the author’s thinking at the author’s pace, getting confused where the author intended confusion, noticing what the author chose to emphasize and what they chose to skip, picking up the texture of how the argument is built.
A summary captures the propositional content. It loses everything else. Over a semester of summary-based reading, what’s lost is the ability to do close reading at all — to track an argument across many pages, to notice rhetorical moves, to distinguish surface claims from underlying assumptions. These are skills that take years to develop and weeks to lose.
This doesn’t mean never using AI to support reading. Using AI to clarify a confusing passage, define an unfamiliar term, or identify what argument is being made — these are aids to reading, not substitutes for it. The line is whether the AI is helping you read, or replacing the reading.
Signs your AI use has crossed into substitution
Some symptoms worth watching for, in yourself or in students you teach:
- You can no longer write a coherent paragraph in a single sitting without AI assistance
- You feel anxious or stuck when trying to think through a problem without checking what AI says first
- You can recognize good writing but increasingly struggle to produce it
- You can summarize material but can’t remember it a week later
- You can pass tests but feel like you didn’t learn the material
- You finish assignments quickly but couldn’t redo them without AI
- You have trouble sitting with confusion long enough for understanding to develop
- You’re not sure which of your recent ideas are actually yours
None of these symptoms mean you should stop using AI. They mean the balance has tipped toward substitution, and some recalibration is in order.
The honest framing for professors
For instructors thinking about this — and the CalMatters reporting suggests many are — the dependency question is harder to address than the cheating question. Cheating has a moment, an act, a detectable signature. Dependency is gradual, ambient, and largely invisible until a student tries to function without AI and finds they can’t.
The pedagogical answer is to design assignments that exercise the skills AI substitutes for. Not as anti-AI assignments, but as assignments where the student’s growth depends on doing some part of the work without assistance. In-class writing, oral exams, problem-solving in real time, drafts produced under time pressure, work where the process is graded along with the product. These don’t ban AI. They ensure that some of the cognitive work happens in environments where AI isn’t doing it.
The strongest version of this isn’t a prohibition. It’s a design principle. Assignments that build skill have to include practice of the skill. The fact that AI exists doesn’t change what skill-building requires. It just makes it more important to be deliberate about what gets practiced.
The closing point
The CSU students filling out their AI surveys are aware of this risk. Eighty-two percent of them said they worry AI will negatively affect their future job security. Some of that worry is about external displacement — AI taking jobs. Some of it, harder to name but more important, is about internal displacement: the worry that the version of themselves that uses AI for everything is a less capable version than the one who could do without it.
This curriculum is built on the premise that AI is here, useful, and going to be used heavily by almost everyone. The point is not to stop. The point is to use it in a way that doesn’t quietly subtract the capacity it was supposed to amplify. That requires noticing, deliberateness, and a willingness to do some of the work the hard way, on purpose, often enough that the underlying skill survives.
Tools are best when they extend their users. They’re worst when they replace what their users used to be able to do. Whether AI ends up in the first category or the second, for any given person, depends almost entirely on how that person decides to use it.
About this knowledge node: This is a cluster article in Tygart Media’s AI Literacy content sprint. It’s licensed for use in any classroom, training program, custom GPT, or Claude Project as long as attribution is maintained. The pillar article that introduces the sprint is here.
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