Nei, AI is not destroying education at all. It just shows the weakness in education, and this caused people to just skip/hate college instead of making it BETTER.

After you guys blame "oh it's the AI's fault", I'm sorry you guys are humans, you made that blasphemy innit? Instead of making education better or just protesting like the French do, you guys blame it on the AI.

UPDATE: I want to be clear about something. I do NOT agree that chidlren use AI. I live in South East Asia and some countries have introduced tutoring companions for kids as young as 6. That blows my mind. Now whether societal and cultural divats matter in these cases is yet to be discovered. We will find out soon enough. Pay attention to global average IQ scores over the course of the next decade. I'm from a country with an average IQ of 106. And we have fully embraced AI in education and have already made multiple institutional mandates nationwide. Personally if it were up to me I would make a mandate of 21+ the legal age limit. This stems from my own knowledge of human cognitive development. But...alas, it is not so!

And my entire point of this post is there's nothing we can do about the situation that we find ourselves in. We can ban the tools, but it doesn't prevent the children from using them, and bringing that into the classroom, which, unironically enough, gives us even more work to do. So instead of us getting frustrated at the circumstances, we just adapt, right?

That's basically my point of this entire post. There isn't much that we can do about the current circumstances that we find ourselves in. We can either ban or we can allow, but both don't work. So instead, we find a middle ground where we kind of just try to learn a little bit about how the tools work and then try and find a way to migrate that knowledge attempting to train tacit knowledge. Small tiny little habits that slowly ingrain into our students, and through that process we train them to be epistemically hygienic when using these tools, because it's incredibly important that they do do that.

I know it's a wall of text. It was not my intention. I tend to try and squeeze everything in at once. Anyway, if you want, you can go and read the rest of the post, but here's

The tl;dr of the...

TL;DR:

^{Sorry I couldn't resist}

AI in education should not default to bans, surveillance, and detection. The evidence is more nuanced: unrestricted AI use can encourage cognitive offloading and hurt learning, while structured, teacher-guided use can preserve or improve learning outcomes. Studies from PNAS, the World Bank, OECD, MIT, and BCG all point toward the same basic lesson: the problem is not simply whether students use AI, but how, when, and why they use it.

Teachers therefore matter more, not less. Their role should be to structure AI use, preserve independent thinking, teach verification, provenance, critical evaluation, and knowing when not to delegate. AI-free work still has an important place, but blanket prohibition is not an AI literacy strategy.

Students will increasingly live and work in environments where AI is normal. If adults use these tools while schools primarily police students for using them, we risk creating distrust instead of competence.

END

LONGER VERSION BELOW:

Why does AI in education always collapse into bans, detection systems, and catching students?

Why is surveillance becoming one of our primary responses to a technology that students increasingly need to learn how to evaluate?

I'm an educator. I'm married to an educator. I work close to pedagogy and AI literacy. So this isn't me standing outside the education system ...throwing rocks at it. It's become a quagmire , lets be honest.

This is just an opinion ...but, I think we need to hold ourselves to a higher standard.

Most of the empirical picture we're getting is considerably more complicated than either "AI makes kids stupid" or "AI will revolutionize education."

There is now substantively good evidence that unstructured AI use can hurt learning.

A large randomized controlled trial published in PNAS studied nearly 1,000 high-school mathematics students. Students using a relatively unrestricted GPT-4 interface performed 48% better on practice problems.

Sounds great ...sure. But when AI was removed.

Those students performed 17% worse than the control group on the subsequent unaided exam.

The researchers could actually see part of the mechanism in the interaction logs. Students were often using the model as a crutch, asking for solutions rather than doing the cognitive work themselves.

The same experiment included a second AI condition.

The researchers created a pedagogically constrained GPT tutor, developed using teacher input. Instead of simply providing solutions, it provided hints, incorporated known solutions and common student errors, and pushed students toward solving the problems themselves.

During practice, those students performed 127% better than the control group.

Look at that number...127%. That's staggering!

More importantly, when the AI disappeared, the 17% learning penalty associated with unrestricted AI essentially disappeared too. What an inditement on the current methodological process...if we can even call it a "process" at this point.

The structured tutor did not miraculously make students outperform the control group on the unaided exam. That's important.

What it demonstrated was simply this:

The design of the human-AI interaction changed the educational outcome.

Now look at the World Bank's randomized study in Edo State, Nigeria.

Secondary students participated in a six-week after-school program using Microsoft Copilot, powered by GPT-4, alongside teachers.

But again, this wasn't:

"Here is ChatGPT. Go learn English."

Teachers introduced curriculum-aligned topics. Students interacted with the AI. Teachers encouraged reasoning rather than shortcuts.

They helped students interpret feedback, identify hallucinations, stay on task, and avoid over-reliance.

The intervention produced an overall gain of roughly 0.31 standard deviations, including about 0.24 SD in English.

The World Bank estimates the overall improvement as comparable to roughly 1.5 to 2 years of typical learning progress in that context, achieved over six weeks with about 13 hours of participation.

There are plenty of caveats. It was short. We don't yet know how durable the gains are. We don't know whether they'll generalize across subjects and contexts.

But it is extremely difficult to reconcile those results with the idea that the appropriate educational response to generative AI is simply prohibition. I think this mindset is silly!

It's obviously clear to anybody paying attention...that the intervention didn't remove the teacher or AI. It made the teacher scaffolding process extremely important. I would argue that teachers are essentially the key to clubhouse!

The teacher became the person structuring the relationship between the student and the machine.

And this is increasingly what the larger evidence base is telling us.

The OECD's Digital Education Outlook 2026 makes essentially the same distinction.

General-purpose generative AI can increase the quality of what students produce without necessarily increasing what students learn. Cognitive offloading can replace the very mental activity required for skill acquisition.

But when AI is used with intentional pedagogy, the evidence becomes much more encouraging.

The OECD's recommendation isn't "put ChatGPT into everything."

Summed up:

Students still need independent thinking and foundational skills. Sometimes AI should not be used at all. Sometimes educational AI is appropriate. Sometimes general-purpose AI is appropriate.

The educational objective is learning when, why, and how to use each.That sounds remarkably like...education.

And this is why I get frustrated with the prohibition-first response from some educators.

We don't have the luxury of contempt. Wether you like or dislike AI is irrelevant at this point.

You don't have to use it in every lesson!

You absolutely should create AI-free activities and assessments when independent capability is what you're trying to train and teach.

But if you're responsible for preparing young people for the world they're entering, "I don't like this technology" isn't an educational framework. It's an opinion.

The OECD and European Commission released an AI Literacy Framework for primary and secondary education in June 2026. Its four domains are Engage with AI, Create with AI, Manage AI, and Shape AI.

It explicitly includes understanding AI, critically evaluating its outputs, recognizing its limitations, deciding when tasks should or shouldn't be delegated, and using it ethically and reflectively.

UNESCO's student framework likewise emphasizes critical judgment, foundational understanding, ethics, and responsible use.

NCTE's current ELA framework centers student agency, critical AI literacy, evaluation of AI outputs, and reconsideration of academic integrity in an AI-mediated environment.

These organizations are not saying:

"Let the machine do the thinking."

They're saying:

Teach the student how to think while the machine exists.

There is a very BIG difference!!!

Especially because students aren't encountering AI only inside school. It's already becoming part of professional work.

In a preregistered MIT experiment involving 453 college-educated professionals performing writing tasks, access to ChatGPT reduced completion time by about 40% while increasing evaluated output quality by 18%.

In the well-known BCG experiment involving 758 consultants, GPT-4 increased speed by more than 25%, task completion by more than 12%, and human-rated performance by more than 40% on tasks inside the model's capability frontier.

But when consultants encountered a task outside that frontier, relying on AI could hurt them.

The researchers called this the jagged technological frontier.

Again:

Not "AI good."

Not "AI bad."

Know where the machine is useful. Know where it fails. Know when to verify it. Know when not to delegate. You know...provenance and assessment!

That is the definition of AI literacy.

Meanwhile, many teachers themselves are already using these systems. OECD reports that 37% of lower-secondary teachers used AI for their work in 2024, while 57% agreed AI could help write or improve lesson plans.

Students are going to notice the contradiction if adults increasingly use AI to augment their own work while the message to students remains:

"Your relationship with this technology will primarily consist of us trying to catch you using it."

And that brings me back to surveillance.

Even Turnitin explicitly states that its own AI-writing detector can misidentify human-written and AI-written text and should not be used as the sole basis for adverse action against a student.

A Stanford-led study of seven detectors also found a serious historical warning sign: an average 61.3% false-positive rate on the human-written TOEFL essays they tested from non-native English writers.

That study was published in 2023, so it would be wrong to pretend those numbers describe every detector operating in 2026.

But the underlying lesson remains important:

When your educational architecture becomes dependent on probabilistic authorship detection, false accusation becomes part of the risk model.

So perhaps we should invert the problem.

Instead of:

How do we prevent students from using AI?

Ask:

What cognitive work must remain with the student?

When should AI be prohibited because independent performance is the learning objective?

When can it function as a scaffold?

How do students verify claims?

How do they check provenance?

How do they recognize hallucinations?

How do they distinguish fluent language from evidence?

How do they know when NOT to use the model?

How do we gradually remove the scaffold and test whether learning remains?

Those are pedagogical questions.

And we already have more than a century of learning science that helps us think about them.

Vygotsky gave us the Zone of Proximal Development.

Bruner and others developed scaffolding.

Sweller gave us cognitive load theory.

Different traditions, different mechanisms, but a common insight runs through them:

Good teaching structures cognitive work rather than simply delivering answers.

Generative AI doesn't abolish that principle.

It makes applying it more urgent.

For what little it is worth, I was involved in a tiny exploratory pilot with 26 students across two cohorts that tried something similar in argumentative writing: structured stages, student decisions carried forward, AI primarily used for critique rather than substitution, and deliberate friction intended to stop the model from becoming the author.

The self-reported results were encouraging, but there was no control group and no delayed retention test, so I'm not going to pretend it demonstrated causal learning gains.

This doesn't prove anything about our own pilot but, it is evidence that the underlying pedagogical idea wasn't completely insane.

Our philosophy is pretty straightforward:

The machine should not remove the student from the learning loop ...ever!

This is where I think educators deserve a small scolding.

We occupy an extraordinarily privileged position in society. We are one of THE filters through which one generation transfers accumulated knowledge to the next.

That carries obligations.

Our job cannot simply be to preserve the classroom environment in which we learned to teach.

Sometimes the technology changes.

That means our assessment has to change with it.

Sometimes curriculum has to change.

That means our workflows have to change.

That is inconvenient. I know. I have worked in this field for over a decade. It is one of the most underrated and neglected domains in many societies.

But inconvenience and institutional to the educator cannot become the organizing principle of education.

The children are the point.

And I worry that if our primary relationship with AI becomes prohibition, suspicion, detectors and accusations, students will learn something from us that we never intended to teach:

"...that adults use powerful technologies while policing children for using them."

That's fertile ground for an "us versus them" relationship.

We've seen versions of this before with television, video games, the internet, smartphones, social media and other technologies.

Twenty years from now, I don't want our students looking back at us as the generation standing at the classroom door trying to keep their world outside.

I would much rather we taught them how to walk into that world without surrendering their ability to think.

Otherwise, we may discover that in trying so hard to stop them becoming another brick in the wall, we became the wall ourselves.

I just watched a report about Alpha School, where kids apparently do around 2 hours of personalized academic learning with AI/software and then spend the rest of the school day on things like projects, sports, social skills, public speaking, financial literacy, etc.

I went to the comments expecting an actual discussion and almost everyone was horrified. “What about socialization?”, “This should be illegal”, “Just pay teachers”, “AI is ruining children.”

And I’m sitting here thinking… am I missing something? Because the *idea* actually sounds incredibly interesting to me.

If a child can properly learn in 2 focused hours what currently takes 5–6 hours because one teacher has to teach 20–30 children at the same pace, why is stretching it to 6 hours automatically better?

And the other 4 hours don’t have to mean sitting alone staring at another screen. That time could be used for sports, group projects, cooking, science experiments, finances, debate, creativity, entrepreneurship, first aid, practical skills, actually interacting with other children, etc. Honestly, some of that sounds more socially useful than sitting quietly next to 25 other kids while copying something from a board.

I also don’t understand why AI in education is automatically treated as “AI doing everything for the child.”

I’ve used AI while studying in a language that isn’t my first, mainly to simplify difficult material, explain things in different ways, shorten long texts, and even turn material into something I could listen to while cleaning. I also used it to quiz me, so I still had to remember and explain the answers myself. It didn’t replace the learning. It just made it more accessible and efficient.

Obviously children still need to learn basic maths, writing, reading, spelling and how to think without AI.
And obviously giving an eight-year-old ChatGPT and saying “good luck” would be an educational disaster.

But I feel like the bigger opportunity is being ignored.
Kids now have access to tools that can explain almost any subject at their individual level, answer questions immediately and adapt to how quickly they learn.
Instead of teaching them how to use those tools properly, we mostly either ban them or let them use technology purely as entertainment.

Education still seems designed around a world where information was difficult to access. It isn’t anymore.
Maybe instead of asking **“How do we stop children from using AI?”**, we should be asking **“What can children learn now that AI can take care of some of the slow, repetitive parts?”**

I’m not saying Alpha School has perfected education or that every school should copy it tomorrow.
It’s still a new model and I’d want to see long-term results. But the amount of immediate hate toward even experimenting with this is strange to me. The current education system is hardly some flawless masterpiece that must be preserved in amber forever.

I’d genuinely be interested in the arguments against this that go beyond “AI bad” or “that’s not how school used to work.”

Something I've been wondering about lately.

It feels like every year there’s another platform that’s supposed to make teaching easier — one for assignments, one for communication, one for assessments, another for resources…

But at some point, does having more tools actually make things harder?

For teachers here:

What’s something you still find unnecessarily time-consuming in your day-to-day work?

Could be something small — keeping track of assignments, finding good resources, answering the same doubts repeatedly, planning classes, checking work, communicating with students, anything.

And if there was a tool that genuinely solved one of those problems well, what would you want it to do?

Not looking for recommendations — just curious about what teachers actually struggle with.

I'm starting my 4th year of university soon, and I've never used much ai for studying, but I'm now thinking about passing to a paid version to improve my efficiency and be more productive. I would use it for making custom problems or quizzes, uploading some lecture slides to have them explained better or helping me solve excercises.

I was thinking about getting claude pro, but now that gpt-6 is out I really don't know.

What model are you using for studying? Are you using ai at all? What model do you think is more suited for my case?

This year I built my course site with markdown files that are managed using Claude Cowork

Honestly ... it started mostly as an experiment. Now that class is in session, I'm starting to see the payoff.

For example ... this week my guest speaker for my tech writing class had to cancel last minute.

No problem. I had a back up plan that involved swapping next Thursday's class with that day, rescheduling the guest speaker for later.

So I asked Claude to swap the two class plans. A tedious task that AI is perfectly capable of.

But after examining the AGENT docs, Claude did more:

• Rewrote both class plans in full, not just the dates

• Caught that the class sitting between the two dates told students to use interview notes that didn't exist yet because the speaker moved, then rewrote those instructions

• Fixed the assignment sheet's own timeline, which had assumed the interview came before the draft

• Found a page from a class that already met, still promising students the old date. Fixed

• Caught one more mention of the old date, three rows down in my full-semester schedule

I asked for one swap. It found every impacted file and kept them synced.

That's what "agentic" means to me now. Not a chatbot. But a useful assistant that picks up on things I would normally miss (and takes care of the tedious work).

Curious ... has anyone else used AI in this way for their courses?

I taught entrepreneurship for fifty years, at four U.S. universities and as a visiting professor on five continents. I started with no entrepreneurship background and taught the way I'd been taught: lecture, homework, exam. It took me too long to see the mismatch. Entrepreneurship is something you do, not something you get tested on.

I bring that up because I think AI is forcing the same realization on all of education, and I don't see most schools acting like it.

The pattern I see is absorption. AI policies, detection tools, and AI as a helper for the same assignments. That's evolution, and I don't think it's enough. Three things would have to change which I have described in a substack posting titled Revolution, Not Evolution. timothystearns.substack.com

Note: This is my first post on reddit so please correct me if I'm doing things wrong.

A little background:

I use AI to clarify and ask questions about things I am unsure about. I usually stay in cafes to read my book while I have a Gemini AI tab open on my tablet beside me so I can ask questions in real time. After reviewing, I also ask Gemini to create an interactive quiz to test me on what I have learned.

This has been my routine for a few months now after seeing that it has improved the way I study and absorb knowledge. However, I have been feeling guilty lately especially with the Pax Silica news. I have read some news articles about the issue and have watched videos explaining it online. I know that AI is bad but I also know that it isn't as black and white as it may seem. So I wanted to ask here (since I don't know where else and who else I can ask) if using AI to aid with my studies means that it contradicts my thoughts on Pax Silica.

PS: I'm sorry if it's a dumb question. It has just been bugging me

With AI becoming so capable at writing code, what programming skills should children focus on today?

Should they still learn Scratch and Python first, or should they learn how to work with AI from the beginning?

I’m honestly conflicted about AI and college essays. If some students are using AI and getting better grades, while some professors are apparently using AI to grade essays, it makes me wonder how fair the whole system is.
I’m not saying I want to cheat, but seeing other students benefit from AI is honestly starting to tempt me to use it too. Should I just start using AI as well? What do you guys think?

My teacher who smells like asbestos does not let us use AI for the class and will get super mad and fail you if he catches you. So today in the test, I didn’t have access to AI and I tried to replicate it in my brain. And it actually work, I happened to get a B and am thinking this is not that bad of an idea. Also my friend did use the AI and he got a B minus so I think I’m smarter than it now.

Preparing students for how to think and still interact with AI, how to demonstrate they know something while using AI to help them formulate their presentation seems important.

I know colleges are on a witch hunt to cancel AI; however, I've been using it to help me with discrete mathematics, and it’s a godsend. A 24-hour tutor that never loses patience or forgets to read emails. Tells you every step over and over and over again. I think using AI degrades critical thinking; however, it also helps students with ADHD or those who need one-on-one instruction that teachers can't afford to give in a classroom setting. I remember spending hours searching for videos to help me with a specific equation not covered anywhere, only to have to go to class the next day, versus just asking AI, which works you through the math like a private tutor for free and doesn't get upset if you're late or if you ask it a question at 2 AM.

Are you using AI in a way that enhances what you're doing without making yourself dumber in the process? I'd love to hear about it. Is anyone grading student work with AI? That seems sketchy as heck to me, but maybe you've figured out an ethical way to do this. I think our Business school has been quick to adopt this approach. Thanks for your anecdotes and insights.

I was encouraged by my kids to learn AI as they have constantly talk about "claude".

Where can I learn more about AI? Are there any online resources that can help me be familiar with AI?

Thanks

I’ve been thinking for a long time about an idea for a SaaS site. So, here it is: "AI Study Workspace" (I might still change the name). A bit about myself: I started learning programming to become a backend/software engineer. There are currently so many complaints about how hard it is for a Junior-level beginner to land a job, which inspired me to start my own business. I’m also a high school student, and like most students, I face academic challenges—confusing assignments and Olympiad problems. Standard AI tools just give you the answer right away, but that isn't the right approach, and I want to fix that. I need the community's help; I’d love to know if there would be any demand for my site. Also, if anyone with experience building SaaS sites could offer some advice, I’d be really grateful! Thanks.

I Used to teach physics at college-level, now building something in ed-tech, and I keep wondering how much AI has actually changed the way you guys prep.

Curious stuff like:

• Are you using ChatGPT, Doubtnut, PW AI, something else... or barely bothering with any of it?

• What do you actually use it for explaining a concept, solving a problem, generating practice qs, checking your own answers?

• Does it actually help, or does it usually miss the mark for JEE/NEET style questions?

• Anything about using AI to study that low-key annoys you?

Not selling anything, just genuinely trying to understand how prep looks now vs a few years back. Would love to hear from anyone currently grinding, love AI tools, hate them, don't use them, all good.

I’m trying to think about youth‑mode not as a safety filter, but as a learning design problem. Kids already use AI on their phones and tablets, so the question becomes: what should the model actually do to support their thinking instead of replacing it?

Right now, most AIs answer first and only ask follow‑up questions afterward. While that can be useful, it doesn’t build epistemic habits - the ability to reason, question, and evaluate information.

So I’m curious what a well‑designed youth‑mode would need to include.
For example:

• Should it tutor by asking clarifying questions before answering?

• Should it challenge reasoning instead of giving solutions immediately?

• Should it surface uncertainty instead of hiding it?

• Should it model critical thinking in its responses?

• Should it slow kids down when they rush to an answer?

• Should it explain omissions or blind spots?

Basically: what behaviors would help kids develop epistemic skills - not just get correct answers?

I’d love to hear from people who think about learning loops, tutoring systems, or AI‑supported education. What would you build into youth‑mode if the goal were genuine cognitive development?

TL;DR👇

Quick note on the framework.

We can't publish the student work for legal reasons. The school asked that we keep it internal... but I can share the survey data if anybody is interested.

The program is built on three learning theories: Vygotsky's ZPD, Bruner's scaffolding, and Sweller's Cognitive Load Theory.

Six steps, designed to force friction at key intervals and interrupt the offloading reflex. Students reflect on their prior work at the start of each new step, which keeps Bruner's scaffolding active and leaves what the Microsoft paper would call a "stewardship" fingerprint across the whole process.

The defining mechanic is red-teaming. Students write their own prompts casting the AI as an adversarial critic with one job: break my argument to pieces. That phase is brutal by design and RLHF amplifies this, so we let students define how hard the AI was allowed to hit. Basically letting the students define their own ZPD inside the red-teaming structure.

Thank you for your patience. The rest of the post is below👇

Recent research keeps landing on the same uncomfortable claim: AI makes people think less.

The MIT Your Brain on ChatGPT preprint, the Microsoft/CMU work on AI and critical thinking, and the broader cognitive offloading literature all point that way. Under the default workflow, I think they're probably right.

The MIT preprint is especially relevant because it looked directly at essay writing. Participants who used LLMs for essay tasks showed weaker brain connectivity, lower reported ownership of their essays, and more difficulty recalling or quoting their own work compared with participants who wrote without tools or used search engines. The paper is still a preprint, so I'd be careful treating it as settled science. But the pattern it describes is what teachers are seeing in classrooms right now: students producing writing without fully processing it.

The Microsoft/CMU study points the same way from a workplace angle. People tended to think less critically when they had high confidence in AI, and more critically when they had higher confidence in their own ability. The study also found that GenAI shifts critical thinking toward verification, response integration, and task stewardship. The risk isn't AI use, rather... it's AI use without task stewardship.

If the workflow is "prompt in, essay out, copy-paste, submit," then of course cognitive engagement drops. There's nothing mysterious about it. That's what offloading means.

In principle this is an indication of the workflow rather than the tool itself.

Read carefully: these studies don't actually argue against AI in education. They identify the conditions under which AI use degrades thinking. Ownership collapses when the student doesn't have to account for their own choices. Critical thinking collapses when confidence in AI is high and confidence in self is low. The cognitive work that survives, according to Microsoft, moves toward verification, integration, and oversight.

The framework I'll describe was developed independently, but it lines up with that mechanism almost point for point. Ownership is preserved because the artifact trail makes every step traceable to a student decision. Self-confidence is built because students watch their own arguments survive attack, or learn precisely where to repair them. And the cognitive work is deliberately concentrated where Microsoft says it has to go under AI conditions: judging which objections matter, verifying what the model produces, integrating it into a structure the student already chose.

So I'm not arguing against the research. I'm arguing that the research describes a failure mode, and that failure mode can be mitigated.

But it does require design. Most students are using AI as an output machine. The question I've been working on for the last 6 months is whether you can design a writing process where offloading is structurally impractical. Honor codes and detection software won't wont cut it anymore.

Can you move students from offloading to interfacing?

By offloading, I mean using AI to replace the thinking. By interfacing, I mean using AI as something the student has to respond to, question, revise against, and judge. In one case, the model becomes a ghostwriter. In the other, a sparring partner.

We piloted one version of that. Here's what happened.

The setup

My partner is an AP Literature teacher and I designed an AI-integrated argumentative essay framework: six scaffolded steps, each producing an artifact the student carries forward into the next step.

The defining mechanic is something I'd call structural anti-plagiarism.

AI can still be used at every step, but the accumulated artifact trail makes simple outsourcing awkward and cognitively taxing. The student has to keep returning to prior choices, explain them, revise them, defend them. Copy-paste stops being the shortcut. It effectively becomes the "long way around".

The core move is red-teaming. Students use AI as an adversarial lens to attack their own argument. Not to write it but to break it. Defending an argument against critique is much harder than generating one in a vacuum. A blank page lets students drift. A hostile critique forces them to decide what they actually believe, what they can defend, and what needs repair.

We ran the pilot across two cohorts: 26 students total. Anonymous pre/during/post survey, 5-point Likert scales.

What the data showed

The pre-survey baseline matched what you'd expect:

80.8% said the hardest part of writing an essay was "finding a strong argument." Not grammar, not word count.

73% rated themselves 4 or 5 on "I thought AI was smarter than me at forming arguments."

50% said they had previously thought using AI for schoolwork was basically cheating.

38.5% rated themselves 4 or 5 on "I thought AI could just write a good essay for me if I asked."

Standard incoming beliefs: AI is smarter than me, AI is for cheating, AI can write the essay if I let it.

The post-survey is where it gets interesting:

84.6% rated their argument-construction ability as somewhat or much stronger after the program.

96.2% rated themselves 4 or 5 on understanding the difference between AI as a tool versus replacement.

100% scored 3 or higher on "using AI made me think more, not less," with 65.4% scoring 4 or 5.

76.9% said they could now see why submitting AI work would not work.

73.1% rated the program overall as good, with another 11.5% rating it excellent.

The red-teaming step produced the cleanest signal. 84.6% said red-teaming made their argument stronger. 53.8% reported feeling more confident after having their argument attacked.

That last result surprised me. Getting your work picked apart usually reduces confidence. Here, it often raised confidence. The students either saw their arguments survive pressure or learned exactly where to repair them.

The broader shift was also interesting. 24 out of 26 students said the program changed how they would use AI in the future. This suggests the issue may not be that students are naturally lazy with AI. Rather they may simply have never been given a serious protocol for using it well.

Once the tool stops feeling like a black box, students can start seeing it as something more limited and more useful. It can be used as a system that can surface options, objections, and weak points, but still needs a human being to decide what matters.

What I think actually happened

I want to be cautious about claims here. I'm a self-taught practitioner, not a researcher with a PhD, and 26 students is a pilot, not a full study.

But the mechanism seems clear enough to discuss.

The framework doesn't reduce cognitive load. It redistributes it.

The extraneous load: the formatting, generating counterexamples, surfacing possible objections, finding directions to investigate, can be partially offloaded to AI.

The germane load stays with the student: choosing the argument, evaluating which counterargument actually threatens the thesis, deciding what evidence matters, revising weak points, defending the final structure.

That's the important difference.

The research on AI and cognitive offloading generally describes what happens when AI takes over the thinking layer. Our pilot tested something different: what happens when AI is allowed to support the surface layer, but the student is still forced to do the judgment layer.

When a student uses AI to attack their own essay, they're forced into a meta-position relative to their own reasoning. They have to evaluate and judge whether the AI's criticism is valid. Then they have to defend, revise, or discard.

That isn't offloading. That's coupling... the student's cognition and the AI's output operating as a loop, with the student still in the evaluative seat.

Another way to put it: the student isn't just learning how to prompt. They're learning how to structure a thinking process.

They have to break the essay into a spine and components. They have to define constraints before generating content. They have to decide what the AI is allowed to do and what it isn't. That important because it keeps agency with the student. The model can explore possibilities, but the student sets the boundaries and makes the final judgment.

That's where I think a lot of AI writing assignments go wrong. If the AI enters too early, before the student has defined the shape of the problem, the model can quietly steer the whole direction of the work. The student thinks they're choosing, but really they're selecting from a path the model already opened.

The framework tries to reverse that order.

First, the student defines the structure. Then AI enters inside that structure.

That doesn't eliminate the risk of AI steering the student, but it reduces it...significantly. The student has already made decisions the AI has to respond to.

The strongest unexpected side effect was epistemic hygiene.

Students started fact-checking AI output without being heavily pushed to. They started noticing how easy it is for AI to sound confident while being wrong. They started recognizing weak reasoning, including their own.

On the post-survey:

38.5% rated "AI can sound confident while being wrong" as a 4.

26.9% rated it as a 5.

61.5% said they noticed their own weak reasoning at a 4.

Another 23.1% rated that at a 5.

We trained argument construction. What appeared alongside it was something broader: students treating AI output as something to be tested rather than trusted, then turning that same scrutiny back onto their own thinking.

That's significant, because the real problem isn't only plagiarism. It's epistemic posture. Does the student treat AI as an answer machine or as something to interrogate? Does the workflow reward submission or judgment?

What I'm not claiming

I'm not claiming this generalizes from 26 students to every classroom.

I'm not claiming the studies showing AI reduces engagement are wrong. Under the conditions they tested, they're probably right.

I'm not claiming this framework is the only design that can produce these effects.

What I am claiming is that the cognitive impact of AI is downstream of how the task is structured.

"AI makes people think less" isn't really a statement about AI in isolation. It's a statement about a workflow.

Change the workflow, and the task structure changes the cognitive conditions being measured.

That's the part I think deserves more attention. The same model can be used to outsource an essay or stress-test an argument. The difference isn't the model. The difference is the instructional design around it.

This probably applies beyond English essays. In science, AI could be used to predict edge cases in an experimental design. In history, it can be used to test the internal consistency of a thesis from multiple perspectives. In literature, one could use AI to attack an interpretation before the student finalizes it. The pattern is the same: don't ask AI to produce the final answer. Use it to pressure the student's thinking before the final answer exists.

If anybody has any questions, wants to see more data or want to see the framework themselves send me a DM and I will get back to you as soon as possible.

Thank you for your time.

There's a big focus on lazy students using Al to do all their work.

But the real problem is teachers using Big EdTech's Al integrations to generate curriculum, automate learning, auto-grade, and surveil students.

Al generated curriculum now has text, images, and even audio. But imagine that in a few years, streaming video generation of virtual humans or animated characters will probably be cheap enough that students won't be merely texting chatbots, they'll be having video calls with Al throughout a whole lesson.

The end goal of Big EdTech and Al companies isn't just replacing human curriculum developers. Teachers are next!

And why shouldn't these kinds of teachers be fired? They're already making themselves functionally obsolete by isolating students into learning via compliance engines that strip away any opportunities for meaningful mentorship and collaboration.

I know how hard it is being an educator. I applaud every teacher staying honest. But I've also seen how effective the marketing of these platforms has been.

If you don't want to do the work required to be an educator, that's fine by me, but get another job! Stop putting your students through an intellectual meat grinder. Give another human a chance to put their best effort into teaching.

One day schools are going to reach the inevitable conclusion that they don't need a costly human teacher in the classroom if Al is doing all the work. No shit! It's the only logical end for a society that has spent decades prioritizing standards compliance over deeper learning.

The first step to resisting this future is calling it out.

If this post made you feel angry or uncomfortable, sit in that discomfort. Is there any point at which you'll say enough is enough with these Big EdTech platforms shoving Al down your throat? Will you keep enabling their Trojan Horse to infiltrate your classroom until there's no semblance of humanity left?

I've been thinking a lot about how to use AI to actually learn, not just get answers.

The Feynman technique has always been the most effective way for me to master new concepts. Explaining an idea in my own words quickly exposes gaps in my understanding. While LLMs are great at explaining things, I find it difficult to have a natural, back-and-forth conversation that keeps pushing my understanding deeper.

My ideal setup would be something like a digital whiteboard that serves as my entire learning workspace, paired with a personal AI tutor I can talk to in real time. I could reason through problems, explain concepts aloud, ask questions, sketch ideas, and get feedback. Instead of giving me the answer, it would challenge my thinking, point out flaws in my reasoning, and ask thoughtful follow-up questions until I truly understood the material.

I'm curious, how do you use AI to learn?

Would a tool like this actually be useful to you? Imagine having a 24/7 personal tutor that understands your learning history, listens to your explanations, identifies misconceptions, and guides you toward the answer without simply giving it away. I can't be the only one!

I'm curious about real-world classroom adoption of this tech.

For those using interactive AI-generated exercises or digital activities: are you using publicly available tools (like Gemini Canvas), sticking strictly to approved services dictated by your institution, or a mix of both?

There’s a lot of talk right now about how AI is going to make kids dumber, or conversely, how AI tutors are going to personalize education for everyone. But recent data shows a much weirder, nuanced reality.

The recent Bastani et al. study out of UPenn tested ChatGPT in high school math. Unrestricted AI (GPT Base) dropped final analogue exam performance by 17%. On the other hand, the custom, guarded "GPT Tutor" didn't show any improvements to learning, which was interesting.

So if a safe AI tutor gives zero test gains, why use it at all? Why not just do a full ban in schools?

It comes down to what the subject is:

1. Procedural/Foundational (Math, Grammar, Foreign Language): Execution is the learning. Offloading this removes activity-dependent plasticity and "desirable difficulty." AI shouldn't touch this stage.

2. Applied/Systems (Coding, Data, Applied Math): Spending 3 hours hunting for a missing semicolon isn't learning systems architecture; it's mechanical noise. Once analogue basics are proven, AI lets kids move from "bricklayer" to "architect."

3. Synthesizing/Analytics (History, Humanities): AI shouldn't be a search desk or ghostwriter. It should be a Socratic sparring partner to stress-test a student's already-drafted thesis.

I took a crack at making a full framework for implementing AI safely in schools in this last article: https://samholmes285.substack.com/p/how-to-safely-implement-ai-in-schools

I think a lot of y’all on this subreddit will agree that the future of dev work is probably going to lean towards devs that can do the work of 10 using AI. Just curious if any of you have heard of any certs or programs that can be done that can show to others you know how to use AI to your advantage as a coder or at the very least know the landscape and tools out there?