What it costs not to surrender: MIT's report on AI and education, read on another budget

Sun, 20 Sep 2026 00:00:00 +0000

On: Report — AI and Education, Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, MIT, 13 August 2026. .

Updated 29 September: at the end.

In mid-September, a university report from August became international news thanks to two words: cognitive surrender. The phrase travelled from CNN to newspapers in India and Malaysia within forty-eight hours, in the shape these things usually take once they leave the document that produced them —AI is making students stupid, universities do not know what to do— and that shape is poorer than the report. It is worth reading in full, because its argument is not about anybody’s intelligence. It is about what it takes for learning to remain possible, and that question, asked at MIT, has an answer that here has to be paid in another currency.

What the report says

MIT’s ad hoc committee on AI use in teaching, learning and research training, co-chaired by Eric Klopfer and Sam Madden, starts from an observation it does not dispute: generative AI already produces credible responses to much of its own institution’s undergraduate written work, including essays, mathematics and science problems, proofs and programming assignments. From that it does not draw the conclusion one would expect. It does not propose a return to pre-AI assessment, which it considers unviable, and it does not propose detection either. It proposes redesign.

The conceptual piece is the distinction between getting the answer and learning. Getting the right answer from a chatbot, the report says, creates the illusion of learning, and can trigger cognitive surrender: the student falls back on AI at the first hint of difficulty. What is lost in that transaction is not the answer —the answer is there, and it is usually right— but the struggle, that uncomfortable stretch in which effort turns into durable knowledge and, above all, into the judgement needed to assess somebody else’s answer. The report registers the effect as a pattern: AI improves performance on take-home work and worsens it on the proctored exam.

The second piece is the one the press did not pick up, and it is the more interesting one. The report documents changes in campus life that are not academic-integrity problems: less attendance at office hours, fewer in-person study groups, less participation in online discussions. At that point the report stops talking about individual conduct and starts talking about something else. Office hours, study groups, mentoring, peer critique and participation in research are not complementary experiences of university life: they are how professional judgement is learned, and they are the first thing to empty out when every student can solve their problem alone at three in the morning. The object of the report, put in a vocabulary the report does not use, is the social organization of learning.

The recommendations follow from that, and they are worth enumerating because the enumeration is this text’s argument: oral exams, semester portfolios, assignments paired with in-class conversation, documented work histories, project milestones, experiential learning, structured social learning within each course, mentoring, and the preservation of undergraduate research positions. Plus two policy items: that each course state explicitly when AI is prohibited, permitted or required, and why; and that instructors be transparent with their students about their own use of AI to prepare materials, give feedback and grade.

Two of those positions deserve to be granted in full. The first concerns detectors: the report warns that they are unreliable in ambiguous cases, that they produce false positives for students who are not native English speakers and for neurodivergent students, and that they install a policing culture. It is a warning with a geography, even if the report does not name it: in any university in the world, the student writing in a language that is not their own comes mostly from a poorer place than the campus where they study, so the cost of the false positive is not distributed at random. The second is instructor transparency, which is the part of the report that will prove most uncomfortable inside institutions, because it blocks the arrangement any hard-pressed faculty would be tempted to make: restricting AI for the student while quietly automating the work of teaching.

The problem with the list

Read from a dean’s office at MIT, that list is a work plan. Read from a Latin American public university, it is an invoice.

Consider what the oral exam, the portfolio with feedback, the in-class conversation about submitted work, the tracking of project milestones, mentoring and the undergraduate research position have in common. All of them are forms of assessment and teaching whose cost grows linearly with the number of students, because all of them consume instructor time per student and not per course. The multiple-choice exam, the standardized midterm and the rubric-graded assignment exist precisely because they decouple cost from volume: they are the technologies that made mass higher education possible. MIT’s report proposes, without putting it that way, coupling them again. And it is right, because the struggle it wants to protect only becomes visible in formats where somebody watches a student do something. But that means the correct answer to cognitive surrender is not a methodology: it is a student-teacher ratio, and a student-teacher ratio is a budget line.¹

The report does not hide this. It recommends funding pilot projects, training instructors, creating dedicated institutional roles and sustaining a standing committee. That is, it knows that what it proposes costs money, and it describes how to pay for it in an institution that can. The problem appears when the document travels, because it travels without the footnote stating the price, and it arrives in systems where the question of cost is not an implementation detail but the entire question.

Eighty-seven and eight

For that journey there is, this time, a number on the other side. On 9 September, at Digital Learning Week in Paris, UNESCO IESALC presented La implementación de la IA en la educación superior en América Latina y el Caribe, a study by Arianna Valentini covering 200 institutions in 19 countries, with fieldwork between August and October 2025. The main figures are five and read better together: 87% of institutions already use artificial intelligence in some of their activities; 26% have a formal strategy; 18.5% have comprehensive institutional policies; 9% have formal evaluation mechanisms; 8% have a dedicated budget for the subject. On the individual side, 92% of students and 79% of faculty in the region already use AI. Adoption is higher in teaching (73.5%) than in research (57%), administration (34.1%) and outreach (20%), and the impulse comes from instructors, researchers and students: not from institutional decisions.

That last datum is what links the study to another from the same week. The survey UNESCO and ICOM published on 17 September, covering more than 400 museums in 90 countries, found the same structure with different percentages: 57% use AI, 55% have no written policy, and adoption is exploratory and staff-led. Universities and museums across three continents are doing the same thing. They adopt on individual initiative and govern afterwards, if they get to it.

Placed next to MIT’s report, the picture arranges itself like this. At MIT, the risk is that AI erodes a set of labour-intensive practices the institution still has and can pay for. Here, 87% have already adopted and 8% have the means to think about what they adopted, so the risk is not the erosion of those practices but something prior: that it is never discussed whether they should exist at all. MIT’s report warns of a loss. In this region, much of what would be lost has been running for years on the duct tape of instructors’ goodwill.

The cause that arrives second

There is one recommendation in the report that sounds preventive in the North and describes a settled fact here. The committee asks that undergraduate research positions (MIT calls them UROP) be preserved, and warns that replacing the novice researcher with AI agents would eliminate not merely some tasks but the entry point into a research community and a professional identity. It is exactly the argument this blog discussed two weeks ago , with a difference in timing that changes everything: here that door has been closing since 2023, and artificial intelligence did not close it. It was closed by the fellowships that were not renewed and the career-entry positions that were never opened.

The consequence of that difference is one of attribution, and it is not minor. When a technology arrives to occupy a hole that was already dug, the institutional account that gets written is that the technology dug the hole. If five years from now a university in this region finds that its research groups are not taking on young people, it will have a prestigious, international, MIT-published explanation ready to hand, and it will have to make a deliberate effort to remember that austerity arrived first. That matters because the two causes are corrected by different policies: against substitution by agents, a rule about which tasks are not automated works; against budgetary hollowing-out, no rule about AI works at all. Confusing them produces institutions that write impeccable protocols about a system that has run out of people.

And in the opposite direction, the same confusion enables the move worth anticipating now, while it still has no proper name. A university system without money, with rising enrolment and a student-teacher ratio that worsens every year, is the ideal customer for automated personalized tutoring, assisted grading and generated feedback. Each of those purchases is justified with the correct argument —the student who today gets no feedback at all will get some— and the correct argument is, moreover, true. The question is not whether that improves the starting situation. It is what gets locked in when the improvement is installed: if automated feedback becomes the system’s floor, the discussion about how many instructors are needed turns, permanently, into a discussion about licences.

The cheapest thing on the list

One way out remains that does not depend on the budget, and it is the part of MIT’s report that was least quoted. Among all the expensive recommendations there is a free one: that each course declare explicitly when AI is prohibited, when it is permitted and when it is required, and explain why, in terms of what the student is supposed to learn there. It requires no new positions, no licences, no platforms, no change to the assessment regime. It requires a paragraph in the syllabus and a departmental conversation before the term starts.

The 18.5% figure says that not even that has been done. This is the point where the diagnosis of budgetary constraint stops working as an explanation, because writing that rule does not cost money: it costs taking a position, which is more uncomfortable. As long as it is not written, the student who wants to do the right thing has no way of knowing what is expected, the instructor who wants to hold a line has nothing to back it with, and the only effective institutional policy ends up being silence, which in practice authorizes everything and protects no one.²

What remains open

MIT’s report does something almost nobody in this debate does, and that is why it is worth rereading: it treats the classroom as a site of AI governance and not as a place where decisions taken elsewhere get applied. That is the exportable part, and it is the part that survives the difference in budget. Cognitive surrender is not fought with surveillance software or with vendor-managed automation; it is fought with pedagogical labour, shared study spaces, open and auditable infrastructure, and forms of assessment that make the process visible and not only the product.

What is not exportable is the price list. And the question left open is which of the two will travel faster. A report downloads for free; an oral exam for a section of two hundred students does not. If universities in this region adopt the diagnosis without the investment, the predictable result is not a system that protects productive struggle: it is a system that knows exactly what it is losing and has no means to prevent it, which, in education policy, is usually worse than never having found out. It is worth having the diagnosis arrive anyway. But it should arrive accompanied by the question the report has no obligation to ask and that here cannot be dodged: who pays for the hours a student needs in order to have someone to struggle with.³

Postscript, 29 September

Two texts from the last few weeks touch the question this post left open and answer it in opposite ways, so it is worth setting them beside the report.

The first is by Brian Klaas, and published it on the 26th under the title “La gran brecha cognitiva” (the , “The Great Cognitive Divide”, dates from June). His thesis is that AI works like a cognitive gym: it amplifies those who already have habits of critical thinking and leaves behind those who hand the effort over to it. He relies on the two studies almost everyone writing on this cites. One, from MIT in 2025 and with a small sample, recorded reduced brain connectivity in people who wrote an essay with ChatGPT, along with difficulty remembering what they had just written. Another, from 2026, found that people who learn with AI assistance persist less and give up sooner. The final recommendation is individual: before each use, ask whether the tool is complementing your thinking or substituting for it.

The description is right and the conclusion falls short, precisely where the MIT report is better. If AI amplifies what the student already brings, the divide is produced not by each person’s disposition but by what each person brings, and that was produced by an institution: someone who marked, who gave feedback, who asked an awkward question in office hours. Klaas’s question can only be asked by someone who has already learned to think without the tool. Turning it into policy shifts onto the student the task the report assigns to the university. And in a system where 8% of institutions have a budget for the matter, it is exactly the policy that will be adopted, because it is the only free one. Klaas notes in passing that there is no Silicon Valley in Madagascar. In most of the region’s universities there is also nobody with the time to teach people how to use the gym.

The second is ’s piece on the mathematicians who hate AI and cannot quit it, from which I quote two lines as reproduced elsewhere, since the text is behind a paywall. Jared Speck, of Vanderbilt, recalls his training: “When I was starting out, I was given problems that people senior to me probably could have solved more easily themselves, or at least done more quickly. But they were investing in me.” It is the report’s argument about undergraduate research positions, voiced by someone who benefited from one, and it is Tao’s told from the other side. The second line is Noga Alon’s, of Princeton, about the problems AI has begun to solve: once it does, “there is no point anymore”. Both lines describe the same thing. A training problem stops making sense as a problem when the machine solves it, and that is exactly why it made sense to give it to someone: whoever assigned it knew the answer existed, and what they were buying was the person who would find it. That purchase is the budget line this post is about, and no brain-connectivity study replaces it.

It is worth not romanticizing the oral format, because it has problems of its own and not all of them are about cost. Oral assessment rewards verbal fluency and penalizes shyness, stammering and accent, and it is more permeable to assessor bias than anonymous grading; in systems with mass enrolment, moreover, a hurried implementation tends to degenerate into a three-minute interrogation that assesses nerves rather than understanding. None of this rules it out: it says the format requires written criteria, training and time — that is, exactly the same resources everything else on the list requires. The advantage of the oral exam is not that it is AI-proof —no assessment is— but that it makes the process visible, and that advantage is lost if it is adopted only as a defence. ↩︎
A clarification about what kind of rule works. The one that works is not “AI is forbidden”, which is unverifiable and which MIT’s own report advises against, but the one that declares which capacity is being assessed in each instance. “This assignment assesses whether you can derive the result, so you do the derivation yourself and you may use AI to check the wording” is a rule a student can follow in good faith and an instructor can hold without policing anyone, because whoever breaks it is left without the capacity the course was going to give them. Put differently: the useful rule does not protect the integrity of the submitted work, it protects the reason it was assigned. ↩︎
The reflexive objection that applies. This text is written with the same tools whose pedagogical effect it discusses, and in a blog that sits no exams. The difference between using AI to write this and using it to solve an undergraduate problem set is not in the tool but in where the struggle is: here the argument with the report is carried by prior reading, which took years and was paid for by a public system, and that is precisely the part a student who surrenders at the first difficulty will never accumulate. Which, if you think about it for two seconds, is not a defence of this text: it is the reason why in fifteen years there will be nobody left to write its equivalent. ↩︎

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