Education | guIA

Mollick in the classroom: a critical and agential reading

Wed, 26 Aug 2026 00:00:00 +0000

The practical map: from prohibition to engagement

Ethan Mollick —a professor at Wharton, author of Co-Intelligence— is by now the obligatory reference for anyone thinking about generative AI in the classroom from a pragmatic stance: neither panic nor blind enthusiasm, but deliberate experimentation. His starting point is simple and, at the same time, uncomfortable: AI can already do most traditional schoolwork better than the average student, and “AI-generated text” detectors don’t work reliably. Banning it isn’t a policy, it’s a fiction that offloads onto the student the responsibility for a structural problem.

From there, a handful of ideas worth keeping close at hand:

The seven roles of AI in the classroom (with Lilach Mollick): tutor, coach, mentor, teammate, tool, simulator, “student” (the student teaches the AI in order to check their own understanding). Each role carries a specific pedagogical benefit and a specific risk —AI as tutor, for instance, can contradict itself or offer an even but wrong knowledge base. The contribution isn’t “use AI or don’t,” but naming in what capacity it’s being invited into the task.
Redesigning assessment: more in-class, low-stakes instances, a focus on process over final product (drafts, notes, oral defenses), tasks anchored in what’s local and discussed in class —what a model can’t easily replicate— and mandatory transparency about what was used and for what.
The flipped classroom: AI as a patient tutor available outside class hours, and class time reserved for what genuinely requires being in the same room —discussion, collaborative work, on-the-spot correction.
Durable skills: instead of teaching prompt engineering (which ages fast), cultivate taste, a personal voice, domain knowledge to audit what AI returns, and agency: the question that matters isn’t what AI is going to do to education, but what we choose to do with it.

That last point —agency— is the hinge into what follows.

A critical and agential reading

Mollick’s framework is the most useful one available today for the actual classroom, and that’s reason enough to adopt it. But adopting it without further thought also risks staying at the level of the classroom, as if the question closed there. It’s worth stretching it in two directions.

From individual agency to institutional capacity. Mollick uses “agency” in a basically individual sense: the teacher or student who decides, task by task, how to invite AI in. That’s a necessary starting point, but an insufficient one if left isolated —it risks the same move as explaining a structural problem by appeal only to one person’s decision (the “micro-to-macro fallacy”). Read through Sen and Nussbaum, what ought to be asked of every use of AI in the classroom isn’t only “does this improve the grade or save time?” but what real freedoms does it expand or contract? An AI tutor available 24/7 can expand the capacity to learn of someone with no access to pedagogical support outside class —or, if the model costs $20 a month and the institution doesn’t subsidize it, it can become one more advantage for whoever already had one. Mollick’s agency is necessary but needs completing with this institutional question; it can’t remain a purely individual virtue.

The Global South perspective, which Mollick’s framework doesn’t address. “$20 a month” or the advice to “use the frontier model for ten hours” are trivial gestures for someone writing from Philadelphia, and much less trivial for a public school in a low-income neighborhood or a rural region of Latin America. Three concrete tensions:

Data coloniality and infrastructural dependency. The AI tutors being installed in Global South classrooms are, overwhelmingly, products of a handful of Northern companies, trained mostly in English and on corpora that don’t reflect the contexts, examples, or varieties of Spanish or Portuguese spoken here. Adopting the “seven roles” framework without asking who designed the tutor, on what data, and at what cost (energy, money, the privacy of the students themselves) is repeating, in miniature, the same pattern of extraction and dependency that runs through AI more broadly.
Epistemic commons and their enclosure. The soundest institutional answer isn’t “ban it or subscribe,” but investing in open alternatives —educational models and tools that the region’s universities and states can audit, adapt, and sustain without depending on a foreign API that can raise its price or change its terms of use without notice. The public policy question isn’t only a pedagogical one.
Adaptive governance, not sides. Neither centralized prohibition nor “let each teacher figure it out” works. Mollick himself arrives at a similar conclusion at the level of the syllabus (explicit categories of allowed/limited/prohibited use, stated clearly); scaled up to an education system, that calls for public policy built with teacher participation, not handed down from above or left to the market.

A pharmacological sensibility, not premature resolution

It’s worth resisting two symmetrical temptations: panic (“AI is ruining critical thinking”) and uncritical enthusiasm (“finally, every student has a personal tutor”). Following Stiegler, AI in the classroom is a pharmakon: the same tool that can widen access to personalized explanation can also deepen dependency on someone else’s infrastructure. Holding on to that ambivalence —instead of settling it with a definitive yes or no— is, paradoxically, the more rigorous position. Adopting Mollick’s seven roles as a concrete toolkit: yes. Adopting them as if they alone settled the question of who AI in Global South education actually serves: no.

What follows is the operational counterpart of all this: not what one ought to think about AI in the classroom, but what actually goes into a course syllabus on Monday morning.

AI policy for the classroom: a model syllabus clause and institutional guide (v0.1)

Wed, 26 Aug 2026 00:00:00 +0000

Note

This is a first version (v0.1): a starting point meant to be adapted course by course and institution by institution, not a closed text. It combines Mollick’s classroom pragmatism with UNESCO’s human-centered ethics and EDUCAUSE’s institutional governance framework.

The previous entry in this chapter discusses what it means to think about AI in the classroom with agency and a critical eye. This is its operational counterpart: a text a teacher can paste directly into their syllabus, and a guidance note for thinking about policy at the level of a single course or of an institution.

Syllabus box (ready to paste)

AI use in this course. AI use is organized into three categories —allowed, limited, or prohibited— and each assignment will state which one applies.

When AI is allowed or limited, whoever uses it must disclose it, verify what it produces, and remains solely responsible for the accuracy, integrity, and final form of their work.

AI may never be used to fabricate sources, data, quotations, or results, or to upload confidential, personal, or institutionally sensitive information to unapproved systems.

Faculty guidance note

Why three categories instead of a yes or a no

A blanket ban is, in practice, unenforceable and impossible to verify; an unconditional “anything goes” empties out much of what assessment is for. Explicitly defining what’s allowed, what’s limited to certain stages, and what’s prohibited —and saying so in every assignment, not just once at the start of the term— is what actually avoids both easy way outs.

AI allowed: for what aims at exploration, feedback, brainstorming, planning, translation, or practice. Whoever uses it still has to verify the output and disclose meaningful use.
AI limited: usable only at the stages or for the purposes an assignment explicitly names (for example, for a first outline, but not for drafting the analysis). Anything not named is not permitted.
AI prohibited: for instances meant to assess unaided reasoning, in-class performance, oral explanation, source reading, or any task involving protected or confidential information.

Assignment-level wording

Each assignment should name its category and, if “limited,” state in one or two sentences exactly what’s permitted. For example:

“AI limited: you may use it to brainstorm possible research questions and to polish the writing, but not to generate the analysis or the references.”

This reduces the most common ambiguity —“can I do this with AI or not?"— and keeps the standard consistent across sections and instructors of the same course.

Disclosure

A brief statement at the end of the assignment is enough:

“I used an AI tool to brainstorm and to revise the writing; I checked the output against the course readings and edited the final version myself. Any remaining errors are my own.”

Assessment design

Since AI can now complete a good share of traditional take-home tasks, it’s worth shifting some weight toward evidence of process and performance: annotated drafts, a brief oral defense, in-class work, process logs, and assignments anchored in specific discussions from the course that a model can’t reconstruct without having been in the room.

Privacy and ethics

State explicitly: don’t upload personal data, other students’ information, unpublished research material, or internal documents to systems that haven’t been approved by the instructor or the institution. This is especially sensitive in methods courses, practica, and fieldwork.

Short faculty template

AI allowed: you may use it to generate ideas, practice, and revise; disclose meaningful use and verify what it returns.
AI limited: only for what this assignment explicitly names; any other use is not permitted.
AI prohibited: don’t use it for this assignment, because it assesses your unaided reasoning or because protected information is involved.

From the syllabus to the institution

A syllabus box solves the problem at the level of a single course. An institutional policy —what it would actually take for this to stop depending on each instructor’s goodwill— also needs:

A shared vocabulary across courses and departments, so that the same phrase (“AI limited”) means the same thing throughout a program.
Approved platforms and clear data-protection criteria, coordinated with whoever manages infrastructure and privacy at the institution —not each course negotiating on its own with a vendor.
Faculty development, not just a memo: room for each course to adapt the three categories to its own discipline.
Explicit equity of access: if the policy assumes a paid subscription or a personal device, it also has to provide an alternative for whoever doesn’t have one.

This last point connects back to the previous entry in this chapter: an institutional policy that never asks who can afford access, and with what infrastructure, solves the individual classroom’s problem while reproducing the same asymmetry at the scale of the whole institution.

A policy declares what may be done; it does not show what was done. The next entry reviews a technical proposal for closing that gap —leaving a verifiable record of every intervention by the model— and what it costs.

Blockchain to audit the AI tutor: a technical proposal for traceability

Sat, 29 Aug 2026 00:00:00 +0000

The problem: a tutor that isn’t accountable

Xiao, Huang, Huang, Ren, and Li start from a diagnosis already familiar in this chapter: LLM-based systems that today tutor, assess, or generate content in the classroom inherit the limitations of the model underneath them —hallucinations, insufficient domain-knowledge validation, inconsistent output— and those failures can translate directly into worse learning outcomes. Their question isn’t whether an LLM belongs in online higher education, but what happens after an error: who can trace it, prove it, and be held accountable.

The proposal: a ledger no one can alter

Their solution combines two pieces. On one side, LLM-based services that provide the intelligent educational interface —personalized tutoring, content generation, automated assessment. On the other, a consortium (permissioned, not public) blockchain that acts as a secure, tamper-proof ledger for everything worth auditing: learning-process data, academic credentials, and the outputs the LLM produces. The result is a fully auditable trail that makes it possible to attribute responsibility when an educational shortfall originates in a model error, without relying on someone reporting it voluntarily.

How it talks to the rest of the chapter

This technical proposal works as the infrastructural counterpart to something the previous entry in this chapter solves by rule: the usage declaration asks the student to say what they did with AI and own the result; here, the system itself is asked to leave an unalterable trail, without depending on the good faith of whoever declares. They’re complementary answers to the same problem —how to sustain accountability when AI is in the loop— from two different levels: the syllabus rule and the technical infrastructure.

But it’s worth reading with the same caution applied to Mollick’s framework. A consortium blockchain isn’t free: it requires coordinating infrastructure across institutions, nodes that someone has to run and maintain, and a consortium governance that decides who’s in and who’s left out —the same cost and access questions already raised around data coloniality and epistemic commons. And there’s a tension the paper doesn’t discuss: immutably logging each student’s “learning process,” errors included, is also building a permanent record of their attempts and mistakes. Auditing the model shouldn’t come at the cost of the learner’s privacy.

The whole proposal assumes an institution able to sustain an infrastructure like this: to decide on it, fund it and govern it. The next entry reviews the global evidence on that capacity, and the picture is a good deal less encouraging than the technical design.

Anticipatory governance for AI in the university: what the global evidence says

Sat, 29 Aug 2026 00:00:00 +0000

The object: how AI is governed in the university, not just the classroom

The chapter’s first two entries look at AI from the syllabus level —what to do with an LLM in a specific class, how to write a course policy. Baroudi shifts scale: a scoping review of 19 academic and grey-literature sources (2020–2025) on how higher education institutions are governing, or trying to govern, the transformation AI brings at the organizational and system level.

The finding: anticipatory governance, not reactive governance

Across the literature reviewed, the recurring model is “anticipatory governance”: foresight, active engagement from faculty, students, and other stakeholders, and a shift in the traditional role of leaders and educators toward profiles that are data-literate, inclusive, collaborative, and forward-looking. The underlying idea is simple but demands reorganizing the institution: don’t wait for AI’s consequences to land and then react, but anticipate scenarios and build institutional capacity before the problem reaches the classroom.

The gap that matters for this chapter

What matters most for this chapter’s throughline is the gap the review finds between theory and implementation: anticipatory governance frameworks exist in the literature, but run into weak policy frameworks and limited digital infrastructure, particularly in the Global South —the review names the Arab world, Sub-Saharan Africa, and Southeast Asia explicitly. It’s the same tension flagged when reading Mollick (individual agency without institutional capacity isn’t enough), and the same one that motivates the blockchain proposal in the previous entry (a traceability infrastructure that presupposes exactly the institutional capacity this review finds missing across much of the planet). Three entries, three scales —syllabus, technical system, system of governance— for the same problem: the gap between what a conceptual framework lets us think and what an actual institution can sustain.

All three scales share an assumption none of them stops to examine: that we know what we are talking about when we say agency, authorship or responsibility. While the discussion stays practical, the assumption holds; the moment a rule has to be written, it collapses. That is the business of the next chapter.

Education | guIA

Mollick in the classroom: a critical and agential reading

The practical map: from prohibition to engagement

A critical and agential reading

A pharmacological sensibility, not premature resolution

Suggested reading

AI policy for the classroom: a model syllabus clause and institutional guide (v0.1)

Syllabus box (ready to paste)

Faculty guidance note

Why three categories instead of a yes or a no

Assignment-level wording

Disclosure

Assessment design

Privacy and ethics

Short faculty template

From the syllabus to the institution

Suggested reading

Blockchain to audit the AI tutor: a technical proposal for traceability

The problem: a tutor that isn’t accountable

The proposal: a ledger no one can alter

How it talks to the rest of the chapter

Suggested reading

Anticipatory governance for AI in the university: what the global evidence says

The object: how AI is governed in the university, not just the classroom

The finding: anticipatory governance, not reactive governance

The gap that matters for this chapter

Suggested reading