Mollick in the classroom: a critical and agential reading

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.

Suggested reading

Mollick, E. (2024). Co-Intelligence: Living and Working with AI. Portfolio.

Mollick, E. and Mollick, L. (2023). Assigning AI: Seven Approaches for Students, with Prompts.

UNESCO (2023). Guidance for generative AI in education and research.

EDUCAUSE (2024). 2024 EDUCAUSE Action Plan: AI Policies and Guidelines.