Institutional policy (v0.1)

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

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.

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