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There is no universally correct AI rule for a course. Instructors should begin with the learning outcome and assessment: permit AI when its use supports the skill being taught, restrict it to named tasks when only some activities benefit, and prohibit it when AI would perform the thinking or practice the assignment is meant to assess. The rule must also fit institutional policy and be explained in operational terms.
A workable policy states which tools and actions are covered, what students must disclose, how they must verify output, what information they must not upload, and where to ask about an ambiguous case. Course-wide language should be repeated in each assignment when the permitted or prohibited uses differ.
Start with the learning goal, not with the tool
Ask what a student must know or be able to do without outsourcing the activity. If an assignment assesses close reading, unaided writing, mathematical reasoning, debugging skill, or oral explanation, having a generative system produce the central work can bypass the learning process. A restriction is then tied to assessment validity rather than to a general dislike of AI.
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Make the reasoning visible to students: identify the capability being developed, explain how the rule protects or exercises that capability, and distinguish practice activities from assessments of independent performance.
Three policy approaches that can work
| Approach | When it may fit | Operational details to state |
|---|---|---|
| Broad permission | AI use is compatible with the learning outcomes, or AI literacy is an explicit course goal. | Name permitted tools or categories; define disclosure or citation; require verification of claims, quotations and code; describe privacy expectations; prohibit outsourcing the parts students must still perform. |
| Conditional or assignment-specific permission | Some activities benefit from AI while other assignments assess unaided capability. | List each named task or assignment, allowed and disallowed actions, disclosure requirements, retained records such as prompts or drafts, and a route for clarifying an uncertain use. |
| Prohibition | AI use would defeat the intended learning activity, assessment, or a clearly explained course constraint. | Define the scope: which tools, features and tasks are covered; connect the prohibition to learning objectives; give examples; explain how questions, accommodations and suspected violations are handled. |
These approaches can coexist in one course. For example, brainstorming with an approved system might be allowed for a project proposal, while the final analysis and in-class exam must be completed without AI.
Can instructors ban AI in a course?
Often an instructor can set course or assignment boundaries, but authority and defaults differ by institution, department, collective agreement and jurisdiction. The University of Minnesota says instructors may allow or prohibit generative AI and strongly encourages clear syllabus expectations linked to academic policies. UVA’s Teaching Hub (updated May 2026) likewise presents prohibition and bounded permission as instructor choices in its institutional context. Those statements do not establish a universal legal rule. Check the applicable campus policy before publishing a ban.
A prohibition should be narrow enough to be understandable and defensible. “No AI” can leave students unsure whether spell-checking, translation, accessibility features, code completion or a grammar assistant count. State the covered tool categories and the specific actions that are prohibited. If an accessibility accommodation or approved support changes the rule, explain the process for arranging it confidentially.
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A decision framework for choosing the rule
1. Identify the capability being assessed
Write the outcome in observable terms: “construct an argument from assigned sources,” “derive and explain the model,” or “diagnose a failing program.” Then ask whether a student could submit convincing work while skipping that capability through AI. If yes, restrict or prohibit AI for that assessment, or redesign the task so the student must demonstrate the capability directly.
2. Separate learning activities from evidence of mastery
Low-stakes practice may permit experimentation and feedback, while a final performance task may require independent work. Mark the difference in the syllabus and in each assignment. A single course-wide rule is not required when the educational purpose changes from one activity to another.
3. Evaluate integrity and accountability
Students remain responsible for everything submitted under their name. Require them to check factual claims, citations, calculations, quotations, code behavior and image provenance. Specify whether they must disclose the tool, date, purpose, significant prompts, generated passages and subsequent edits. A disclosure rule gives instructors evidence of process without pretending that every AI contribution can be detected.
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Before permitting a tool, review institutional guidance and the provider’s terms. Students should not paste personal, confidential or sensitive information, original student work, examination content or copyrighted material into a third-party system unless the relevant permissions and safeguards are clear. Consider whether every student can access the required tool, whether paid features create an inequity, and whether an accessible alternative is available.
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5. Confirm institutional fit
Align the course language with the academic-integrity code, copyright guidance, records or privacy requirements, and any approved-tool list. State that institutional rules prevail if they change. Do not present an instructor’s preference as a campus-wide mandate.
How to write an AI policy in your syllabus
Use the following sequence to turn a principle into instructions students can follow.
- State the default in one sentence. Say “AI is permitted for the activities listed below,” “AI is permitted only for the named assignments,” or “AI-generated content and automated rewriting are prohibited for assessed work.”
- Name the tools or categories. If the rule applies to chatbots, image generators, code assistants, translation systems, grammar rewriters or built-in platform features, list them. If only an institution-approved option may be used, say so.
- Map uses to assignments. Give concrete examples such as brainstorming, outlining, translation, coding, editing, image generation, data analysis or feedback. Mark each as allowed, allowed with conditions, or prohibited.
- Specify disclosure and citation. Tell students where the acknowledgment goes and what it contains. You might require the system name, access date, purpose, representative prompts, output excerpts and a description of editing. Apply the same standard to permitted uses of generated images, code or translated text.
- Set verification duties. Require students to validate claims, citations, quotations, calculations, code and factual details. Make clear that a fluent answer is not evidence of accuracy.
- Protect data and intellectual property. Warn against entering personal, confidential, original or copyrighted material without checking institutional and tool terms. Explain any approved environment or alternative workflow.
- Connect the rule to consequences. Refer to the academic-integrity process and identify what happens when a student uses AI outside the stated boundary. Do not promise an infallible detection method.
- Provide a question channel. Give an email, discussion board or office-hour route for uncertain cases. Require students to ask before submitting rather than infer permission from silence.
- Repeat exceptions in assignment instructions. A short “AI use for this assignment” box prevents students from having to reconcile a general syllabus paragraph with a different assessment purpose.
- Review the language. Update tools, examples and links when institutional policy or material capabilities change, and announce substantive revisions to the class.
Assignment-level rules are often clearer than a blanket ban
For each assessment, add a compact box with four parts: purpose, permitted actions, prohibited actions and evidence of process. For a research essay, for example, you might permit idea generation and requests for counterarguments, prohibit generated prose in the submitted argument, require independent source checking, and ask students to attach a brief disclosure. For a programming lab, you might permit an approved assistant for syntax suggestions but require students to explain every submitted function and prohibit generated solutions to the core algorithm.
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Accuracy, bias, privacy and copyright need explicit treatment
AI systems can produce incorrect, fabricated or biased output. Tell students which sources or tests must be used to verify it, and grade the verification when it is part of the outcome. Do not claim that a particular system is accurate, unbiased or safe without evidence for the exact version and setting.
Privacy risks arise when a prompt contains identifiable student information, unpublished research, client data or private peer work. Copyright and ownership questions can arise when students upload protected text, images, code or recordings, or when they reuse generated material whose provenance they cannot establish. The UNESCO Guidance for generative AI in education and research (published September 7, 2023; page updated January 16, 2026) frames privacy protection, ethical validation, pedagogical design and coherent policy as parts of a human-centered approach. It is international guidance, not a course-specific mandate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why detection tools should not be the policy
A rule should define acceptable conduct even if no detector exists. The University of Minnesota says it does not license generative-AI detectors, notes that such tools vary in reliability, and does not recommend them as the sole verification of student work. University of Washington Teaching@UW states: “While instructors have the right to prohibit the use of AI tools, no technology can reliably identify unauthorized student use of AI.” That sentence is UW teaching guidance, not a universal legal conclusion.
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What published guidance and observed practice show
UNESCO’s guidance emphasizes human-centered governance rather than a single permission setting. Minnesota, Washington, Virginia and George Washington University each provide examples of instructor-selected boundaries, from prohibition to specified or open permission. George Washington’s model language places responsibility for submitted content on students and distinguishes general, selective and prohibited use; its rules apply to that university and should not be generalized.
A 2024 study, Analysis of Generative AI Policies in Computing Course Syllabi, examined 98 computing-course syllabi from 54 U.S. R1 institutions. Most syllabi in that sample prohibited or restricted generative AI, commonly citing veracity, privacy or learning concerns. The sample describes computing syllabi at those institutions; it is not a census or a representative percentage for all disciplines or countries. Read the study at arXiv.
Sample syllabus language you can adapt
AI use in this course is permitted only where an assignment says “AI permitted.” For each permitted use, identify the tool, date, purpose and material contribution in your submission. Verify every factual claim, quotation, citation, calculation and piece of code; you remain responsible for the final work. Do not enter personal, confidential, unpublished or copyrighted material into a third-party tool unless the course and institutional rules expressly allow it. AI-generated text, code or images may not replace the capability an assignment is designed to assess. If an assignment does not state a rule, ask the instructor before using AI. Uses outside these boundaries are handled under the course and institutional academic-integrity procedures.Replace the broad wording with assignment-specific examples. If you prohibit AI entirely for one assessment, say so again in that assessment and explain the learning objective that requires independent work. If you require AI use, specify the system, approved account, logging or disclosure format, and an equivalent alternative for students who cannot use it.
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Common policy failures and fixes
- Failure: a slogan with no examples. Fix it by listing tools, tasks and edge cases such as translation, grammar rewriting, code completion and image generation.
- Failure: allowing AI but ignoring verification. Fix it with a written responsibility for checking claims, sources, quotations and outputs.
- Failure: treating a detector as proof. Fix it by using process evidence and the normal academic-integrity procedure.
- Failure: copying a policy from another institution. Fix it by checking your own campus rules, privacy requirements, accessibility process and approved-tool guidance.
- Failure: changing the rule silently. Fix it by dating the policy, announcing material changes and applying new requirements prospectively where possible.
- Failure: making students guess whom to ask. Fix it with a named contact route and a promise to answer ambiguous cases before submission.
Bottom line for instructors
Choose the least restrictive rule that still protects the learning outcome and assessment. Permit AI when students are meant to learn or practice responsible use; restrict it by assignment when purposes differ; prohibit it when AI would perform the capability being assessed. Whatever you choose, write concrete boundaries, disclosure and verification duties, privacy warnings, institutional connections and a question process into the syllabus and assignment instructions.
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