Set AI rules from your learning outcomes, then state them at two levels: a course-wide position in the syllabus and precise permissions on each assignment. Tell students exactly what they may do, what they may not do, how to disclose or cite use, what information must stay out of prompts, and where to ask before submitting work. Keep students responsible for the accuracy and originality of what they submit, and never treat an AI-detector score as proof of misconduct.
Start with the learning outcome, not the tool
A useful AI guideline begins with the question: What must students be able to know or do themselves by the end of this task? The answer determines whether an AI tool supports practice, undermines the assessment, or is irrelevant.
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For example, an assignment assessing independent topic generation may require students to produce the initial idea without generative AI. An assignment assessing revision may permit a tool to suggest wording after the student has drafted the argument. A coding exercise may allow AI for debugging but require the student to explain every change. These are different rules because they measure different abilities.
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Identify the work students must perform themselves
- List the knowledge, reasoning, communication, technical or professional skills the assignment is intended to demonstrate.
- Mark which steps are the evidence of learning: choosing a question, solving a problem, interpreting data, drafting an argument, producing code, or reflecting on a process.
- Decide whether AI assistance would leave that evidence intact, change the skill being assessed, or make the evidence impossible to interpret.
Allow support where it advances the objective
AI can be useful for low-stakes practice, brainstorming after an initial attempt, generating questions for review, explaining a difficult concept in different words, or identifying possible organizational problems in a draft. If you allow such uses, define them as permitted tasks rather than assuming students will infer the boundary.
Explain the rationale briefly
A short reason helps students apply a rule to a new tool. Queen’s University offers the fill-in frame: “In this course, Generative AI is ____________ because ____________.” Complete it with the skill being protected or practiced, not with a general opinion about technology.
Choose and state one of three policy postures
Most course policies fit one of three approaches. None is universally best; the decision depends on the learning outcomes, the assignment, institutional rules and the support students need.
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| Approach | What students must know | Decision test |
|---|---|---|
| Generally permitted within guidelines | Approved uses, prohibited work, disclosure or citation requirements, fact-checking duties and privacy limits | Does assistance support the course goals while students still demonstrate their own learning? |
| Allowed only for named assignments or with permission | Which assignments permit use, the exact tasks allowed, required attribution, and the default rule elsewhere | Do some objectives require independent performance while other activities benefit from supported practice? |
| Not permitted | Which tools and forms of work are covered, the learning rationale, any exception for grammar or accessibility support, and where students can ask | Is independent production itself a central skill or assessment outcome? |
Use plain language such as “You may use an AI tool to generate practice questions for the ungraded study worksheet” rather than “Use AI responsibly.” If the rule changes by task, make the assignment-level rule control that task and say so explicitly.
Put the policy where students will use it
Course syllabus
Include the overall posture, the reason for it, disclosure expectations, privacy warnings, the process for questions and the relationship to your institution’s academic-integrity procedure. The syllabus should tell students what to expect, not attempt to list every future tool.
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Assignment instructions
Repeat the operational rule next to the task. State the permitted tool category or function, the prohibited functions, whether collaboration is allowed, and what documentation must accompany the submission. Students should not have to search a long syllabus to determine whether a particular step is allowed.
Class discussion and a standing question channel
Explain the policy on the first day or establish a class agreement. Give students a predictable place to ask before they use a tool for assessed work. The University of Washington sample wording is direct: “If you are uncertain whether a specific tool or technology qualifies as generative AI or whether its use is permitted, please consult with me before using it.”
Define permitted and prohibited actions with examples
For every assignment that involves AI, provide examples on both sides of the boundary. “AI allowed” is incomplete because a tool can perform many different functions.
Examples of permissions to specify
- Brainstorming possible questions after the student has identified a subject.
- Creating additional practice problems for study, without submitting generated answers.
- Receiving feedback on organization or clarity after writing an original draft.
- Debugging code when the student documents the error, the suggested change and the final explanation.
- Using an approved accessibility or language-support function when institutional rules permit it.
Examples of restrictions to specify
- Generating the thesis, analysis, solution or reflection that the assignment is designed to assess.
- Submitting generated text, images, code or citations as original work without disclosure.
- Uploading another person’s personal data, unpublished work, confidential case material or protected records.
- Using a tool to fabricate sources, quotations, observations, laboratory results or interview responses.
- Allowing a tool to complete group work without telling the group and following the assignment’s collaboration rule.
Address edge cases such as grammar and style assistance, translation, image generation, code completion and group projects. If a general prohibition is intended to include these uses, say so; if an accessibility or approved-support exception applies, state its limits and documentation requirements.
Require useful disclosure or citation
Students need to know whether disclosure is required for every use, only for generated material, or only when a tool materially changes the work. Your institution may have a required format; follow it rather than inventing a competing standard.
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A practical record identifies the tool, the date or version when relevant, the purpose, the material affected and the student’s review. The University of Washington sample asks students to identify the tool, describe how it was used, and explain how generated content was evaluated, modified and verified. Adapt that model to your course.
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- Tool: Name the service or tool category and, if available, the model or version.
- Purpose: State whether it was used for brainstorming, explanation, editing, coding, translation, image creation or another defined task.
- Extent: Identify which section, file or stage was affected.
- Review: Describe what the student checked, corrected, rewrote or rejected.
- Verification: Confirm that factual claims, quotations, calculations and sources were independently checked.
For a minor permitted use, a short statement may be enough. For a substantial use, require a prompt-and-output record or an appendix if that is consistent with privacy and accessibility requirements. Do not require students to submit sensitive prompts or personal information that should never have been entered into a tool.
Make accuracy and source verification the student’s responsibility
Generative systems can produce plausible but incorrect claims, invented citations, faulty calculations and outdated information. Permission to use a tool is not permission to outsource judgment.
Tell students which claims require checking and what counts as acceptable evidence. Depending on the course, that may mean consulting the assigned readings, primary documents, an authoritative database, a calculation they can reproduce, or a source approved in the rubric. Require students to remove unverifiable material rather than cite a tool as if it were an authority.
Include a sentence such as: “You are responsible for the accuracy, attribution and originality of everything you submit, including material suggested or transformed by an AI tool.” Tie that responsibility to the grading criteria so it is enforceable and understandable.
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Protect privacy, confidential work and intellectual property
Students should not put personal, sensitive, confidential or protected information into a tool unless institutional rules specifically permit it and the tool has been approved for that information. This includes identifiable student records, patient or client details, unpublished research, examination questions, private peer feedback, access credentials and copyrighted material that the student is not authorized to upload.
- Tell students what categories must never appear in prompts.
- Point them to the institution’s privacy, data-classification and approved-technology guidance.
- Require de-identification where an exercise genuinely needs example data, and explain that removing a name may not remove identifying details.
- Provide a non-AI route for students who cannot or should not use a particular service.
Privacy rules can differ by institution, program and jurisdiction. A course statement should defer to the stricter applicable rule instead of promising that a commercial tool is private.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a repeatable drafting sequence
- Write the rationale. Connect the boundary to one or more learning outcomes and the evidence the assignment must produce.
- Set the scope. Decide whether the rule is course-wide, assignment-specific or both. State the default when no assignment-specific instruction appears.
- List allowed actions. Name functions students may use, not just a tool brand or a vague category.
- List prohibited actions. Include submission, fabrication, privacy and collaboration boundaries.
- Define attribution. State whether students must disclose every use, selected uses or only generated content, and specify the required details.
- Add verification duties. Explain how students must check facts, sources, calculations, quotations and code.
- Add privacy limits. Identify information that must not be entered and the approved-tool requirement, if any.
- Provide a question path. Tell students where to ask before submitting and how quickly or in what format you will answer when practical.
- Explain handling of suspected breaches. Refer to the institution’s academic-integrity process, possible review and the student’s opportunity to respond. Do not create a penalty that conflicts with university rules.
- Review the draft. Check clarity, accessibility, equity, inclusion, assessment alignment and consistency with institutional technology and integrity policies.
- Publish twice. Place the course position in the syllabus and repeat the operative rule in each assignment’s instructions.
Adapt sample language without turning it into a universal policy
Use these elements as drafting prompts, then replace the brackets and check them against local rules:
- “In this course, [tool category/use] is [permitted/restricted/not permitted] because [learning rationale].”
- “For [assignment], you may use AI to [specific tasks]. You may not use it to [specific tasks]. If you use it, disclose [tool and purpose] and explain how you checked and revised the output.”
- “If you are unsure whether a tool or use is allowed, ask the instructor before using it for assessed work.”
Hsinmei Lin, a lecturer at UW Bothell, illustrates how a learning rationale can shape a boundary in an assignment statement: “The goal of this two-fold assignment is for you to go through the critical thinking process of how to create an original research topic on your own.” Treat that as an example of assignment-specific reasoning, not as a rule that automatically applies to every course.
Design enforcement around learning evidence, not detector scores
Teaching@UW states that no technology can reliably identify unauthorized student use of AI. The University of New England likewise cautions that a detector result may prompt a conversation or investigation but cannot be the sole basis for a misconduct finding.
Best Value
Use evidence that fits the course and the assignment:
- Ask the student to explain their process, decisions, sources or code.
- Compare the submitted work with drafts, notes, revision history or in-class performance when those records are part of normal instruction.
- Check whether required citations, calculations or explanations can be verified.
- Follow the institution’s notice, documentation, confidentiality and appeal procedures.
A detector can be one limited signal in a broader review where institutional policy allows it, but an automated percentage is not a finding. Avoid promising students that a particular detector is definitive or requiring them to prove a negative based on an opaque score.
Review the policy from a student’s point of view
Before publishing, ask a colleague or student-support professional to test the wording against realistic scenarios. A clear policy should answer these questions without guesswork:
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- Does the rule distinguish learning support from replacing the assessed work?
- Are grammar, translation, accessibility tools and group work addressed?
- Is the disclosure burden proportionate to the permitted use?
- Can a student complete the assignment without an inaccessible or unapproved tool?
- Does the policy avoid disadvantaging students who have different language backgrounds, disabilities, devices or internet access?
- Does it match current academic-integrity, privacy, copyright and technology rules?
UNESCO’s Guidance for generative AI in education and research frames policy and instructional design around ethical, effective and human-centered use. That perspective supports a policy that protects learning and privacy while giving students a workable way to use approved assistance where it genuinely helps.
Quick Recap
A compact decision framework for each assignment
| Question | If the answer is yes | If the answer is no |
|---|---|---|
| Is independent production the skill being assessed? | Restrict or prohibit AI for that step; explain the rationale. | Consider defined assistance that leaves the intended evidence intact. |
| Can the student verify and explain the tool’s contribution? | Permit the use only with a proportionate disclosure and review record. | Do not permit that use for assessed work. |
| Could the prompt contain protected or confidential information? | Require an approved environment or prohibit the upload and provide an alternative. | Still apply ordinary privacy and copyright rules. |
| Would a reasonable student know the boundary from the instructions? | Publish the rule in the assignment and syllabus. | Add examples, definitions and a question route before release. |
What a clear final policy should contain
- A learning-outcome-based reason for the rule.
- The course-wide default and any assignment-specific exceptions.
- Concrete permitted and prohibited actions.
- Disclosure or citation requirements, including what to record.
- Accuracy, source-checking and originality responsibilities.
- Privacy and confidential-information restrictions.
- Rules for grammar, accessibility support, translation and group work where relevant.
- A pre-submission route for uncertain cases.
- A reference to institutional procedures for suspected breaches.
- A commitment to review the wording when assignments, approved tools or institutional rules change.
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