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A clear course AI policy connects every permission or restriction to a learning outcome, names exactly what students may do for each assignment, and tells them how to disclose use, verify outputs, protect information, and ask questions. The most workable policy is usually not a single course-wide yes or no: it sets a baseline in the syllabus and adds assignment-specific rules where the learning objective changes.
Start with what students must learn
Do not begin by choosing a chatbot or copying a generic prohibition. Begin with the knowledge, skills, and processes students are being assessed on. For each outcome, ask: What must the student be able to do without AI evidence? and Where could a tool support practice without replacing that performance? Arizona State University, the University of Washington, Queen’s University, and the University of New England all frame course decisions around learning goals and assessment design rather than around a universal rule.
Separate independent demonstration from supported practice
Classify each significant task before writing a policy. A single course may legitimately use all three categories.
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|---|---|---|
| Independent demonstration | The student’s own reasoning, calculation, writing process, oral explanation, or practical performance | No generative AI for the assessed product; define any separate grammar, accessibility, or reference-tool exception. |
| Supported practice | Learning through feedback, rehearsal, brainstorming, translation, or low-stakes examples | Permit named uses, with verification and disclosure when the work is submitted or discussed. |
| Applied or professional work | Judgment about when and how to use tools responsibly in a discipline | Permit selected tools and require a record of prompts, outputs, decisions, revisions, and source checks. |
This classification prevents a contradiction in which one paragraph bans AI while an assignment quietly expects students to use it. It also gives students a reason for the boundary. A restriction is easier to follow when it explains the capability the assignment is designed to measure.
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Write a short rationale
Use one or two plain-language sentences for each major rule. For example: “This draft is an independent assessment of your ability to construct and support an argument, so generative AI may not produce text or analysis for the submitted paper.” A different assignment might say: “This workshop assesses your ability to critique machine-generated suggestions; using a named tool is part of the task.” The rationale should describe the learning goal, not imply that all AI use is inherently good or bad.
Choose an explicit policy posture
Students should be able to identify the default without interpreting vague phrases such as “use AI responsibly.” Official university examples commonly use one of three postures. None is a universal ranking; the choice depends on institutional rules, course outcomes, and assessment design.
| Posture | Tell students | Use this decision test |
|---|---|---|
| Generally permitted within guidelines | Which tools and tasks are approved, which work remains prohibited, whether disclosure or citation is required, how to verify output, and what information must not be entered | Does tool use support the course goals while students still show their own learning? |
| Allowed only for named assignments or with permission | The assignments that permit use, the permitted tasks, the required attribution, and the default rule everywhere else | Do some outcomes require independent performance while other activities benefit from supported practice? |
| Not permitted | The tools and work covered, the learning rationale, any grammar or accessibility boundary, and where students can ask about an edge case | Is independent production itself a central skill or the assessment target? |
State the posture in the first page of the syllabus, then repeat the relevant rule in every assignment. If a course uses different postures, call that out directly: “The course default is restricted use; the exceptions are listed under Assignments 2 and 4.”
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Translate the policy into assignment-level instructions
Students make decisions while completing an individual task, not while rereading a general syllabus paragraph. Give each assignment a compact permission block. A matrix like the following can help you audit consistency before publishing.
| Assignment type | Allowed use | Not allowed | Evidence to submit |
|---|---|---|---|
| Research proposal | Brainstorming keywords or questions, if the assignment permits it | Having a tool select the research question or write the rationale | Disclosure naming the tool and purpose; independent evaluation of suggested sources |
| Problem set assessed for method | Practice problems outside the submitted set, when stated | Generated calculations, steps, or final answers in the assessed set | Show your own working and be prepared to explain a solution |
| Revision workshop | Suggestions about clarity or organization, if the instructor allows them | Replacing the student’s argument, evidence, or voice | Original passage, suggestions considered, and changes made |
| AI-critique exercise | Use of the specified tool to create an output for analysis | Submitting the output as the student’s own work | Prompt, output, fact-checking notes, critique, and revised version |
Name both permitted and prohibited actions
Concrete verbs are easier to apply than categories alone. Say “you may ask for three possible headings” or “you may not generate paragraphs for the submitted reflection.” Clarify whether a tool may translate a source, summarize a reading, create code comments, suggest test cases, or provide grammar feedback. If a category is not addressed, students may reasonably reach different conclusions.
Handle grammar, accessibility, and group work explicitly
A total ban can leave students unsure about spelling assistance, screen-reader features, speech-to-text, translation, or disability-related accommodations. State whether ordinary proofreading tools are allowed, whether an accessibility tool requires disclosure, and whom to contact for an accommodation. For group work, specify whether one member may use a tool on behalf of the group, how that use is recorded, and whether every member remains responsible for checking the result.
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Draft a syllabus rule students can act on
Use the following sequence to turn your decisions into a short, readable policy. Adapt the wording to your institution’s academic-integrity and technology rules rather than presenting it as a universally valid policy.
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- Give the rationale. Link the rule to a named course outcome or assessment purpose.
- Define the scope. Say whether it applies to the whole course, particular assignments, exams, discussion posts, code, images, translation, or group work.
- State the default posture. Use “permitted,” “restricted,” or “not permitted,” and identify exceptions immediately.
- List allowed actions. Name the tool category or task and the limits, such as brainstorming only, private practice only, or a specified classroom activity.
- List prohibited actions. Include generating submitted text, fabricating citations, solving assessed problems, impersonating a student, or concealing required use when those actions conflict with the learning goal.
- Specify disclosure or citation. Tell students what to name, where to record it, and how to describe their evaluation and revisions.
- Explain accuracy and privacy duties. Students remain responsible for claims, calculations, quotations, sources, code behavior, and the information they enter into a tool.
- Provide a question route and process. Give a contact point before submission and state that suspected breaches are handled under institutional procedures.
- Review the wording. Check clarity, accessibility, equity, consistency with university rules, and alignment with each assessment.
- Publish in two places. Put the baseline in the syllabus and repeat the task-specific rule in the assignment instructions students will use.
Queen’s University uses a useful fill-in frame: “In this course, Generative AI is permitted/restricted/not permitted because learning rationale.” The blanks are prompts for your decision, not language to leave unresolved.
Adaptable assignment language
For a permitted task, you might write:
For this assignment, you may use an AI tool to
name the specific tasks. You may not use it toname the prohibited tasks. If you use it, disclosetool and purposeand explain how you checked, revised, and verified the output. You remain responsible for the submitted work.
For a restricted course, write:
The course default is restricted AI use. Unless an assignment page grants an exception, do not use generative AI to create, rewrite, translate, solve, or substantially structure assessed work.
State any separate grammar, accessibility, or practice exception here.
These are components to adapt after checking local requirements, not a ready-made policy that overrides them.
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“Cite AI” can mean different things. Distinguish a disclosure of assistance from a conventional citation of material that readers can retrieve. Your policy should say which is required for each assignment.
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Minimum disclosure fields
- The tool’s name and version or access date when that information is available.
- The purpose of use, such as brainstorming, language feedback, coding help, translation, or generating an object for critique.
- The material affected: identify the section, code, image, table, or idea.
- How the student evaluated the output, including fact, source, calculation, or bias checks.
- What the student changed, rejected, or verified before submission.
- Any required prompt or output record, subject to privacy and copyright limits.
A University of Washington sample asks students to identify the tool, describe its use, and explain how generated content was evaluated, modified, and verified. You can require a short statement at the end of a paper, a process log, or an appendix. Tell students whether an unused experiment must also be reported; do not leave that inference to them.
Give a practical record format
Tool and access date: [record the name and date]Purpose: [describe what the tool was asked to do]Material used: [identify the section or artifact]Checks performed: [list source, fact, calculation, or code checks]Changes made: [summarize what was accepted, revised, or rejected]
Keep variable syntax such as the examples above inside the form students will replace; explain whether a transcript, screenshot, export, or concise summary is acceptable. If an assignment requires a formal citation style, provide an example in that style and explain whether the disclosure is additional to the citation.
Make accuracy and privacy non-negotiable
AI output can contain incorrect facts, invented sources, faulty calculations, insecure code, or a confident answer to a question it misunderstood. A permission to use a tool never transfers responsibility for the final work. Require students to verify output in ways appropriate to the discipline.
A verification workflow students can follow
- Identify every factual claim, quotation, citation, calculation, image attribution, and code dependency supplied or influenced by the tool.
- Check claims against authoritative course materials, primary sources, library resources, documentation, or a reproducible calculation.
- Open and inspect each cited source; do not rely on a generated reference list or a link that has not been checked.
- Test code, formulas, or data transformations with known cases and inspect edge cases.
- Compare the result with the assignment rubric and revise language that overstates certainty or hides limitations.
- Record the checks and changes required by the assignment’s disclosure rule.
Define information that must stay out of prompts
Tell students not to enter personal, sensitive, confidential, proprietary, copyrighted, or protected information unless institutional rules explicitly permit that use with an approved tool. Examples include student records, client or patient details, unpublished research data, examination materials, passwords, access tokens, private correspondence, and identifying information about classmates. Anonymization is not automatically safe: combinations of details can re-identify a person. Point students to the institution’s privacy, research-ethics, information-security, and accessibility guidance, and name an approved alternative when one exists.
Give students a safe way to ask before they submit
Uncertainty is predictable when a new feature combines search, translation, writing, coding, or image generation. The University of Washington’s guidance offers a direct model: “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.” Put an equivalent sentence in the syllabus and assignment pages.
Make the route operational: identify office hours, a private message channel, or a question form; state the usual response time; and invite students to ask before assessed work is submitted. Students should be able to describe the tool, the proposed task, and the information they would enter. If waiting for an answer would create an access problem, provide a default safe action, such as completing the task without the tool or using only the explicitly listed accessibility support.
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Place expectations where decisions happen
- Syllabus: State the course default, rationale, scope, disclosure rule, privacy warning, question route, and relationship to academic-integrity procedures.
- Assignment page: Repeat only the rules for that task, with allowed and prohibited examples and the required evidence.
- Class discussion: Explain an example on the first day or establish a class agreement. Ask students to identify an ambiguous case and resolve it together.
- Rubric: Grade the learning outcome and required process. If verification or disclosure is part of the objective, make it a visible criterion.
- Submission form: Include a yes/no disclosure question and a text field or upload slot when a record is required.
Consistent placement matters: a student should not have to search several documents to discover that the rule for an assignment differs from the course default.
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Do not treat an automated detector score as proof of unauthorized use. Teaching@UW states that no technology can reliably identify unauthorized student use of AI. The University of New England likewise says a detector result may prompt discussion or investigation but cannot be the sole basis for a misconduct finding.
A fair response sequence
- Check the published rule for the exact assignment and confirm that the student had access to it.
- Preserve relevant evidence, such as drafts, version history, process notes, source checks, or a required disclosure. Avoid collecting unnecessary personal data.
- Invite the student to explain the process and, where appropriate, demonstrate the underlying skill orally or in a supervised setting.
- Compare the evidence with the learning outcome and institutional academic-integrity procedure.
- Document the decision, give the student the appeal or review route required by the institution, and apply consequences consistently.
Do not create a hidden evidentiary standard that students could not have known. If you want drafts, prompt logs, oral defenses, or in-class checkpoints, announce that requirement in advance and explain how it supports the assessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check equity, accessibility, and institutional fit
Before publishing, ask whether the rule creates an unnecessary barrier for students with disabilities, multilingual learners, limited connectivity, or unequal access to paid tools. A prohibition on one tool should not silently prohibit an approved accommodation. Offer an equivalent way to meet the outcome, and coordinate with disability or accessibility services rather than asking students to disclose private medical details to the class.
Review the policy with your department, academic-integrity office, teaching center, and privacy or information-security office as appropriate. Confirm that the named tools are permitted in your jurisdiction and institution, that copyrighted or protected material is handled lawfully, and that your consequences match existing procedures. UNESCO’s 2023 Guidance for generative AI in education and research frames policy and instructional design around ethical, effective, and human-centered use; that principle supports checking who benefits from a rule and who may be disadvantaged by it.
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Audit the policy before students see it
- Can a student state the default posture in one sentence?
- Does every major assignment have its own permission block?
- Are allowed and prohibited actions expressed as observable tasks?
- Does the policy explain grammar, translation, accessibility, images, code, and group-work edge cases where they matter?
- Does disclosure identify the tool, purpose, affected work, checks, and revisions?
- Are accuracy, source verification, and privacy responsibilities explicit?
- Is there a pre-submission question route and a safe default for unresolved cases?
- Are detector results excluded as the sole proof of misconduct?
- Does enforcement follow institutional procedure and preserve an opportunity to respond?
- Is the language accessible, readable, and available in the places students complete the work?
- Have you checked alignment with learning outcomes, assessment design, equity, inclusion, and approved accommodations?
Worked examples of clear boundaries
Example: original research question
An assignment intended to assess independent topic creation can prohibit generated research questions and rationales while allowing ordinary library-search instruction. Explain the purpose in the assignment itself. Hsinmei Lin, a lecturer at UW Bothell, illustrates this kind of rationale with the sentence: “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.” Attribute such wording as an illustrative assignment statement, not as a universal institutional rule.
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Require the student to submit a short account of how the question developed, the sources consulted, and any feedback received. If brainstorming with a tool is allowed during an ungraded exercise, state that the exercise is separate and may not be transferred into the assessed submission without permission.
Example: critique of generated analysis
Make the tool part of the learning objective: provide a prompt or specify the permitted tool, require students to identify errors and unsupported claims, and grade the critique rather than the generated prose. The submission can include the original output, a fact-check table, the student’s corrections, and a reflection on where the tool failed. Prohibit replacing the critique with an unexamined rewrite.
Example: programming assignment
If students are learning algorithm design, prohibit generated implementation in the assessed solution but allow a separate practice environment. If the objective is professional tool evaluation, permit code generation for a named task and require tests, dependency inspection, security review, attribution, and an explanation of every accepted section. State whether autocomplete, debugging assistants, and generated documentation fall under the same rule.
Give students a simple decision rule
End the policy with a short procedure students can remember:
- Read the assignment’s AI box, not just the syllabus default.
- Identify what the tool would create or change.
- Check whether that action is explicitly allowed.
- Remove personal, confidential, or protected information from any prompt.
- Ask before using the tool if the rule does not clearly cover the case.
- Verify every consequential output and complete the required disclosure.
- Submit only work you can explain and defend as evidence of the stated learning outcome.
That sequence turns a broad technology question into a series of decisions tied to the course’s purpose. It also gives instructors a consistent basis for revising rules when tools, assignments, or institutional requirements change.
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