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Design the assignment around the learning outcome first, then decide where generative AI belongs. Identify the knowledge, judgment, analysis or practical performance students must demonstrate; permit or restrict AI only where that choice protects (or deliberately develops) that outcome. Make the rule specific to each assignment, provide an equivalent non-AI route when AI is optional, and collect enough process or explanation evidence to judge the student’s learning. This approach is more reliable than trying to make every task “AI-proof,” and it avoids treating AI use itself as misconduct when the assignment never defined the expectation.
The guidance below combines higher-education recommendations from DAMU, Australia’s Tertiary Education Quality and Standards Agency (TEQSA), UNSW, Simon Fraser University, Michigan Technological University and the University of South Carolina. Your university, department and course policies still control what you may require.
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Why should the learning outcome come before an AI rule?
An AI policy is only useful when it protects a clearly stated outcome. Begin by writing what students should be able to do in observable terms, such as “interpret a spectroscopic result and defend the conclusion” or “construct a source-based argument that addresses a counterclaim.” Then identify the evidence that would convince you the student can do it.
Break the task into its meaningful stages and ask what each stage measures:
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- Knowledge: Can the student recall or explain concepts, terminology, methods or professional standards?
- Reasoning: Can the student select evidence, make assumptions explicit, weigh alternatives and justify a conclusion?
- Practice: Can the student perform a calculation, laboratory technique, design procedure, translation or clinical action accurately?
- Communication: Can the student adapt an explanation, visualisation or recommendation to a specified audience?
- Metacognition: Can the student evaluate an approach, identify uncertainty and improve the work?
Mark the stages that require independent demonstration. AI may be useful in another stage without undermining the outcome. For example, a student might use a language model to brainstorm possible examples for a policy memo, while still having to select evidence, verify every claim and defend the recommendation. Conversely, if the outcome is unaided calculation fluency, allowing a model to perform the calculation would remove the very performance you intend to assess.
Michigan Technological University’s teaching guidance frames the decision this way: the goal is not to make an assignment “AI-proof,” but to design it so students engage in the thinking and practice required by the learning objectives. If the existing task still produces valid evidence of that outcome, keep it. If AI can complete the graded work without showing the intended learning, preserve the outcome and change the task, evidence or setting.
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What evidence would demonstrate the outcome?
Write this before choosing a permission category. A persuasive essay may require a defensible thesis, relevant sources and reasoning that connects evidence to claims. A programming assignment may require a working program, tests, an explanation of design choices and the ability to modify the code. A nursing simulation may require observable decisions under time pressure. The more precisely you define the evidence, the less you need to guess how a final product was produced.
When is an assignment worth changing?
Review the task if a generic AI tool can produce an apparently complete submission while bypassing the course’s intended practice. You can revise the prompt to require local data, a student-generated artefact, iterative drafts, a comparison of competing explanations or a live demonstration. Do not add burdensome documentation merely to create an audit trail; every extra requirement should help you judge the stated outcome.
Which AI permission model fits a particular assignment?
Use a separate, plain-language rule for each assignment. UNSW describes a range from no AI use through limited assistance and planning support to permitted or required use. The right category depends on what students must learn, not on a universal course-wide sentence.
| Permission model | Use it when the outcome requires | What the brief should specify | Evidence to collect |
|---|---|---|---|
| No AI for this task | Unaided recall, calculation, performance or an in-class demonstration is itself the outcome. | Name the prohibited tools and stages, explain the pedagogical reason, and identify permitted accessibility support. | In-class work, supervised performance, or another direct demonstration. |
| Limited assistance | Students may benefit from proofreading, accessibility functions or idea generation, but disciplinary reasoning must be theirs. | List allowed purposes and prohibited actions. State whether generated text, code, images or translations may appear in the submission. | Final work plus a short disclosure and any required verification. |
| Planning or design support | The assessed product is independent, while planning, outlining or alternative approaches are useful practice. | Define the permitted planning stage and require students to make and justify their own final decisions. | Plan, revised artefact and explanation of changes. |
| AI permitted with attribution | Students can use AI as one tool, but the learning outcome remains disciplinary judgment rather than AI operation. | Require fact, source, calculation and media checks; give an acknowledgment format and any data-privacy limits. | Submission, disclosure and targeted evidence of verification. |
| AI as a taught outcome | Responsible tool selection, prompting, evaluation or workflow is explicitly part of what students must learn. | Name approved tools, access arrangements, prohibited data, required iterations and evaluation criteria. | Prompt or interaction record where pedagogically useful, output critique, and a defensible final product. |
If AI is forbidden because it would defeat the outcome, explain that connection rather than implying that every use is inherently dishonest. DAMU recommends communicating proportionate limits in advance and considering assistive uses for students with specific needs. Simon Fraser University advises reviewing the pedagogy or moving an assessment into class when a prohibited use would otherwise compromise the course goal.
How do I write rules students can actually follow?
Put the rule in the assignment brief, not only in a general syllabus policy. Distinguish stages, output types and tools; “use AI responsibly” is not an operational instruction. Tell students what they may do, what they may not do, what they must disclose and what remains their responsibility.
Specify the permitted stage and purpose
Examples include “You may use a language model to generate three possible research questions before drafting,” “You may use grammar assistance after completing your own draft,” or “You must use the approved image generator to produce one concept and then critique its biases.” A permission for brainstorming does not automatically permit generated prose in the submitted answer.
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Name restricted work and protected data
State whether students may submit generated text, code, images, translations or citations. Prohibit entering confidential client, patient, unpublished research or personally identifying information into a service unless your institution has approved that use. Identify any tool, account or model that is required or disallowed by local policy.
Give a short acknowledgment pattern
Students should not have to infer what “cite AI” means. Provide a course-specific example and tell them where to place it. A concise note can identify the tool, date or version when available, the purpose, and the student’s checking and revision. For example:
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State the accountability sentence
Regardless of permission, the student is responsible for accuracy, attribution, privacy, originality and meeting the rubric. A tool’s confident wording does not transfer responsibility to the instructor or provider.
What evidence should an AI-permitted assignment collect?
Collect evidence that matches the outcome. A process note, verification record or oral explanation is useful when it reveals the targeted reasoning; it is not useful merely because AI exists.
Require verification where errors matter
Tell students what to check: sources and quotations in a history paper, calculations and units in engineering, code execution and tests in computing, image rights and representation in media work, or clinical guidance against authoritative protocols. Ask them to identify at least one limitation or uncertainty when the outcome includes evaluation. Do not require students to pretend that a generated citation is valid; they should replace it with a source they can locate and read.
Use staged submissions
A sequence such as proposal, annotated evidence, draft, feedback response and final version shows how the work develops. Each stage should have a purpose and a manageable grade or completion check. If AI is allowed only at one stage, label that boundary on the schedule and require students to carry forward their own decisions, not a model’s unexplained output.
Add an oral or practical explanation when necessary
A brief viva, demonstration, code walk-through or in-class application can test whether the student understands consequential choices. DAMU and SFU identify supervision, disclosure and oral or practical defense as alternatives to relying on automated detection. Keep the format proportionate: a five-minute explanation may be enough for a small design decision, while a clinical or laboratory outcome may require a supervised performance.
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Use reflection to expose judgment, not to demand confession
Prompts such as “Which suggestion did you reject and why?” or “What evidence changed your answer?” elicit disciplinary reasoning. Avoid asking students to prove every interaction with a tool when that record does not inform the learning outcome.
How should the rubric treat AI use and acknowledgment?
Separate the quality of disciplinary work from the mere fact that a student used AI. If AI is optional and not an outcome, grade the submission against the same learning criteria whether or not the student used a tool. TEQSA states this principle plainly: “Where AI is not assessed, grade the output only; ignore whether AI was used.”
If responsible AI use is an explicit, taught outcome, add criteria for that skill rather than quietly penalising or rewarding tool use. A rubric might look like this:
| Criterion | What proficient work shows | Possible evidence |
|---|---|---|
| Disciplinary quality | Accurate concepts, sound method, relevant evidence and a conclusion that follows from the analysis. | Final product and supporting artefacts. |
| Tool choice and purpose | A tool is selected for a defensible reason, with attention to capability, limitations and data sensitivity. | Brief rationale in the process note. |
| Prompting or workflow | Instructions are specific enough to support the task, and the student iterates rather than accepting the first output. | Selected prompts or a concise iteration summary, only when this is an outcome. |
| Evaluation and verification | Claims, sources, calculations, code or media are checked; errors and uncertainty are addressed. | Annotated checks, tests, source trail or critique. |
| Disclosure | The acknowledgment is included in the requested location and format. | AI-use note. |
TEQSA’s assessment principles allow acknowledgment to be assessed for inclusion and style while keeping the content of the acknowledgment from determining the mark. In practice, a missing note can be handled under a transparent submission requirement, while the quality of the paper is judged on its learning criteria. If a student discloses an allowed use, do not deduct marks simply because the tool was used.
How can I make the design fair and accessible?
Access is part of assessment validity. If AI use is optional, provide a suitable non-AI pathway that can meet the same criteria. The alternative might be a library database, a human feedback workshop, a structured planning worksheet, or an equivalent manual method; it must not quietly require more time, money or specialist skill.
If AI use is required, confirm that every student can access the approved tool under the same practical conditions. Explain account requirements, availability by region, privacy restrictions, rate limits and what to do during an outage. Provide instruction before grading begins, and offer an approved substitute if a student cannot use the service because of disability, data protection, cost or a documented accommodation.
Coordinate expectations with colleagues where possible. Students taking several courses may otherwise receive conflicting rules about the same tool, disclosure format or data handling. Your assignment must follow the current university, department and course policy; institutional rules can differ even when the educational rationale is similar.
When should responsible AI use itself be an outcome?
Make it an assessed outcome only when graduates need that capability in the discipline or profession. TEQSA’s principle is that AI may be recommended in any task but should be required only where AI use is a learning outcome. “Use a chatbot” is not enough: students should learn to choose an appropriate tool, formulate a useful request, test the result, recognise failure and communicate their decisions.
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Design a teachable AI outcome
Use an observable verb and a context, for example: “Evaluate a generated literature summary for unsupported claims and revise it using primary sources,” or “Compare two coding assistants on test coverage and explain which output is safer for the specified application.” Teach the relevant concepts—bias, hallucinated sources, privacy, intellectual property, accessibility and professional accountability—before grading them.
Provide approved tools and a non-negotiable safety boundary
List the tools students may use, what data must never be entered, and how to obtain help. If the tool changes during the term, explain whether students should switch versions or document the change. A required AI workflow should not depend on an unannounced free trial, a personal payment method or a feature unavailable to part of the class.
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How should instructors handle AI detectors?
Do not make a detector score the sole basis for a grade or misconduct finding. DAMU’s 2026 decree states: “Teachers should not base the assessment of students’ work solely on ‘GenAI detectors’, since their reliability is limited.” A detector may produce false positives, miss generated text or behave differently across languages and disciplines.
If a submission raises a legitimate question, use the process evidence already tied to the outcome: ask the student to explain a source, reproduce a calculation, walk through code, or discuss a design decision. Follow your institution’s academic-integrity procedure, give the student a chance to respond, and avoid treating stylistic variation as proof. The strongest prevention is a valid task with clear rules and evidence of learning, not a hidden detection contest.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the common design failures and their fixes?
| Failure | Why it causes trouble | Better design |
|---|---|---|
| One vague “AI allowed” or “AI banned” sentence for the whole course | Different assignments measure different outcomes, so students cannot tell which work is independent. | State a permission category, stage and purpose in every brief. |
| Prohibiting AI without changing a take-home task | The rule may be impossible to enforce and may not produce evidence of unaided learning. | Use supervised work, a practical defense or another direct demonstration when unaided performance matters. |
| Grading disclosure as if tool use were a quality defect | Students hide allowed use, and the rubric no longer measures the intended outcome. | Assess acknowledgment for required format; assess AI skill only when it is an outcome. |
| Requiring a complete prompt transcript | The record becomes onerous and may reveal little about disciplinary learning. | Request selected prompts or a short rationale only when they support the outcome. |
| Trusting generated citations or facts | Models can invent sources, misquote material or produce plausible errors. | Require students to locate, read and verify sources, calculations and other consequential outputs. |
| Assuming all students have identical access | Account, cost, privacy, disability or regional barriers create unequal conditions. | Offer an equivalent route, approved accommodations and clear support or outage procedures. |
| Using a detector as the assessment method | Limited reliability can produce unfair decisions and distract from the learning goal. | Use drafts, explanation, supervised performance and established academic-integrity processes. |
How do I review the assignment after students use it?
Review the task each term and after a significant change in tool capability or institutional policy. Ask four practical questions:
- Did the task still measure the stated outcome? Compare the submitted evidence with the skill you intended to assess.
- Did students understand the permission boundary? Note recurring questions, inconsistent disclosures and places where the brief was ambiguous.
- Was the evidence sufficient but proportionate? Remove process requirements that did not help you judge learning; add a targeted explanation or check where important reasoning remained invisible.
- Were access and policy conditions workable? Record tool outages, privacy concerns, accommodation requests and differences between sections, then revise the next brief.
Keep a short version history of the assignment and rubric so students can see what changed. When policy changes mid-course, communicate the new rule, its effective date and how already completed work will be treated. DAMU and TEQSA both emphasize explicit criteria and meaningful evidence rather than unsupported inferences about how text was produced.
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Before publishing, check that a student can answer these questions without guessing:
- What learning outcome is being assessed?
- Which stages and output types may use AI, and for what purpose?
- Which work must be completed independently?
- What information may not be entered into a tool?
- Which tools, versions or institutional accounts are approved, if any?
- What must be verified, tested or cited?
- What acknowledgment format and location are required?
- Is an equivalent non-AI pathway available, or is AI a required outcome?
- What process evidence, demonstration or reflection will be graded?
- Where can students get help or request an accommodation?
You can adapt the following core language to your local policy:
AI use for this assignment: You may use generative AI for [permitted stage or purpose]. You may not use it for [restricted stage or purpose]. You are responsible for checking any output you use and for meeting the assignment criteria. Include a short note describing whether and how you used AI, following the example provided. You may complete the assignment without AI and meet the same learning criteria.Remove the final sentence when AI use is a required learning outcome. In that case, replace it with the approved-tool, access and support requirements. Do not let sample wording override your campus or course policy.
How do different assignment approaches compare?
No single approach is best for every discipline. Compare options against the same five questions: Is AI prohibited, limited, optional or required? How directly does the task measure the outcome? Can a student meet the criteria without AI? What process evidence is necessary? Is responsible AI use itself taught and assessed?
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|---|---|---|---|
| Supervised, no-AI demonstration | High for unaided performance | Low | Valid in-class or practical conditions and accommodations. |
| Take-home task with limited assistance | High when boundaries and verification are explicit | Moderate | Stage-specific rules and a concise disclosure. |
| AI-optional project | High if criteria are tool-neutral | High | Equivalent non-AI route and identical learning criteria. |
| AI-integrated critique | High for evaluation and judgment | Usually defined by the task | Approved tool, output critique and safety instruction. |
| AI-required professional simulation | High for responsible workflow when properly taught | Limited | Reliable access, tool literacy, privacy controls and assessment of decisions rather than novelty. |
The best design makes the intended learning visible, tells students exactly how AI fits, and gives you fair evidence to grade. Start with the outcome, align permission and rubric to it, and revise the task when the evidence no longer supports the claim you want to make about student learning.
Frequently Asked Questions
Can I allow AI for brainstorming but prohibit it in the final draft?
Yes, if the brief defines brainstorming, the point at which independent work begins, and whether any generated wording may be carried forward. Ask students to disclose the brainstorming use and assess the final draft on the stated disciplinary criteria.
What should I do when a student says an AI tool produced a wrong citation?
Treat the citation as an unverified output, not as evidence that the student intended to mislead. Require the student to locate the source, correct the reference and explain the verification process under your normal academic-integrity and revision rules.
How can I accommodate a student who cannot use the approved AI service?
Offer a method that reaches the same learning outcome without imposing a penalty, such as a library-based workflow, manual process or supervised alternative. Record the accommodation and ensure the substitute is judged with the same outcome criteria.
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Only when interaction analysis is an explicit learning outcome and the requested record is proportionate. Otherwise, selected prompts, a short process note or an explanation of key revisions usually provides more useful evidence with less privacy and workload risk.
What if university policy changes after students have started the assignment?
State the effective date, explain which version applies to work already completed, and avoid retroactive penalties for conduct that was permitted when it occurred. Give students a written update and a route to ask questions or seek an accommodation.
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