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Design the assignment around the learning outcome first, then decide what role generative AI can play. Specify which work students must do independently, which assistance is allowed, what they must disclose, and what evidence will let you judge their learning. A strong brief aligns those permissions with the rubric, gives students a workable non-AI route when AI is optional, and assesses AI skills only when responsible AI use is itself an outcome. This approach is more defensible than trying to make every task “AI-proof”: Michigan Technological University’s teaching guidance says the goal is to require the thinking and practice named in the objectives.
What should an assignment measure before you decide its AI rules?
Write the intended learning outcome in observable terms before choosing a permission level. Then identify the evidence that would demonstrate it. For example, “analyze competing explanations using primary sources” requires visible source selection, interpretation, and argument; “produce a polished public-facing summary” may allow AI-assisted editing if the student still demonstrates accurate judgment and audience awareness.
Ask three questions:
- What must students know or be able to do? Name the disciplinary knowledge, procedure, judgment, or performance you intend to teach.
- Which steps are the learning? Separate work that exercises the outcome from routine formatting, translation, transcription, or accessibility support.
- What could a student submit that would prove the outcome? Decide whether the final product is enough or whether drafts, calculations, source checks, code execution, a design rationale, or an oral explanation are necessary.
Michigan Technological University recommends reviewing whether an existing task still produces meaningful learning before changing it merely because AI is available. If a model can complete the graded portion while the course never asks students to demonstrate the underlying reasoning, revise the outcome, the evidence, or both. Do not add a surveillance requirement simply to compensate for an outcome that was never made explicit.
Which AI permission model matches the learning outcome?
Use an assignment-level category rather than one vague sentence for an entire course. UNSW’s taxonomy and TEQSA guidance support a range from no AI use to intentionally required use. State the category, the stages it covers, and the evidence students must provide.
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| Model | Use it when | Tell students | Evidence to collect |
|---|---|---|---|
| No AI | You need an independent demonstration of unaided recall, reasoning, calculation, or performance. | Name the prohibited tools and the exact work that must be completed without them. Explain the learning reason, not just a prohibition. | In-class work, supervised performance, handwritten or locally executed calculations, or another proportionate record of independent work. |
| Limited assistance | AI may help with a bounded support task while the disciplinary reasoning remains the student’s. | List allowed functions, such as spelling feedback or accessibility support, and prohibited functions, such as generating claims or solving the assessed problem. | The submitted work plus a short statement confirming the tool and function used when required. |
| Planning or design support | You want students to explore ideas, alternatives, or structures before producing their own final work. | Define the planning stage, require independent checking, and state whether generated text or code may appear in the final submission. | Plan or prompt excerpt, explanation of adopted or rejected suggestions, and the student’s final artifact. |
| Permitted with attribution | AI assistance is compatible with the outcome, but students remain responsible for accuracy, sources, and decisions. | Say where AI may be used, how to acknowledge it, and what verification is mandatory. | Final work, verification notes, and the specified acknowledgment or process note. |
| Required AI use | Responsible tool selection, prompting, output evaluation, or human–AI collaboration is an explicit learning outcome. | Name approved tools, access arrangements, privacy limits, required activities, and the criteria for judging AI use. | Prompts or interaction record when relevant, output evaluation, revisions, and a rationale for consequential choices. |
TEQSA’s principle is concise: AI may be recommended for a task, but it should be required only when AI use is a learning outcome. When AI is optional and not assessed, grade the work against the same learning criteria regardless of whether a student used it.
How do you write assignment-specific AI rules students can follow?
Put the rules in the assignment brief, not only in a general syllabus statement. Students should be able to tell what is allowed at each stage without interpreting a broad phrase such as “use AI responsibly.”
Define the stages
Divide the workflow into stages such as topic selection, research, outlining, drafting, coding, editing, and submission. Mark each stage as prohibited, allowed with limits, or required. A student might be allowed to ask for counterarguments during planning but required to write the final analysis and verify every cited source independently.
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Describe the activity that matters: generating text, translating, debugging, summarizing a supplied reading, producing images, checking grammar, or suggesting test cases. This keeps the rule understandable when students use a different tool and lets you address accessibility software without implying that one product is the only permitted option.
State responsibility and boundaries
Tell students that they remain responsible for factual accuracy, calculations, citations, confidentiality, copyright compliance, and the final submission. Prohibit entering confidential client, patient, or research-participant information unless the applicable policy explicitly permits it. If a tool stores prompts or uses submissions for training, direct students to the institution’s privacy guidance rather than asking them to guess.
Explain independent work
For a no-AI or limited-use task, identify the reasoning, performance, or artifact that must be the student’s own. DAMU recommends proportionate rules communicated in advance and notes that assistive uses may need separate consideration for students with specific needs. Coordinate any accommodation with the institution’s established process.
What evidence of learning should an AI-permitted assignment collect?
Collect evidence that answers the learning question; do not demand an exhaustive audit trail by default. SFU emphasizes process and critical engagement, while DAMU identifies supervision, disclosure, and oral or practical defense as alternatives to relying on detection.
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Use staged submissions when the process is part of the outcome
A proposal, annotated source set, calculation sheet, code commit, storyboard, or draft can show how the student moved from a problem to a defensible result. Require only the stages that reveal the targeted skill. A short checkpoint may be more informative than a complete transcript of every interaction with a tool.
Require verification where errors matter
Match checks to the discipline: confirm quotations against the original text, rerun generated calculations, execute and test code, validate a translation with a qualified reference, or check generated images for factual and attribution problems. Ask students to flag a consequential error and explain how they corrected it; that demonstrates evaluation rather than mere tool use.
Add an explanation or defense when authorship of reasoning matters
A brief rationale, conference, viva, demonstration, or practical defense can show whether a student understands choices in the submitted work. Use this when the outcome includes judgment or performance that a polished artifact cannot reveal. Keep the format accessible and apply the same criteria to students who did not use AI.
Keep process requests proportionate
Do not require prompts, screen recordings, or private account data merely because a tool was available. Ask for a prompt excerpt or interaction summary only when it helps assess the stated outcome, responsible tool selection, or output evaluation. Tell students what to redact and how the material will be handled.
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How should the rubric and AI acknowledgment fit together?
Separate the quality of the disciplinary work from the fact that a student used AI. If AI use is optional and not an outcome, the choice to use it should not change the mark; TEQSA advises grading the output against the learning criteria and ignoring whether AI was used.
When responsible AI use is not an outcome
Assess accuracy, reasoning, evidence, method, communication, or performance as usual. Add an acknowledgment requirement for transparency, but do not award or deduct points simply because a student disclosed AI use. You may mark whether the required acknowledgment is present and follows the requested format, while its content does not inflate the academic score.
When responsible AI use is an outcome
Add explicit criteria such as:
- Tool choice: The student selects a tool appropriate to the task, audience, privacy constraints, and reliability needs.
- Instruction: Prompts or task specifications provide relevant context, constraints, and evaluation criteria.
- Verification: The student tests claims, sources, calculations, code, or media and identifies material limitations.
- Decision-making: The student explains which output was adopted, revised, or rejected and why.
- Disclosure: The student gives the required acknowledgment in the requested style.
Do not let a beautifully written acknowledgment compensate for weak disciplinary work. Conversely, do not penalize a student for choosing the non-AI pathway when the assignment makes AI optional.
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Give an acknowledgment model
Specify the minimum information: tool name and version if known, date or stage of use, purpose, and the student’s verification or revision. A brief process note is often enough. If students used no AI, say whether you want them to write “No generative AI used” or leave the section blank; remove that ambiguity from the brief.
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An AI policy is fair only when students can realistically follow it. TEQSA recommends a suitable non-AI pathway when AI is optional and practical access and support when AI is required.
If AI is optional
Design an equivalent route that can meet every criterion without a tool. Provide comparable time, materials, and feedback. Do not make the non-AI route a visibly inferior version, such as requiring a longer paper while AI users submit a shorter one for the same outcome.
If AI is required
Confirm account availability, device and network requirements, language support, rate limits, and costs before assigning the task. Identify an approved alternative if the named tool is unavailable, and provide a way to complete the work without exposing sensitive personal data. Explain where students can get technical and accessibility help.
Coordinate local policy
University, department, and course rules may differ. Check the current institutional policy, honor code, disability-access process, data-protection requirements, and any assessment-board guidance before publishing your brief. If policies change during a term, communicate the new rule in writing and avoid retroactively penalizing conduct that was permitted when the work was done.
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Use this sequence for a new task or a substantial revision.
- Define the outcome. Write what students will demonstrate and the observable evidence that would convince you they achieved it.
- Map the work. List the stages and mark where students must exercise knowledge, judgment, analysis, or practice. Identify routine support that does not compromise the outcome.
- Select the permission category. Choose no AI, limited assistance, planning support, permitted with attribution, or required AI. If different stages need different categories, say so.
- Design the evidence. Add drafts, verification, reflection, oral explanation, or practical demonstration only when each item answers an assessment question. Decide what students must retain and what they may redact.
- Align the rubric and disclosure. Grade disciplinary quality separately from responsible AI skills. Provide an acknowledgment example and state whether its presence or style is assessed.
- Check access and policy. Test the instructions from a student’s perspective, confirm tool access and privacy constraints, and provide an equivalent non-AI route when appropriate.
- Review after use. Ask whether students understood the boundaries, whether the evidence was sufficient, and whether the task still measured the intended outcome after tool capabilities or institutional rules changed.
What should an assignment brief say?
Adapt the following language to your course policy and the actual outcome:
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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.
Replace the bracketed phrases with concrete functions and stages. Do not include the non-AI sentence when AI use is a required learning outcome. In that case, name the required tool or acceptable alternatives, access arrangements, privacy limits, support, and the AI-specific criteria students will be assessed against. The sample does not override campus or course policy.
How do different assignment approaches compare?
No single permission rule suits every outcome. Compare an approach against the evidence it can produce, the student’s ability to meet the same criteria without AI, and whether AI competence is intentionally taught.
| Approach | Best fit | Main strength | Main risk to manage |
|---|---|---|---|
| Supervised, no-AI demonstration | Unaided recall, foundational calculation, oral language, or practical technique. | Direct evidence of independent performance. | It may measure speed, anxiety, or test conditions beyond the intended outcome; provide accommodations and justify the setting. |
| AI-optional product with common rubric | Analysis, design, communication, or problem solving where tool choice is not itself taught. | Students can choose a workflow while the criteria remain stable. | Access differences; provide an equivalent non-AI route and grade the output, not the choice. |
| AI-assisted process with verification | Tasks that benefit from critique, iteration, or comparison of alternatives. | Makes checking and judgment visible. | Students may submit unverified errors; require discipline-specific checks. |
| AI-integrated project | Courses teaching prompting, evaluation, human–AI collaboration, or responsible deployment. | Assesses a contemporary professional practice directly. | Students need tool access, privacy guidance, and a rubric that distinguishes AI skill from subject-matter quality. |
What might alignment look like in different disciplines?
History: source-based argument
Outcome: construct an evidence-based interpretation from primary sources. Permit AI for brainstorming questions, but require students to locate, read, and cite the sources themselves. Collect an annotated source set and a short note identifying any generated suggestion that was rejected because it was inaccurate or unsupported. Grade the historical reasoning and source use; do not grade whether brainstorming occurred.
Computer science: tested implementation
Outcome: design, implement, and debug a program that meets specified constraints. Permit an assistant for explanations or candidate code, but require students to write tests, run the program, explain key functions, and document any defects they found. An oral walkthrough or live modification can provide evidence that the student understands the implementation. If debugging skill is the outcome, include it explicitly in the rubric rather than treating tool use as an automatic violation.
Nursing or allied health: clinical judgment
Outcome: prioritize patient needs and justify a safe intervention using current evidence. If AI is allowed for organizing notes, prohibit entering identifiable patient information and require verification against approved clinical references. Assess the student’s risk reasoning, evidence selection, and explanation of uncertainty. A short case conference can reveal judgment that a polished written plan might conceal.
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How should you respond to suspected misuse or unreliable AI detectors?
Do not treat an automated detector score as proof that a student violated a rule. DAMU Decree Article 4(5) states that teachers should not base assessment solely on “GenAI detectors,” because their reliability is limited. SFU likewise recommends process evidence and critical engagement rather than detector-only decisions.
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If a submission raises a legitimate question, follow the published academic-integrity procedure: compare the work with the assignment’s required evidence, ask the student to explain consequential choices, and offer an oral or practical demonstration when appropriate. Apply the same process to students who used AI and those who did not. Keep the conversation about the stated learning outcome and the evidence available, not about an unsupported probability score.
How do you review the assignment after students use it?
Review the task each term and whenever tool capabilities or institutional policy materially change. Use student questions and grading patterns as design evidence.
- Could students tell which stages and functions were permitted?
- Did the evidence let you judge the intended outcome without an excessive audit burden?
- Were verification instructions specific enough for the discipline?
- Did optional AI use create an unequal route, or did both pathways meet the same criteria?
- Did the rubric distinguish disciplinary quality from responsible AI practice?
- Were privacy, accessibility, account, and cost constraints clear before students began?
- Did any policy or tool change require a revised brief for the next offering?
Meaningful evidence and explicit criteria are more durable than trying to infer how every sentence or line of code was produced. Revise the assignment when the evidence no longer answers the learning question, not simply when a new model is released.
Frequently Asked Questions
Can students use AI for grammar or accessibility support when the assignment otherwise bans it?
Possibly, but treat the support as a separate accessibility or editing rule rather than an unstated exception. Specify which functions are allowed, require the same substantive independent work, and direct students to the institution’s accommodation process when a documented need is involved.
Do I need to collect every prompt students entered?
No. Collect prompts or interaction summaries only when they are relevant evidence for an explicit outcome such as prompt design or output evaluation. Otherwise, a concise acknowledgment and the required disciplinary evidence are usually more proportionate; students should redact private or sensitive information.
What should I do if a student says an AI tool was unavailable on the deadline?
Provide a preannounced fallback, such as the equivalent non-AI pathway or an approved alternative tool, and apply the same learning criteria. If no fallback was published, resolve the case under the institution’s extension or technical-failure process rather than penalizing a student for an access problem.
How can I change the AI rule after an assignment has started?
Avoid retroactive changes. Announce the new rule in writing, explain which submissions it applies to, and preserve the original option for work already completed unless the institutional policy requires otherwise. For future offerings, revise the brief, rubric, and access guidance together.
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