Design the course from its intended learning outcomes, then decide—outcome by outcome—what students must do independently, where generative AI can support learning, and what ethical judgment they must exercise. Build psychological safety in parallel by making expectations visible, treating mistakes as part of learning, providing reliable ways to ask for help, and structuring participation so that challenge remains high without humiliation. No checklist guarantees safety or learning gains; the design has to be tested and revised with students.
Start with outcomes, not with an AI rule
A blanket “AI allowed” or “AI banned” statement cannot express every course’s learning purpose. The University of Alberta’s Centre for Teaching and Learning recommends backward design: identify the intended learning outcomes first, then align activities and assessments and decide where generative AI belongs.
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For each outcome, write four decisions before choosing a tool or drafting an assignment:
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- Useful assistance: Could AI help a student practise, generate alternatives, translate, simulate a situation, or receive formative feedback?
- AI literacy: Must students evaluate outputs, improve prompts, verify sources, edit language, or explain what they contributed?
- Professional judgment: What disciplinary, ethical, or civic decision should remain the student’s responsibility?
This separation prevents a polished generated product from being mistaken for evidence that the student learned the target skill. It also gives students a reason for every permission or restriction.
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An outcome-to-AI planning table
| Learning outcome | Independent evidence | Potential AI role | Judgment students must show |
|---|---|---|---|
| Construct and defend an argument | A reasoned claim supported by appropriate evidence | Generate counterarguments or questions for critique | Detect weak reasoning, verify evidence, and explain revisions |
| Perform a technical procedure | A demonstration, worked solution, or oral explanation | Offer hints after the student attempts a step | Check whether the suggested method is valid and safe |
| Communicate for a defined audience | A purposeful draft and explanation of choices | Produce variants for comparison or accessibility support | Choose, edit, and justify language, tone, and structure |
| Use a professional or disciplinary process | A traceable process record and final decision | Role-play a client, patient, stakeholder, or reviewer | Protect confidentiality, recognize limits, and take responsibility for the decision |
The University of Alberta gives related examples: students can critique generated work for errors, improve prompts, edit an output, and explain the value they added. In each case, the assessable work is the student’s evaluation and reasoning, not fluency alone.
Define psychological safety without lowering standards
Psychological safety is a learning condition in which students can ask questions, acknowledge uncertainty, make mistakes, take intellectual risks, and seek support without humiliation. Newcastle University’s teaching guidance, quoting the Advance HE Education for Mental Health Toolkit, describes a psychologically safe classroom as one where students “feel safe to make mistakes, take risks to further their learning and thinking and ask for help and support when needed.”
Safety is not constant comfort, agreement, or freedom from difficult feedback. A course can require demanding work, challenge a claim, or mark an answer incorrect while still protecting the student’s dignity and opportunity to improve. Avoid promising that a set of norms will make every student feel safe; students’ identities, prior experiences, status, and the stakes of an activity all matter.
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Turn the definition into observable teaching behaviours
- Respond to an error by identifying the reasoning that can be examined, not by labelling the person.
- Invite questions in more than one channel, such as anonymous submissions, office hours, discussion boards, and private messages.
- Explain what high-quality work looks like and where students can get help before a high-stakes submission.
- Separate critique of an idea, draft, or method from judgments about a student’s intelligence or character.
- Keep challenge and grading criteria stable while allowing students more than one way to participate.
Make AI expectations visible before students begin
Put the policy in the syllabus and beside every assignment where it matters. Students should not have to infer the rules from a detector, a warning after submission, or a different instructor’s practice. TEQSA guidance for Australian higher education places decisions at the provider, discipline, course, unit, or assessment level, while recommending consistent institutional messaging and ongoing monitoring.
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Use a permission scale that matches the task
| Category | What students may do | What they must disclose | Instructor note |
|---|---|---|---|
| Not used | Complete the task without generative AI | State that no AI was used if your process requires that declaration | Provide a direct opportunity to demonstrate the skill independently |
| Support use | Use specified functions such as brainstorming, language support, or practice questions | Tool, purpose, and substantial edits or decisions | Clarify whether entering course, personal, or confidential data is prohibited |
| Object of study | Generate, test, compare, or revise output as part of the learning activity | Prompts or a concise interaction record plus verification and reflection | Assess critique and judgment, not the tool’s apparent polish |
| Expected use | Use an approved tool to complete a defined professional or disciplinary workflow | Required declaration, sources checked, and human decisions retained | Offer an equivalent non-tool route when access or accommodation requires it |
Write instructions in plain language. Define “AI assistance” for this course, list permitted and restricted uses, explain what to acknowledge, and show a short example. TEQSA’s assessment guidance supports clear, manageable acknowledgement expectations rather than an unreadable legal code. If local policy uses different labels, adopt those labels consistently.
A concise assignment statement
An assignment brief can say: “You may use a generative AI tool to generate counterarguments and identify unclear sentences. You may not submit generated text as your own analysis. In a note of up to 150 words, name the tool, describe how you used it, identify two outputs you rejected or corrected, and explain how you verified factual claims. Do not enter personal, confidential, or unpublished information.” Adjust the allowance, word limit, and data rules to the outcome and your institution’s policy.
Design participation so students can take risks
Harvard Graduate School of Education guidance emphasizes shared purpose, recognition of contributions, inclusive language, and discussion norms that are maintained throughout a course rather than announced once. Establish expectations with students, revisit them after difficult discussions, and explain how they apply online, in small groups, and in office hours.
Build a predictable participation ladder
- Prepare privately: Give students a short prompt, reading annotation, or question before discussion so they can think without an audience.
- Start with low-stakes contribution: Use a poll, anonymous question board, or one-minute written response.
- Move to pairs or triads: Ask groups to compare reasoning before inviting whole-class responses.
- Invite, do not compel exposure: Offer several ways to share—speaking, chat, a shared document, or a follow-up post—and explain when a spoken demonstration is an essential outcome.
- Close the loop: Identify useful ideas, correct errors without ridicule, and state what will happen next.
Participation choice does not mean every activity can be optional. If oral communication is an outcome, assess an oral performance while providing rehearsal and feedback. If it is not an outcome, do not use public speaking as an unannounced gatekeeper.
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Use norms that support disagreement
- Critique claims, evidence, and methods; do not speculate about a person’s motives or ability.
- Ask for clarification before rebutting an unfamiliar point.
- Make room for voices that have not spoken and avoid treating speed as expertise.
- Credit a contribution when building on it, including ideas posted in a shared document.
- Explain how to raise a concern privately and how the instructor will respond.
Structure collaboration and help-seeking
Unstructured group work can increase social risk: a student may be unsure whether to admit confusion, challenge a confident peer, or disclose AI use. Give each activity a purpose, roles, and an individual accountability point.
Roles that make risk-taking safer
- Facilitator: keeps the group on the question and invites quieter members.
- Evidence checker: tests claims, citations, calculations, or generated suggestions.
- Recorder: captures decisions and unresolved questions.
- Reporter: presents the group’s reasoning, including uncertainty.
Rotate roles so that authority and visibility do not settle on one student. Require a brief individual reflection or contribution log when it measures the outcome; do not add surveillance merely to prove that a group met.
Provide routes to ask for help
Publish response times and escalation routes for routine questions, technical problems, accessibility needs, and concerns about an AI instruction. A private form can help students who will not speak in class. When a student reports a mistake or misuse, begin with fact-finding and a path to repair learning; disciplinary procedures still apply where required, but public shaming is not a teaching method.
Align assessment with the role AI is meant to play
Assessment should make the intended learning visible. If independent performance is the outcome, state the boundary and give students a supervised or otherwise direct opportunity to demonstrate it. If evaluating AI is part of the outcome, assess verification, revision, and explanation.
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Assess an AI-enabled process
- Ask students to submit an initial attempt or plan before consulting a tool.
- Require them to identify the tool and the task for which it was used.
- Have them verify important claims, calculations, quotations, or code against appropriate sources or tests.
- Ask for two or more substantive revisions, with reasons for accepting or rejecting suggestions.
- Include a short reflection on what the student contributed and what the tool could not establish.
Use process evidence only when it measures the outcome. A prompt transcript that adds no insight can become surveillance, create privacy risks, and disadvantage students who use assistive technology. A concise decision log, annotated draft, oral explanation, or staged submission may be better evidence.
Assess independent performance when it matters
For an outcome such as solving a problem, conducting an analysis, or explaining a concept, reserve a clear portion of the course for work completed without generative AI. Tell students the conditions in advance, provide practice under those conditions, and grade the demonstrated skill rather than trying to infer authorship from writing style. The available guidance does not establish that AI detectors reliably prove misconduct; do not present them as definitive evidence.
Use a transparent rubric
| Criterion | Questions for the assessor |
|---|---|
| Outcome mastery | Did the student demonstrate the specified knowledge or skill? |
| Verification and accuracy | Were important claims, sources, calculations, or outputs checked appropriately? |
| Reasoning and contribution | Can the student explain choices, revisions, and limitations? |
| Responsible practice | Did the student follow disclosure, privacy, accessibility, and local policy requirements? |
Handle access, privacy, and equity before requiring a tool
If students are expected to use generative AI for teaching or assessment, Uppsala University’s published guidance says coordinators should ensure the tools are available at no cost to students. Treat that as Uppsala’s institutional guidance, not a universal legal rule; check your own institution and jurisdiction.
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Before naming a service, check:
- Whether every student can create and maintain an account, including students under age or outside the service’s supported regions.
- Whether a free tier is sufficient for the assignment throughout the teaching period, rather than only on the day you test it.
- What happens to prompts, uploaded files, chat history, and account data, and whether students can opt out of retention or training uses.
- Whether institutional rules restrict personal data, health information, unpublished research, copyrighted materials, or assessment content.
- Whether an equivalent non-AI route is available for students who cannot or should not use the tool.
The University of Alberta points instructors to separate data-safety guidance. The sources above do not resolve every institution’s procurement or privacy requirement, so follow your institution’s current rules and obtain the appropriate review before assigning a service.
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Compare possible course designs deliberately
When two policies or assessment formats appear plausible, compare them against the same criteria. The following framework synthesizes the cited teaching and assessment guidance; it is a decision aid, not a validated scoring instrument.
| Criterion | Diagnostic question |
|---|---|
| Outcome alignment | Does the AI role serve the stated learning outcome rather than convenience? |
| Demonstrable learning | Can students show the target skill, including any independent component? |
| Transparency | Could a student explain the rule and acknowledgement requirement after one reading? |
| Equitable access | Are cost, disability access, language, connectivity, and account barriers addressed? |
| Privacy and data risk | Is the information students enter appropriate for the service and policy? |
| Productive struggle | Does the design preserve the effort students need to build the skill? |
| Assessment feasibility | Can instructors apply the criteria consistently without disproportionate monitoring? |
Review the course as an ongoing governance process
Tools, provider terms, institutional rules, and student experience change. TEQSA recommends ongoing monitoring of measures related to AI and assessment. Build review into the course rather than waiting for a dispute.
Collect actionable feedback
- “Which AI instructions were unclear, and where did you look for clarification?”
- “When did you feel able—or unable—to ask a question or admit uncertainty?”
- “Did the participation options let you demonstrate the outcome?”
- “What access, privacy, or accommodation problem affected your work?”
- “Which assessment step helped you learn, and which added work without evidence of learning?”
Offer anonymous and private channels, and report what you changed or why a change was not possible. Review patterns by activity, not just overall satisfaction: a class may feel welcoming in discussion but unsafe during public critique or an AI-related allegation.
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- Every outcome has an explicit independent-performance decision and an AI-role decision.
- Each assignment states permitted, restricted, or expected use in readable language.
- Acknowledgement examples match the work students will actually submit.
- Students know where to ask questions and how quickly they can expect a response.
- Discussion norms cover disagreement, errors, attribution, and online spaces.
- Group activities have roles and an individual accountability point where appropriate.
- Assessment rewards verification, reasoning, and contribution—not generated fluency alone.
- Required tools meet local access, privacy, procurement, and accessibility requirements.
- An equivalent route exists when a student cannot use the selected tool.
- The course includes a feedback and revision point for climate, access, and AI expectations.
What this approach can—and cannot—establish
A qualitative study of 18 fourth-year medical students identified clear expectations, autonomy, relationships, and frequent feedback as student-reported contributors to psychological safety in clinical learning. The sample and setting are specific: the findings do not establish prevalence, causation, or identical effects in other disciplines, class sizes, or online formats. Use the themes as design questions, not guarantees.
Likewise, clear AI instructions can reduce avoidable ambiguity, but they cannot eliminate unequal access, conflicting institutional requirements, or every form of misuse. Responsible design is a cycle: align the task, explain the boundary, support participation, examine evidence of learning, protect data, and revise when the course experience shows a problem.
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