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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 judgment students must exercise when using it. At the same time, make interpersonal expectations explicit, give students structured ways to participate and seek help, and respond to mistakes without humiliation. Psychological safety does not mean removing disagreement, difficulty, or standards; it means making it possible to take productive learning risks while standards remain clear.
The practical sequence is: map outcomes to appropriate AI roles; publish readable rules before work begins; align activities and assessment with those decisions; design participation and collaboration deliberately; check access, privacy, and local policy; and review the course with student feedback and evidence from the work.
What psychological safety and responsible AI use mean in a course
Psychological safety is permission to learn visibly
In a psychologically safe classroom, students can ask a question, offer an unfinished idea, admit confusion, or make a mistake without being ridiculed or treated as incapable. Newcastle University’s teaching guidance, quoting the Advance HE Education for Mental Health Toolkit, describes it this way: “In a psychologically safe classroom, students feel safe to make mistakes, take risks to further their learning and thinking and ask for help and support when needed.”
That definition is about conditions for learning, not constant comfort. Students can encounter demanding material, receive critical feedback, disagree with one another, and revise work. Lowering the standard to prevent every uncomfortable moment would deprive students of the challenge they are there to master. Your design should make the route through that challenge predictable and respectful.
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Responsible AI use is contextual, not a blanket permission
Generative AI can be a subject of study, a tool for practice, a prohibited aid for a particular performance, or some combination of those roles. The appropriate choice depends on the learning outcome, discipline, assessment purpose, tool access, privacy obligations, and institutional policy. A single “AI allowed” or “AI banned” statement for an entire course usually hides those differences.
Responsible use also includes being able to question an output, verify claims, protect confidential information, disclose assistance when required, and explain the human contribution. Fluent text or code is not, by itself, evidence that a student achieved the intended outcome.
Start with outcomes and work backwards
The University of Alberta’s course-design guidance recommends beginning with intended learning outcomes and aligning assessment, activities, and AI decisions to them. Use one design record for every outcome rather than writing a generic policy first.
| Design question | What to decide | Evidence students can produce |
|---|---|---|
| What must the student do? | State the observable disciplinary skill, knowledge, or judgment. | A performance, explanation, calculation, design, critique, or demonstration that directly shows the outcome. |
| What must be independent? | Identify the part that students need to perform without generative AI or other assistance. | An in-class task, oral explanation, supervised demonstration, or independently produced stage of the work when that is genuinely relevant. |
| Where could AI support learning? | Choose a bounded role such as brainstorming, generating a counterexample, translating a draft for comparison, or producing an intentionally flawed model to critique. | Prompts, annotations, revisions, verification notes, or a comparison that shows what the student noticed and changed. |
| What AI literacy is part of the outcome? | Specify whether students must evaluate accuracy, bias, citation, privacy, limitations, or the suitability of a tool. | A source check, error analysis, rationale for accepting or rejecting output, or an explanation of human decisions. |
Keep the distinction between an AI-enabled task and proof of learning. If an outcome is “write a defensible policy memo,” a model may help generate alternatives, but students still need to select evidence, apply the relevant framework, and defend the recommendation. If the outcome is “identify weaknesses in generated arguments,” then the generated material becomes part of the object being assessed.
Decide where AI belongs in each learning activity
- List the outcome and its threshold. Write what a passing demonstration looks like without relying on a tool’s fluent output.
- Separate support from substitution. Mark tasks where AI may reduce unproductive friction and tasks where it would perform the very reasoning students must learn.
- Design a purposeful AI role. Ask students to critique an output for factual or logical errors, improve a prompt, compare two approaches, edit for a specified audience, or explain the value they added. These uses make judgment visible rather than treating the tool as an invisible author.
- Choose the evidence before choosing the tool. Decide what you will assess and how students will show it; only then identify whether a particular tool is necessary.
- Check the learning cost. Consider how much productive struggle remains. If a tool removes the practice needed for the outcome, restrict it for that stage and provide another low-stakes opportunity to use it for feedback or exploration.
Use the same reasoning for lectures, studios, laboratories, seminars, and placements. An AI exercise in a seminar can prepare students for a later independent analysis; it should not silently replace that analysis.
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Publish clear AI expectations before students start
Students should not have to infer the rules from an integrity investigation or from a single sentence buried in the syllabus. Put a short course-level statement in the syllabus, then repeat the relevant rule beside each assignment. TEQSA guidance emphasizes decisions at the provider, discipline, course, unit, or assessment level, with consistent messaging and ongoing monitoring. The provider and institution determine the applicable rule in context.
| Assignment setting | Example student-facing rule | What students disclose |
|---|---|---|
| AI permitted for preparation | You may use a generative tool to brainstorm or obtain feedback before drafting. The submitted argument, evidence selection, and wording must be your own. | Tool name, date, purpose, and a brief description of what you kept or changed if the assignment asks for it. |
| AI required as an object of analysis | Use the approved tool to produce one response, then verify it against course sources and annotate its errors, omissions, and revisions. | The prompt, relevant output excerpt, verification sources, and explanation of revisions. |
| AI restricted for independent performance | Do not use generative AI during this assessment. You may use the course’s stated accessibility accommodations and ask the instructor about permitted support. | Any assistance required by the course or institutional process, not an unnecessary private account of personal circumstances. |
| AI use not addressed | Ask before using a generative tool. Until you receive an answer, complete the work without it. | Follow the instructor’s written direction once provided. |
Explain what counts as AI assistance
Define the scope in ordinary language. Depending on your course, “generative AI” may include systems that produce text, code, images, audio, translations, summaries, or synthetic data. Say whether grammar correction, predictive text, reference managers, accessibility software, or discipline-specific analytic tools are treated differently. Do not require students to guess whether a tool’s marketing label changes the rule.
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Make acknowledgement manageable
TEQSA’s assessment guidance supports clear, proportionate acknowledgement expectations. A short form is often enough: tool, date, purpose, and the material affected. If a student must submit prompts or output, explain why it measures the outcome and how you will protect personal or confidential information. Avoid demanding extensive logs that do not help students learn or help you assess the stated outcome.
Align assessment with the purpose of the course
When AI use is part of the skill
Assess evaluation and revision, not merely polished output. For example, provide a generated explanation containing several errors, ask students to verify each claim against assigned sources, and grade the quality of the diagnosis and correction. In a programming course, students might test generated code, identify a security or logic defect, and justify a revised implementation. In a design course, they might compare generated alternatives with a human-authored brief and defend the final choice.
- Give students a clear task and a defined tool role.
- Require evidence of checking, such as citations, tests, calculations, or annotations appropriate to the discipline.
- Grade the reasoning behind acceptance, rejection, and revision.
- State what remains the student’s responsibility even when a tool is used.
When independent performance is the goal
Make the boundary explicit and provide a credible way to demonstrate the skill directly. A supervised exercise, oral explanation, studio demonstration, or staged draft can be appropriate when it measures the outcome rather than serving as surveillance. Tell students what resources are allowed, how questions will be handled, and what support is available before the assessment begins.
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Use process evidence only when it serves the outcome
Drafts, design decisions, source notes, version histories, reflections, and short conferences can reveal learning when the process itself matters. They should not be added solely to police tool use. Choose the smallest amount of process evidence that lets students demonstrate the intended skill and lets you assess it consistently.
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Use more than one kind of evidence when appropriate
A single take-home product can conceal which parts a student understands. Pairing it with a brief explanation, application to a new case, critique of a peer example, or in-class transfer task can test understanding without assuming that any one format is universally secure. Explain the connection between each component and the outcome so students see it as learning, not an unexpected obstacle.
Build psychological safety into participation
Co-create a small set of workable norms
At the start of the course, invite students to help define how discussion will work. Keep the list observable: critique ideas rather than people; make room for more than one voice; ask before assuming; explain disagreement with reasons; keep examples and personal disclosures appropriately confidential; and repair harm when a norm is missed. Harvard Graduate School of Education guidance emphasizes shared purpose, recognizing contributions, inclusive language, and revisiting discussion norms throughout the course. Treat the norms as a continuing practice, not a one-time slide.
Offer several routes into participation
Speaking first in a large room should not be the only evidence that a student is engaged. Combine individual writing, anonymous question submission, think–pair–share, small-group reporting, discussion boards, office hours, and short polls when those formats fit the outcome. Give students enough preparation time to formulate a response, and rotate roles so the same people are not always the spokesperson or note-taker.
Respond to errors without lowering the bar
Separate the quality of an idea from the worth of the person offering it. A useful response identifies what is promising, asks how the claim could be tested, and explains the correction. If an answer is unsafe, discriminatory, or seriously misleading, address the content directly and restore the discussion norm; avoiding correction would not be responsible teaching. Show that revision is expected by modeling your own correction when you discover an error.
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Make help predictable
Publish when and how students can ask for clarification, request an extension under the stated process, report a participation concern, or discuss an AI rule privately. Provide an anonymous route for questions that may feel embarrassing. Return feedback on a schedule students can understand, and give them a way to act on it. A route to help is useful only if students know what response time and next step to expect.
Structure collaboration so risk is shared fairly
Unstructured group work can make the confident student more visible while leaving uncertainty and labor hidden. Assign a bounded purpose, roles that rotate, a time limit, and an individual accountability point. Roles might include facilitator, evidence checker, devil’s advocate, recorder, and reporter; adapt them to the discipline rather than treating the list as a formula.
- Give groups a common prompt and a private way to flag a concern.
- Require each member to contribute an identifiable analysis, question, or revision.
- Teach students how to challenge an answer with evidence rather than status.
- Debrief both the content and the collaboration: what helped people speak, what blocked them, and what the group will change next time.
When AI is used in a group, record who decides the prompt, who verifies output, and who makes the final disciplinary judgment. Shared tool access should not erase individual responsibility.
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Access and cost
Uppsala University’s published guidance says that when students are expected to use generative AI for teaching or assessment, coordinators should ensure the tools are available at no cost to students. This is institutional guidance, not a universal legal rule, so check your own institution’s current requirements. Offer a non-tool route when access, disability, account creation, regional availability, or connectivity would otherwise prevent a student from meeting the outcome.
Privacy and data handling
Tell students what must not be entered into an external service: personal data, identifiable peer work, confidential placement information, unpublished research, client material, or assessment content covered by an institutional restriction. The University of Alberta points instructors toward separate data-safety guidance, but the sources available here do not settle every institution’s procurement or privacy requirement. Follow your institution’s current rules and approved-tool list before directing students to a service.
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Consistency and change
Coordinate wording across modules where students may encounter the same tool under different rules. Explain the course-specific difference rather than presenting contradictory instructions as a student failure. Tools and institutional policies change; record the date of your policy, review it before the next offering, and tell students promptly when a material change affects an active assessment.
Compare possible designs with a transparent decision framework
When two approaches seem plausible, compare them against the same questions. The criteria below are a practical synthesis of course-alignment, assessment-transparency, and access guidance; they are not a validated scoring instrument.
| Criterion | Question to ask | Warning sign |
|---|---|---|
| Outcome alignment | Does the design directly practice and measure the stated outcome? | The tool is present because it is fashionable, not because it serves the skill. |
| Demonstrability | Can each student show the target skill, including the human judgment behind any AI-supported work? | A fluent product is accepted without explanation or verification. |
| Transparency | Could a student summarize the rule and acknowledgement requirement after reading the assignment? | Key conditions are scattered across links, slides, or implied conventions. |
| Equity of access | Can every student use the required resource without an unreasonable cost, account, or connectivity barrier? | Students must purchase a service or disclose private information to participate. |
| Privacy and data risk | Are the inputs, outputs, retention, and institutional permissions acceptable? | Students are encouraged to upload identifiable or confidential material. |
| Productive struggle | Does the design preserve the practice needed to build the outcome? | The tool completes the reasoning students are supposed to learn. |
| Assessment feasibility | Can instructors apply the criteria consistently within available time? | The design demands exhaustive logs that cannot be read or interpreted reliably. |
A practical implementation sequence
- Before the course opens: map every outcome to independent performance, possible AI support, required AI literacy, and evidence. Check institutional rules, approved tools, privacy constraints, and access costs.
- Before each assignment: write the permitted, restricted, or required use in the assignment itself. Define acknowledgement, allowed resources, the assessment criteria, and where students can ask questions.
- In the first weeks: establish discussion norms with students, model respectful correction, practice a low-stakes participation routine, and demonstrate the difference between using an output and verifying it.
- During learning activities: use structured roles, rotate participation formats, and provide predictable help routes. Watch for silence, repeated exclusion, or confusion about the AI rule as signals to clarify the design rather than as proof of student disengagement.
- During assessment: collect only the process evidence that serves the outcome. Apply the published criteria consistently and give students a route to explain an unusual tool or access circumstance.
- After a task: ask whether the rule was understandable, whether students could participate, whether the tool was accessible, and whether the assessment measured the intended skill. Review responses and work samples before the next offering.
How to review whether the design is working
Do not treat a checklist as proof that a class is psychologically safe or that learning improved. Look for converging evidence: student comments about whether questions felt possible; patterns in who participates and who receives feedback; the quality of error analysis and independent demonstrations; requests for clarification about AI rules; and access or privacy problems reported through the course process.
A short mid-course pulse check can ask: Which participation route helps you contribute? Where is it unclear when AI is permitted? What prevents you from asking for help? What change would make the next activity more useful? Share what you will change and what cannot change, with the reason. TEQSA recommends ongoing monitoring of measures around AI and assessment, so review is part of course governance rather than a one-time launch task.
Interpret evidence cautiously. 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. Its sample and clinical context do not establish a causal effect or a result that automatically transfers to every discipline, cohort, or delivery mode. Use such findings to generate design questions, then examine your own students’ experience and learning evidence.
What a well-designed course makes visible
Students should be able to answer four questions without guessing: What am I expected to learn? Which parts must I be able to do myself? Where may or must AI assist, and how do I verify and acknowledge it? How can I participate or ask for help when I am uncertain? Instructors should be able to answer a fifth: What evidence will show that the intended learning occurred?
When those answers are aligned, psychological safety and responsible AI use reinforce one another. Clear boundaries reduce fear of accidental rule-breaking; structured participation makes questions and corrections ordinary; and assessments that value verification, explanation, and revision keep human judgment at the center. The result is not a risk-free course or a universal AI policy, but a course in which challenge, accountability, access, and respectful learning risks are designed together.
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