Design the assignment around the learning outcome, then require a small, purposeful set of evidence showing how students reached their conclusions. A proposal, source evaluation, draft, feedback response, revision note, or short explanation can make reasoning visible without turning the course into a surveillance exercise.
State exactly what generative AI may do, what students must do themselves, and how they must disclose assistance. Grade disciplinary judgment, evidence, revision and explanation—not the sheer volume of documentation. No assignment is completely “AI-proof,” but a well-aligned process can keep the assessment meaningful when students have access to AI.
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Begin with the learning outcome
Write the outcome before choosing a format or thinking about how to stop AI use. The outcome should describe what students will know or be able to do after the assignment: evaluate competing evidence, apply a method, design a solution, interpret data, make a defensible argument, or communicate for a particular audience.
Ask what work would let you see that capability directly. If the goal is source evaluation, a polished essay alone may hide whether a student checked provenance and bias. If the goal is laboratory reasoning, a final graph does not show whether the student selected an appropriate model or noticed an anomalous result. The task and its evidence should therefore be built backward from the intended learning.
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Define the purpose in student language
Connect the assignment to a course goal and a plausible future use. A brief explanation such as “You are practicing how policy analysts turn incomplete evidence into a recommendation” tells students why the stages matter. Guidance from Boston University recommends making this connection explicit, while University of Michigan guidance emphasizes explaining the relationship between the task and the knowledge, skills and values being assessed.
Specify what counts as evidence
For each outcome, list the observable evidence you need. Examples include an annotated source set for evaluating evidence, a worked calculation for showing method, a design rationale for explaining choices, or an oral defense for responding to questions. Do not add a checkpoint merely because it makes unauthorized copying harder. Every submitted item should help students practice or help you judge the target capability.
Choose the assignment’s relationship with AI
Decide the role of AI at the design stage, not after students submit work. TU Dublin describes four useful approaches. A course can use different approaches for different assessments.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute| Approach | What it means | Best fit | Evidence to request |
|---|---|---|---|
| Robust | The task is designed for conditions in which AI is unavailable or not used. | Learning outcomes that require unaided recall, personal performance or a controlled demonstration. | Work completed in the defined setting, with an accessible equivalent when a control would disadvantage a student. |
| Resilient | The task remains valid even when students can access AI. | Research, analysis and problem-solving where judgment matters more than producing a generic answer. | Selected plans, source checks, drafts, feedback responses and a final explanation of consequential decisions. |
| Integrated | AI is permitted at defined stages and becomes part of the workflow. | Tasks that teach evaluation, editing, prompting, verification or responsible use. | The relevant outputs, verification notes, disclosure of use, and an account of what the student accepted, rejected or changed. |
| Enabled | AI makes a new kind of task possible and is itself part of the learning. | Critiquing model outputs, comparing generated alternatives or studying limitations of automated systems. | The AI output, independent checks, identified errors or omissions, and a justified improved version. |
TU Dublin’s guidance states: “Authenticity can be supported by the design of the task and assessment process, rather than primarily through surveillance or restriction.” The practical implication is not to ignore misconduct rules; it is to make the assessed thinking visible and relevant.
Write a student-facing brief that removes ambiguity
Put the rules on the assignment page and in the rubric. Students should not have to infer them from a general institutional statement.
- Task: State the question, deliverable, audience, format and length or time limit.
- Purpose: Name the course outcome and the capability being practiced.
- Success criteria: Describe what a strong solution demonstrates.
- AI boundary: Say whether AI is prohibited, limited to named stages, allowed with disclosure, or required.
- Student responsibility: Identify the reasoning, evidence checks, calculations, decisions and writing that must be the student’s own.
- Disclosure: Give a short format for naming tools, dates, purposes and material changes. Require students to indicate what they accepted, rejected, checked or rewrote.
- Evidence to submit: List only the checkpoints you will use for learning or grading.
- Support: Point to permitted tools, accessibility arrangements, citation guidance and a way to ask questions before the deadline.
Use plain examples. “AI may help you brainstorm three research questions, but you must select one, verify every source and write the analysis” is clearer than “Use AI responsibly.” If AI is prohibited for a particular stage, identify the stage and the reason rather than implying that every digital aid is forbidden.
Build a small, purposeful chain of process evidence
Process evidence should reveal decisions over time, not create paperwork for its own sake. Choose a few stages that correspond to the outcome and set deadlines early enough for feedback to change the work.
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1. Question, proposal or plan
Ask students to submit the question they will answer, the audience they are addressing, the method they expect to use and the main risk they see. Return quick feedback on scope and feasibility. A plan is especially useful when the final product could be generated from a generic prompt, because it records the student’s chosen problem and approach.
2. Sources, data or evidence log
Require a compact annotated list or research log. For each important source, students can record why it is relevant, who produced it, what limitation it has and how it changes the developing argument. In a data task, the equivalent might be a note on collection, cleaning and missing values. Grade the quality of evaluation, not the number of entries.
3. Draft or working artifact
Collect a version that is early enough to contain real choices. A draft may be an outline with a sample section, a prototype, a model specification, a storyboard or a worked problem. If AI is allowed, the student should label where it was used and retain only the material needed to explain the decision.
4. Feedback and response
Use peer, instructor or stakeholder feedback that addresses the learning outcome. Ask students to identify which feedback they acted on, which they declined and why. This response is more informative than a completion tick because it shows judgment and revision.
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Have students submit the final work with a short note naming two or three consequential changes, the evidence behind them and any unresolved limitation. The rationale can be brief; its purpose is to connect the final product to the process.
6. Reflection when it serves the outcome
A reflection should explain learning, not invite generic statements about effort. Prompts such as “Which source most changed your interpretation?” or “What did your first model get wrong, and how did you detect it?” elicit specific reasoning. Do not require a reflection if the outcome is already demonstrated clearly through another artifact.
Assignment patterns you can adapt
Scaffolded writing or project
Students submit a question or plan, selected evidence, a draft, a response to feedback and a final version with a revision rationale. This pattern fits essays, design projects, technical reports and policy recommendations. Keep the checkpoints short and tied to the rubric.
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AI-output critique
Provide or permit an AI-generated answer. Students check its claims and cited sources, identify omissions or errors, compare it with course material, and produce a corrected answer. The assessed skill is verification and contextual judgment, not the ability to produce a clever prompt.
Contextual case task
Give students a specific local situation, dataset, object, client brief or historical record. Require them to apply course ideas to that context and explain why a tempting alternative does not fit. Specific context reduces the value of a generic response while preserving access to permitted tools.
Process portfolio
Students select artifacts such as outlines, notes, drafts, readings and feedback, then explain what each shows about their choices and learning. The portfolio can accompany a larger project or receive a smaller share of the grade. Ask for representative evidence rather than every scrap of work.
Oral or multimodal demonstration
Debates, proposals, role plays, podcasts, posters and short films can reveal skills that a standard essay does not. Choose the mode because it matches the outcome, not simply because it appears harder to automate. Provide a script, rehearsal opportunity, captioning, alternative response mode or other accommodation where needed.
Research-process reflection
Ask students to describe how they evaluated sources, how evidence changed their thinking, and what problem they encountered and solved. This works as a short companion to a final product when the process itself is part of the learning.
Compare formats before you commit
Use the following questions to select evidence. No single format is universally superior.
| Decision axis | Questions to ask |
|---|---|
| Alignment | Does this artifact directly demonstrate the stated outcome, or merely show that a step was completed? |
| Direct evidence | Can you see the student’s understanding and disciplinary judgment without guessing? |
| Access | Can students with documented needs demonstrate the same capability through an equivalent route? |
| Feedback value | Will feedback arrive early enough to improve the final work? |
| Workload | Can you review and respond to every checkpoint at the promised level? |
| AI relationship | Is AI excluded, tolerated, integrated or the object of analysis, and is that choice visible to students? |
Berkeley guidance cautions that timed oral or handwritten assessments can disadvantage some students, and King’s guidance notes that oral assessment may cause anxiety. If an oral defense is pedagogically appropriate, provide preparation, predictable criteria and an equivalent accessible option rather than treating stress as proof of authenticity.
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Grade the thinking that matters
Publish a rubric before students begin. Weight criteria according to the outcome and discipline, not according to how many files a student uploads.
| Criterion | Evidence of strong work | What not to reward |
|---|---|---|
| Reasoning and interpretation | Claims follow from relevant evidence; assumptions and uncertainty are made visible. | Length, confidence or polished wording without a defensible chain of thought. |
| Evidence and source judgment | Sources, data or examples are appropriate, checked and used in context. | A long bibliography or unexamined AI citations. |
| Disciplinary method | The student applies the course’s concepts, procedures or conventions accurately. | Generic problem-solving language detached from the subject. |
| Response to feedback | Revision addresses consequential issues and explains reasonable decisions to accept or decline suggestions. | Cosmetic edits or an unchanged draft submitted again. |
| Final communication | The product serves its stated audience, purpose and format. | Presentation polish treated as a substitute for understanding. |
| Process explanation | The student can account for key choices and disclose permitted assistance accurately. | Documentation volume or performative claims about “doing everything alone.” |
Grade a process artifact only when it supplies evidence for one of these criteria. A research log that no one reads is administrative overhead, not assessment.
Set AI disclosure and privacy rules
When AI is allowed or required, ask students to record the tool, date, purpose and material contribution. A concise statement might say that an assistant was used to generate alternative headings, that all factual claims were checked against course sources, and that the final wording and structure were rewritten by the student. Require students to mark uncertain or unverified output rather than present it as evidence.
Align these instructions with institutional policy. Cardiff University advises staff not to upload student submissions or identifiable student work to external generative-AI tools and stresses that academic staff remain responsible for grades, standards and feedback decisions. Tell students and colleagues to check their own institution’s rules before entering personal, confidential or assessed material into any external service.
Do not treat an AI detector score as proof of authorship. A process record, conversation about choices and review of the work against the rubric provide more relevant evidence. If misconduct is suspected, follow the institution’s established procedure rather than improvising a technical test.
A complete example: a policy recommendation with visible reasoning
Outcome: Students will evaluate competing evidence and produce a recommendation that addresses a defined public audience.
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- Week 1—Question and plan: Submit a one-page problem definition, intended audience, proposed criteria and two risks. Receive scope feedback.
- Week 2—Evidence log: Submit four to six sources or data items with annotations explaining relevance, reliability and limitations.
- Week 3—Draft: Submit the recommendation and a short note identifying the strongest counterargument.
- Week 4—Feedback response: Record which peer or instructor suggestions will change the recommendation and why.
- Week 5—Final and rationale: Submit the revised recommendation plus a concise explanation of consequential revisions and AI assistance, if any.
The rubric evaluates evidence judgment, application of the course method, reasoning, response to feedback, audience-appropriate communication and accurate disclosure. It does not award points for producing more checkpoints than required.
Common design failures and repairs
Failure: trying to make the task “AI-proof”
Repair: Define the human capability you need to see and design evidence around it. Acknowledge that access cannot be eliminated in every setting.
Failure: multiplying checkpoints
Repair: Keep only stages that practice or demonstrate the outcome. Combine a draft and revision rationale when separate submissions add no useful information.
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Failure: vague AI language
Repair: Name permitted tools or stages, prohibited actions, disclosure fields and the student’s non-delegable responsibilities.
Failure: grading documentation instead of judgment
Repair: Put reasoning, evidence quality, disciplinary method and revision in the rubric; treat logs as supporting evidence.
Failure: using resistant formats without access planning
Repair: Check accommodations, anxiety, technology, language and scheduling before selecting timed, oral or handwritten work. Offer equivalent ways to show the same skill.
Failure: outsourcing academic decisions to software
Repair: Keep instructors responsible for standards, grades and feedback. Use student work and established procedures when questions arise.
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Review the assignment after it runs
Collect student questions, drafts and final work. Check whether each stage produced evidence you actually used, whether feedback arrived in time, and whether students misunderstood the AI boundary. Remove steps that created workload without insight; clarify prompts that yielded generic reflections; and adjust the rubric when students met the format but missed the learning outcome. The goal is a sustainable process that improves learning and makes consequential thinking visible.
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