Use a fair, policy-led process—not a guess based on writing style. When a student may have concealed or misrepresented generative-AI assistance, first identify the rule that applied to the assignment, then document the specific concern, invite a private explanation, review evidence that your institution permits, and follow the established academic-integrity procedure. A detector score, a stylistic hunch, or an AI model’s opinion about authorship is not proof on its own.
The applicable rule may differ by course, institution, state or country. The instructions students actually received control the initial analysis: what uses were allowed, what uses were prohibited, and what acknowledgment or process evidence was required.
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What to do first
- Pause and separate facts from inference. Write down observable facts, such as a missing disclosure required by the assignment, a citation that does not exist, an answer that conflicts with a documented process, or a statement the student made about producing the work. Treat a detector result, abrupt style difference or unusual vocabulary as a reason to ask a question only where local policy allows—not as a finding.
- Secure the relevant records. Keep the submitted file, instructions, rubric, permitted-use statement, cited sources and any legitimately available drafts or version history. Do not obtain private material by bypassing access controls or by demanding data your policy does not authorize you to collect.
- Read the rule as the student received it. Check the assignment sheet, course outline, institutional integrity code and applicable school or jurisdiction guidance. Note whether the rule distinguished brainstorming, translation, editing, accessibility support, feedback and generated text, and what disclosure format it required.
- Invite a private, neutral conversation. Describe the concrete concern and the relevant expectation without opening with an accusation. Give the student a meaningful opportunity to explain their process and any permitted use.
- Review context and document your reasoning. Use only evidence allowed by the procedure. Record the rule, evidence reviewed, student response, decision and rationale.
- Refer or decide through the correct process. Follow required referral routes, timelines, evidence standards, response rights and appeals. Do not impose a penalty simply because a tool produced a high score.
This sequence protects the student’s rights and keeps the assessment focused on the learning outcome rather than on an uncertain authorship prediction.
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Start with the assignment rule, not the suspicion
There is no universal definition of “AI misuse.” One course may permit brainstorming but prohibit generated sentences; another may require an approved tool and a disclosure statement; a third may prohibit generative tools altogether. Determine the rule that was announced before deciding whether a student misrepresented their work.
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Questions to answer from the course materials
- Was generative AI allowed, limited, required or prohibited for this task?
- Did the rule distinguish planning, tutoring, translation, grammar correction, accessibility support, image generation, code generation and substantive rewriting?
- What did “disclosure” mean: a statement in the submission, a citation, an appendix, a prompt log or another format?
- Was the requirement changed during the course, and can you show when students received the change?
- Does the institutional policy require a referral rather than a faculty-level decision?
The University of Victoria directs students to their course outlines to determine whether GenAI tools are allowed and treats prohibited use under its own academic-integrity policy. That is a UVic rule, not a definition that automatically applies elsewhere. Calvin University likewise allows course- or assignment-specific rules to define disclosure and requires faculty to use existing integrity procedures.
How do I know if a student used AI?
You usually cannot establish authorship from prose alone. The strongest response is an evidence-based conversation tied to the announced rule, not an attempt to calculate the probability that a detector is correct.
Potentially relevant facts and their limits
| Observation | What it may justify | What it cannot establish by itself |
|---|---|---|
| Required disclosure is missing | Ask what tools were used and whether the student misunderstood or omitted the required statement. | That prohibited AI-generated text was used; the omission may be intentional, accidental or caused by unclear instructions. |
| A cited source does not exist or does not support the claim | Verify the source and ask the student to explain how it was selected and checked. | That an AI system produced the entire assignment. |
| The submission conflicts with drafts, notes or a documented process | Ask the student to walk through the change and the reasoning behind it. | That every changed passage is AI-generated. |
| The work differs sharply from demonstrated knowledge or usual performance | Use an explanation, oral check or other authentication method if permitted by policy. | That a style difference proves AI use. Victoria’s Department of Education lists this as a possible reason to authenticate, while warning that detection tools can be inaccurate or inconclusive. |
| A detector reports a high AI-likelihood score | Only follow the local rule about whether the result may prompt further inquiry. | A probability that the student cheated, or definitive proof of a violation. |
OpenAI says its published detector findings did not find tools reliable enough for consequential educator decisions. The University of Rochester describes GenAI detectors as “unreliable, biased, easily defeated, and unable to provide definitive evidence of academic honesty policy violations.” UVic does not permit GenAI tools to determine violations or serve as evidence in its investigations. California’s exemplary model policy says detector software should not be the sole basis for discipline or a grade penalty. Other institutions may prohibit detector use more strictly, so check your own policy.
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Asking ChatGPT or another generative model, “Did you write this?” does not produce a reliable authorship determination. OpenAI states that the model’s answer is random and not grounded in knowledge of the text’s origin. Treat any such response as unusable for an integrity finding.
Account for equity and language effects
Concise, formulaic or multilingual writing can be misread as machine-generated. OpenAI reports that its published detector findings indicated possible disproportionate impact on English learners and formulaic or concise writing. Do not penalize a student for a linguistic pattern or genre convention without evidence tied to the assignment rule and an opportunity to respond.
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What should I say to a student suspected of using AI?
Meet privately and use a neutral, specific opening. The goal is to understand the production process and give the student a fair chance to address the concern.
A practical opening script
“I want to discuss one aspect of your submission. The assignment required [state the exact rule]. I noticed [describe the observable fact, such as an uncited passage or a source that cannot be verified]. This is a conversation, not a decision. Please walk me through how you developed this answer, which sources and tools you used, and how you understood the disclosure requirement.”
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- What was your first step after receiving the prompt?
- Which source led you to this claim, and how did you check that it supported it?
- Why did you choose this example, method or structure?
- What tools did you use for brainstorming, translation, editing, accessibility or drafting?
- Which parts did you write or revise yourself?
- What did you understand the assignment’s disclosure language to require?
- Can you explain this conclusion in your own words or apply the same method to a comparable question?
An explanation is not a theatrical “gotcha” test. A student may have used a permitted tool, misunderstood a boundary, relied on an accessibility feature or made an ordinary source error. Listen to the answer, ask follow-up questions about the documented facts, and avoid promising a particular outcome before the review is complete.
Review evidence in context and protect privacy
Use evidence that is relevant, authentic and permitted by your procedure. A single artifact rarely settles authorship.
Useful contextual material
- Assignment drafts, planning notes and version history when the student or institution legitimately has access to them.
- Source checks showing whether references exist and support the claims made.
- The student’s explanation of the submitted reasoning, method and choices.
- Prompt or interaction records when the student voluntarily provides them or the assignment clearly required them.
- An oral explanation or alternative assessment authorized by policy and applied consistently.
OpenAI describes interaction records as potentially useful for examining reasoning and source-checking, while California’s model policy identifies educator evaluation, alternative assessment and documented authorship evidence as possible approaches. These materials inform a contextual judgment; none is an automatic guilt test.
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Privacy safeguards
- Do not upload identifiable student work, grades, disability accommodations, conduct information or other sensitive records to an unapproved external AI service.
- Follow your institution’s retention, access and data-protection rules when storing drafts, screenshots or logs.
- Ask only for records relevant to the stated concern; do not demand a student’s entire private account or device.
- Tell the student what evidence you are reviewing and who will receive it under the procedure.
Calvin University expressly bars entering sensitive or identifiable student information into external or unapproved tools. Comparable restrictions may exist in your institution’s policy even when they are not written in the assignment instructions.
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Choose the response path that your policy permits
| Path | When it fits | Strengths | Risks and controls |
|---|---|---|---|
| Discussion and authentication | The concern is limited or ambiguous, and policy permits a conversation or oral explanation. | Lets the student explain permitted use or misunderstanding; can reveal whether the work measures the intended learning. | Inconsistent questioning can create inequity. Use the same core questions and document the basis for any follow-up. |
| Formal academic-integrity referral | The concern remains after review, or institutional rules require referral. | Provides defined evidence standards, notice, response rights, timelines and appeal routes. | Do not prejudge the outcome. Send the rule, evidence and student response rather than a detector score presented as proof. |
| Detector-informed inquiry where allowed | Local policy expressly permits a detector as a limited prompt for further review. | May identify work that merits a closer look. | Scores are unreliable, biased and easy to defeat. Disclose the tool, never use it as the sole basis, and provide a meaningful chance to rebut the concern. |
Compare each option against six questions: Is the evidence admissible and reliable under local rules? Can the student understand and answer the concern? Does the response match the announced assignment policy? What are the privacy and equity effects? Is the outcome proportionate? Does the assessment still measure the intended learning?
Document the decision
A concise case record makes the process reviewable and helps colleagues apply the policy consistently. Include:
- The assignment, course, date and exact AI-use or disclosure rule.
- The observable facts that prompted the concern, separated from inferences.
- Evidence reviewed and how it was obtained.
- The questions asked and the student’s material response.
- Any accessibility, language or technology context the student raised.
- The policy section and procedure used.
- The decision, rationale, outcome and instructions for appeal or next steps.
California’s model policy recommends documented authorship evidence and clear rebuttal criteria. Calvin requires students to have an opportunity to respond and faculty to follow existing academic-integrity processes. Those safeguards are especially important when evidence is uncertain.
If a breach is established, respond proportionately
Use the outcomes available under your institution’s procedure rather than copying a sanction from another school or country. A response may be educational, disciplinary or both, depending on the policy, the assignment, the student’s explanation and any prior findings. Victoria’s guidance calls for consistent, proportionate responses. Ofqual’s England materials warn that coursework malpractice can have serious consequences, but those consequences should not be exported to systems with different rules.
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Communicate the outcome
Tell the student which rule was applied, what was decided, what happens next and how to seek review or appeal. Do not disclose another student’s case or publish private details as a warning to the class. For the class, restate the general expectation and disclosure method without implying that a private case proves a broader pattern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Prevent the next dispute with clearer assessment design
Prevention starts before submission. State the boundary in plain language and design evidence of learning into the task.
Write an explicit permitted-use statement
Identify what students may do, may not do and must disclose. For example:
For this assignment, you may use a spelling checker and a permitted accessibility tool. Do not use a generative system to produce or substantially rewrite the answer. If you use an approved tool for brainstorming or translation, describe the tool, purpose and affected sections in a disclosure note.
Adapt the statement to the actual learning outcome and institutional policy. Do not use a generic prohibition if the course permits a different boundary.
Make acknowledgment low-burden and checkable
Provide one short format, such as a disclosure box or appendix, and show an example. The TEQSA-hosted University of Queensland principles advise transparent criteria focused on learning outcomes and caution: “Adding descriptors aimed at spotting misconduct confuses students and markers and rarely works.” The same principles recommend acknowledgment that is clear without being onerous.
Align the task with the learning outcome
Victoria notes that some tasks may appropriately limit AI, while projects or portfolios may permit or require it. If the outcome is source evaluation, require source annotations. If it is oral reasoning, include a brief explanation or viva. If tool use is part of professional practice, assess the student’s verification, attribution and decision-making rather than pretending the tool was not used.
Use process evidence consistently
Tell students in advance whether drafts, planning notes, version history, a prompt log or a short oral explanation will form part of assessment. Apply the same requirement to the class, offer an accessible alternative where needed, and avoid collecting more personal data than the learning outcome requires.
Jurisdiction and institution notes
| Context | What the published guidance says | Limit |
|---|---|---|
| England schools and colleges | Ofqual published classroom and leadership resources on 9 March 2026. Its resource states, “However, AI should not be used to generate coursework without proper disclosure,” and provides discussion materials and scripts. | England-specific guidance; check the rules of the awarding body, school and qualification. |
| Victoria, Australia schools | The Department of Education recommends clear expectations, assessment-specific decisions, transparent authentication and consistent, proportionate responses. It warns that detection tools may be inaccurate or inconclusive. | State-school guidance; consult the school and applicable jurisdiction. |
| University of Victoria | Course outlines identify whether tools are allowed; UVic does not permit GenAI tools to decide integrity violations or serve as investigation evidence. | A UVic policy, not a sector-wide Canadian rule. |
| Calvin University | Students must have an opportunity to respond; faculty must follow existing integrity processes, and course-level rules can define disclosure. | A Calvin policy, not a universal United States standard. |
| University of Rochester | Detector tools should not be the sole basis for an integrity violation; if used, their use should be disclosed and limited to prompting conversation and further investigation. | Rochester guidance; another institution may prohibit detector use entirely. |
| California Department of Education model policy | Detector software should not be the sole basis for discipline or a grade penalty; the model is exemplary and invites local adaptation. | It is not mandatory by itself; check current state, district and institutional rules. |
| TEQSA-hosted University of Queensland principles | Assessment criteria should be transparent and tied to learning outcomes rather than misconduct spotting, with clear acknowledgment expectations. | General advice developed for UQ staff, not mandatory TEQSA policy. |
A short checklist for staff and leaders
- Read the exact assignment and course rule.
- Record facts separately from suspicions.
- Check whether detector use is allowed, restricted or prohibited.
- Invite a private explanation using neutral language.
- Verify sources and review permitted process evidence.
- Protect identifiable and sensitive information.
- Give the student notice, response rights and any required appeal route.
- Document the rule, evidence, response, decision and rationale.
- Apply a consistent, proportionate outcome through the established procedure.
- Clarify future AI boundaries, disclosure and process evidence for the whole class.
The defensible standard is not “Can I prove that software wrote this?” It is “Did the student follow the rule that applied, and have I reached that conclusion through a fair process using evidence my institution permits?”
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