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Respond to suspected undisclosed AI use as a documented academic-integrity question, not a contest between a teacher’s intuition and a detector score. First identify the rule students received, then invite a private explanation, review policy-permitted evidence, protect privacy, and use the established school or institutional process. A detector result or unusual writing style can prompt a conversation where local policy allows; neither proves misconduct.
Start with the rule that applied to this assignment
You cannot decide whether a student misrepresented AI use until you know what the student was told. Read the assignment sheet, course outline, rubric, institutional academic-integrity policy, and any school or jurisdiction guidance that applied when the work was produced.
Establish four points before contacting the student:
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- Which uses were prohibited, including generating passages, substantially revising submitted work, or producing answers in place of the student.
- What disclosure or acknowledgement the assignment required and where it had to appear.
- Whether the instructions changed during the course and which version the student received.
Rules are not interchangeable across jurisdictions. The University of Victoria, for example, directs students to course outlines for the applicable GenAI rule and treats prohibited use under its own integrity policy. Calvin University allows course- or assignment-level rules to define disclosure and requires faculty to follow existing procedures. Those are institution-specific examples, not universal definitions.
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A fair response sequence
Use the following sequence whenever a concern arises. It keeps facts, questions, and decisions separate.
1. Pause and describe the observable concern
Write down what you actually know before forming a conclusion. Examples include a missing disclosure that the assignment required, references that do not exist, a process requirement the submission cannot demonstrate, a student statement about how the work was created, or an answer that conflicts with documented work completed in class.
A sudden change in style or a detector result may justify a question where local policy permits it. Treat either as a lead, not as a finding. Victoria’s Department of Education lists differences from a student’s usual performance as a reason to authenticate work, while warning that plagiarism and detection tools can be inaccurate or inconclusive.
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Confirm what evidence your procedure permits you to collect and use. A policy may specify referral routes, timelines, who decides the matter, what the student may review, and how an appeal works. Do not collect information merely because a tool makes it easy to obtain.
3. Invite a private, neutral conversation
Tell the student what you noticed, identify the relevant expectation, and ask for an explanation without announcing a verdict. A useful opening is:
“Your submission appears to differ from the process required for this assignment, and the required AI acknowledgement is not present. I would like to understand how you developed this work and make sure we apply the course policy fairly.”
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Ask the student to walk through their process, sources, tools, drafts, and understanding of the disclosure rule. Leave room for permitted assistance, a misunderstanding, an accessibility-related tool, or an error in your initial interpretation. Do not promise a particular grade or sanction during this conversation.
4. Review context, not a single artifact
Use only evidence allowed by policy and legitimately available to you. Depending on the course, that can include drafts, version history, planning notes, cited-source checks, in-class work, a brief explanation of the submitted reasoning, or an oral follow-up task. If a student voluntarily shares prompts or interaction records, examine whether they show a process of checking ideas and sources; do not treat a log as an automatic guilt or innocence test.
5. Decide through the established process
If the concern remains, refer or resolve it exactly as the applicable policy requires. Record the rule, the evidence reviewed, the student’s response, the decision-maker, the reasoning, and any response or appeal rights. A detector score by itself is especially inappropriate where policy excludes detector output from investigations.
6. Close the loop
Communicate the outcome and next step to the student under the required confidentiality rules. For the class, clarify the expectation without revealing private details: which uses are allowed, which are not, how to acknowledge assistance, what process evidence may be requested, and what happens after a suspected breach.
How to speak with a student suspected of using AI
Use specific observations
“The bibliography contains two sources I cannot locate” is actionable. “This sounds like AI” is not. Describe the missing disclosure, unverifiable source, contradiction with the assignment process, or answer the student cannot yet explain.
Ask process questions
- How did you choose and verify your sources?
- What did you do first, and how did the draft change?
- Which tools did you use for brainstorming, translation, editing, coding, or generating text?
- What did the assignment’s AI rule mean to you?
- Can you explain the reasoning behind this claim, calculation, design choice, or citation?
These questions test understanding and process rather than asking a student to prove a negative. An explanation that is incomplete may require further review, but it is not itself proof of intentional deception.
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Keep the conversation non-accusatory
Do not threaten a sanction, demand a confession, or imply that a student must disclose private medical or disability information. If an accessibility tool is relevant, follow the institution’s accommodation and privacy process instead of requiring public disclosure in front of peers.
Give a meaningful opportunity to respond
Calvin University states: “Students must have an opportunity to respond to concerns about suspected AI misuse, and faculty must follow existing academic integrity processes.” That principle is a practical minimum even when another institution’s procedure uses different forms or deadlines. Tell the student how and when to provide a response and what evidence the decision-maker will consider.
Why AI detectors cannot decide the case
Detector outputs are not a universal measure of authorship. OpenAI reports that its own detector research 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.” Its guidance says a detector should not be the sole basis for an integrity violation; if an institution permits use, the result should prompt conversation and further investigation.
Institutional rules can be stricter. The University of Victoria says GenAI tools may not be used to determine an integrity violation or as evidence in an investigation. California’s Department of Education model policy likewise says detector software should not be the sole basis for discipline or a grade penalty. That model is exemplary rather than mandatory, so schools must check current state, district, and institutional rules.
Do not ask a generative model whether it wrote the passage
OpenAI says a model’s answer to “Did you write this?” is random and not grounded in knowledge. Such a response cannot authenticate a student’s work.
Do not turn a score into a cheating probability
A percentage displayed by a detector is not a measured probability that a student cheated. It does not establish which tool produced text, whether a student authorized that use, or whether the assignment rule was violated.
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Account for language and writing differences
OpenAI’s description of its detector research notes possible disproportionate effects on English learners and on concise or formulaic writing. A multilingual student, a student using accessibility support, or a student completing a highly structured task should not face a higher burden merely because prose patterns differ.
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Evidence that can help, and its limits
| Evidence or step | What it can establish | What it cannot establish alone |
|---|---|---|
| Assignment and course instructions | The permission, prohibition, and disclosure standard the student received. | Whether the student actually followed it. |
| Drafts, notes, or legitimate version history | How ideas developed and whether the documented process is consistent with the submission. | That every undocumented step was misconduct. |
| Source and citation checks | Whether cited works exist and support the claims made. | Who wrote accurate or inaccurate prose. |
| Student explanation or oral follow-up | Whether the student can explain reasoning, choices, and the process used. | A mechanical verdict without considering the policy and context. |
| Prompt or interaction records | What assistance was requested and whether the student verified or revised the output, when sharing is permitted. | That any AI assistance was prohibited or that a record is complete. |
| Detector output or style difference | A possible reason to ask a policy-permitted question. | Proof of AI use, intent, authorship, or a sanctionable breach. |
OpenAI suggests documenting AI interactions as a way to examine reasoning and source checking. California’s model policy also points to educator evaluation, alternative assessment, and documented authorship evidence. Use those approaches only when they fit the announced assessment and local procedure.
Protect student privacy while investigating
Never paste identifiable student work, grades, disability or accommodation information, conduct records, or other sensitive data into an unapproved external AI service. Calvin University’s acceptable-use policy expressly bars entering sensitive or identifiable student information into external or unapproved tools.
- Use institutionally approved storage and case-management systems.
- Remove names and unnecessary identifiers before a permitted consultation.
- Share only the material needed for the decision.
- Record who accessed evidence and retain it for the period required by policy.
- Tell the student what evidence is being considered when the procedure requires disclosure.
Choose the response path that fits the evidence and policy
| Path | Appropriate when | Advantages | Risks to control |
|---|---|---|---|
| Discussion first | The concern is specific but unresolved, and the policy allows an instructor conversation. | Lets the student explain permitted use or misunderstanding before a formal allegation; supports learning. | Do not pressure the student, promise an outcome, or bypass a mandatory referral. |
| Formal referral | The policy requires referral, or documented evidence indicates a possible breach that the designated office must decide. | Uses trained decision-makers, consistent standards, notice, and appeal rights. | Submit the rule and evidence, not speculation or a detector score presented as proof. |
| Detector-informed inquiry | Only where local policy permits detector use as a limited prompt. | May identify work for a closer, human-led review. | Disclose its use if required; never make it the sole basis for a grade or discipline decision. |
| Alternative or oral assessment | The learning outcome can be assessed through a permitted follow-up, demonstration, or supervised task. | Tests the intended knowledge directly without pretending to identify an author from prose alone. | Apply the same standard fairly and avoid creating an unannounced second assessment that changes the course requirements. |
Compare every 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 its intended learning outcome?
Jurisdiction and institution differences
| Context | Current guidance | Limit on applying it elsewhere |
|---|---|---|
| England schools and colleges | Ofqual’s “AI in coursework” resources, published 9 March 2026, state: “However, AI should not be used to generate coursework without proper disclosure.” The package includes classroom discussion materials and a senior-leadership briefing. | Its regulatory framing and consequences apply to England; they are not a universal rule for other systems. |
| Victoria, Australia schools | The Department of Education recommends clear expectations, assessment-specific decisions, transparent authentication, and consistent, proportionate responses. Its guidance was updated 17 June 2025. | This is state-school guidance. Check the school, sector, and jurisdiction that govern your class. |
| University of Victoria | Course outlines identify whether tools are allowed, and the university disallows GenAI tools for deciding violations or serving as investigation evidence. | This is UVic policy, not a sector-wide Canadian standard. |
| Calvin University | Students must have an opportunity to respond; faculty must follow existing integrity processes, and course-level rules can define disclosure. | This is a Calvin policy, not a universal United States standard. |
| University of Rochester | Detectors should not be the sole basis for an integrity violation. If used, the institution says to disclose their use and treat results as a prompt for conversation and investigation. | Other institutions may prohibit detector use more strictly. |
| California Department of Education model policy | The exemplary model says detector software should not be the sole basis for discipline or a grade penalty and encourages local adaptation. | It is not mandatory by itself; consult current state, district, and school rules. |
| TEQSA-hosted University of Queensland principles | The principles advise designing transparent criteria around learning outcomes rather than misconduct spotting and making acknowledgement clear and low burden. The page identifies them as general advice for UQ staff, not mandatory TEQSA policy. | Do not present the advice as a binding national requirement. |
If a breach is established
Use the available educational or disciplinary outcomes in the applicable policy. Do not copy a sanction from another university, state, or country. Victoria recommends a consistent, proportionate response, while Ofqual’s England-specific materials warn that coursework malpractice can have serious consequences. The same conduct may therefore enter different procedures or produce different outcomes in different systems.
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Prevent recurrence with clearer assessment design
State permitted and prohibited uses in plain language
Replace “use AI responsibly” with concrete categories. Explain whether students may brainstorm, translate, check grammar, generate code, obtain feedback, or generate text; whether they must retain drafts or prompts; and exactly where an acknowledgement belongs. Keep the acknowledgement requirement simple enough that students can comply without guessing.
Align the task with the learning outcome
TEQSA-hosted University of Queensland principles caution: “Adding descriptors aimed at spotting misconduct confuses students and markers and rarely works.” Write criteria that assess the knowledge, reasoning, performance, or design skill the course intends to teach. Do not add vague style criteria merely to make AI use harder to detect.
Choose assessment formats deliberately
Victoria notes that some tasks may appropriately limit AI, while projects or portfolios may permit or require it. A supervised explanation, staged draft, demonstration, reflection on sources, or oral defense can make the relevant learning visible without relying on a detector. If AI is part of professional practice, assess the student’s ability to direct it, verify outputs, disclose use, and correct errors.
Make authentication transparent
Tell students in advance what process evidence may be requested and why. Apply the same requirement to comparable students, provide accessible alternatives, and avoid collecting more personal data than necessary.
Documentation checklist for instructors and integrity staff
- The exact assignment and course rule in force when the work was submitted.
- The observable concern, separated from any inference about authorship.
- Dates, participants, and a factual account of the student conversation.
- Evidence reviewed and the policy basis for collecting it.
- Any detector use, including whether local policy allowed it and how its limitations were explained.
- The student’s response, supporting material, and any accessibility or language context relevant under policy.
- The decision, rationale, proportionality analysis, notification, and appeal or response rights.
- Storage, access, retention, and deletion steps for sensitive records.
This record allows a later reviewer to see not only what conclusion was reached, but whether the announced rule, fair opportunity to respond, privacy protections, and evidence standards were followed.
What to tell the class afterward
Do not disclose the individual case. Issue a short, consistent reminder that answers five questions: what AI uses are allowed, what uses are prohibited, how to acknowledge allowed assistance, what evidence of process may be requested, and what procedure follows a suspected breach. Ofqual’s 2026 England resources include discussion scripts and emphasize consistent messaging across an institution; schools elsewhere should adapt the communication to their own rules.
The goal is not to make teachers better at guessing which sentences came from a model. It is to make expectations knowable, investigations fair, evidence relevant, and assessment capable of showing the learning it is supposed to measure.
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