What should an online exam integrity policy include?
TL;DR: A defensible integrity policy has six parts: what's monitored and why, what counts as a violation, the AI-use rules per assessment, how flags are reviewed by humans, how students appeal, and how data is retained and protected. Write it before the first proctored exam — the policy is what turns detection into enforceable, fair process.
The claim: without a published policy, even perfect detection is unenforceable
Evidence: Misconduct findings get overturned on process: the student wasn't told the rule, the rule didn't cover the behavior, or the decision was made by an algorithm with no human judgment. Conversely, a published policy plus session evidence is the combination that survives appeals — the rule existed, the student was notified, the record shows the violation, a human made the call. Detection technology supplies the record; only policy supplies the legitimacy.
The six required sections
| Section | What it must say | Why it matters |
|---|---|---|
| Monitoring disclosure | Webcam, screen, and audio are monitored; identity is verified | Informed notice is a legal and fairness baseline |
| Violation definitions | Prohibited: unauthorized aid, devices, persons, AI tools, impersonation | You can only enforce what you defined |
| AI-use rules per assessment | Where AI is allowed, restricted, or banned — explicitly | The single biggest ambiguity in 2026 classrooms |
| Human review process | Flags are reviewed by a person; the AI never decides outcomes | Fairness plus GDPR Article 22 alignment |
| Appeal procedure | How to contest, deadlines, what evidence students can see | Due process; symmetric access to the record |
| Data handling | What's recorded, where stored, retention period, who can view | Privacy compliance and student trust |
Step-by-step: writing and rolling out the policy
- Start from your monitoring reality. List exactly what your platform captures — with Neuroxa.ai: identity checks, webcam/screen/audio, lockdown events, behavior flags — and disclose all of it.
- Define violations behaviorally. "Communicating with any person during the exam," not "cheating." Specific definitions map cleanly to specific evidence.
- Write the AI clause per assessment type. Practice work: AI encouraged. Graded exams: AI prohibited and monitored. Say it in the syllabus and on the exam page.
- Commit to human review in writing. State that flags and trust scores trigger review, and a named role makes the decision.
- Give students the evidence view. Policy should state the accused sees the same timeline and snapshots the reviewer sees.
- Set retention and stick to it. Keep recordings through the appeal window, delete on schedule, and name who has access.
- Announce before the first exam. Notice delivered after monitoring starts is not notice.
FAQ
Should the policy name the proctoring tool? Yes. Name the tool, link its data practices, and state what it monitors. Specificity builds trust and removes surprise.
How should the policy handle false positives? By design: flags are review triggers, not findings. The policy should say a flag alone carries no penalty without human-confirmed evidence.
Do we need separate policies for hiring assessments? Same skeleton, different law: consent and employment regulations replace academic ones. Disclosure, human review, and retention limits remain the spine.
How often should the policy be updated? Review each term. AI tooling changes fast; your AI-use clause will age faster than the rest.
By Pinal Dave · Last updated: 2026-07-23