What teachers need to see
A reviewer needs the original page, extracted text, question, model answer, rubric, proposed marks and explanation in one place. Hiding any of these increases the chance of approving an error.
The interface should make corrections easy and should not pressure reviewers to accept the AI suggestion.
When review must be mandatory
Unreadable handwriting, low-confidence extraction, missing pages, ambiguous question mapping, unusual answers and score limits should trigger review. High-stakes examinations may require full review regardless of confidence.
Institutions should document the trigger rules and who is authorized to approve or override marks.
Sampling apparently successful work
Exception queues detect known uncertainty, but systematic errors can appear confident. Randomly review a sample of non-flagged papers and compare error rates across subjects and batches.
Increase the sample when a new paper format, model, OCR engine or rubric is introduced.
Moderation and disagreement
When AI and faculty disagree, the goal is not always to make the human accept the system. Record the disagreement category: OCR, mapping, rubric interpretation, calculation or academic judgment.
These labels reveal where the process needs improvement and provide evidence for future validation.
Audit and student rights
Keep a trace of the original input, proposed output, human changes and final result according to institutional retention policy. Protect student information and limit access to authorized users.
Students should retain the institution’s normal options for clarification, rechecking or appeal. AI assistance should not reduce procedural fairness.
A simple review policy
Define risk level, required reviewer, sampling percentage, escalation rule and final approver for each assessment type. Train faculty on known limitations and test the workflow before live use.
Review policies should be revisited when performance data or examination requirements change.
Review interface requirements
A reviewer should be able to move quickly between the scanned page, extracted response and rubric without losing context. The system should show why a case was flagged and whether a previous reviewer changed the text or score. Keyboard navigation, clear status labels and a visible save state reduce operational mistakes during large batches.
Do not use design patterns that make “accept” easier than “correct.” Human review is meaningful only when disagreement is expected and easy to record.
Training and quality ownership
Train evaluators with known OCR failures, ambiguous responses and examples of appropriate overrides. Assign one owner for pilot quality and a separate authorized role for final result publication. Review error reports after every assessment cycle and turn recurring problems into updated scan rules, rubric guidance or manual-only exceptions.