​AI PLAGIARISM DETECTOR

• Summary: Lack of Human Intervention – The AI flagged a student’s work as AI-generated and, with an unclear policy, staff lean toward trusting the software. There was no human double-check or intervention before penalizing the student, which is problematic.

• Human in the Loop – Require a human (teacher or academic committee) to review AI plagiarism detections before finalizing any accusation or grade. In practice, the teacher should manually verify the work’s originality (e.g. discuss with the student or use alternative checks) instead of relying solely on the AI’s judgment.

• Medium Risk – A false accusation of cheating can significantly harm a student’s academic record and trust, though it is reversible if caught. The situation poses a moderate risk to fairness and student–teacher trust (not life-threatening, but serious for integrity).
   

• Decision: This scenario’s fairness issue should be addressed rather than tolerated. The school would implement the safeguard of keeping a human in the loop – for example, updating school policy so that AI plagiarism flags are treated as preliminary, requiring human review and clear evidence before any disciplinary action. By mitigating the risk (adding oversight and perhaps training staff about AI detection limitations), the school avoids unjust punishment of students and maintains trust in the grading process.