When Machines Mislead: Human Review of Erroneous AI Cheating Signals

William Belzak, Chenhao Niu, Angel Ortmann Lee


Abstract
This study examines how human proctors interpret AI-generated alerts for misconduct in remote assessments. Findings suggest proctors can identify false positives, though confirmation bias and differences across test-taker nationalities were observed. Results highlight opportunities to refine proctoring guidelines and strengthen fairness in human oversight of automated signals in high-stakes testing.
Anthology ID:
2025.aimecon-wip.11
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:
October
Year:
2025
Address:
Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
Editors:
Joshua Wilson, Christopher Ormerod, Magdalen Beiting Parrish
Venue:
AIME-Con
SIG:
Publisher:
National Council on Measurement in Education (NCME)
Note:
Pages:
90–97
Language:
URL:
https://aclanthology.org/2025.aimecon-wip.11/
DOI:
Bibkey:
Cite (ACL):
William Belzak, Chenhao Niu, and Angel Ortmann Lee. 2025. When Machines Mislead: Human Review of Erroneous AI Cheating Signals. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 90–97, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
When Machines Mislead: Human Review of Erroneous AI Cheating Signals (Belzak et al., AIME-Con 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.aimecon-wip.11.pdf