detector_evaluation
StyleShield: Exposing the Fragility of AIGC Detectors through Continuous Controllable Style Transfer
Guantian Zheng · arXiv (2026) · doi:10.48550/arxiv.2605.00924 · gemini-3.1-flash-lite
Papers tagged with: #академічна-доброчесність
assessment_design
Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating
Tobias Braun, Jonas Grebe, Louis Rethfeld, Marcus Rohrbach · arXiv (2026) · doi:10.48550/arxiv.2608.01112 · gemini-3.1-flash-lite
psychometric_modeling
What exam scores can and cannot prove about unauthorized AI assistance: Evidence from a highly public classroom episode
Chad M. Topaz, Utsav Bahl · arXiv (2026) · doi:10.48550/arxiv.2607.27978 · gemini-3.1-flash-lite
detector_evaluation
Why AI Detection Fails for Academic Integrity
Jonathan A. Karr, Grigorii Khvatskii, Ting Hua, Nitesh V. Chawla · arXiv (2026) · doi:10.48550/arxiv.2608.11256 · gemini-3.1-flash-lite
quasi_experiment
Does AI Assistance Leave a Temporal Fingerprint? Detecting Overreliance in AI-Assisted Writing and Programming
Eduardo Davalos, Yike Zhang · arXiv (2026) · doi:10.48550/arxiv.2609.17883 · gemini-3.1-flash-lite