Cheating detection in large classes with online, take-home exams is an extremely difficult problem. As class size increases, the process of detection and evidence building becomes a significant investment of time. To identify cheating without invasive real-time monitoring, Examinator v4.0 uses answer rarity and submission timestamps. Creative question design allows detection of cheating even when answers are correct. Automatic report generation saves instructors time when compiling evidence for cases. Examinator v4.0 streamlines the identification and reporting of cheating students in online take-home exams, analyzing 7683 unique submissions from 1923 students across two semesters and resulting in 52 convictions of academic misconduct.

Cheating detection in large classes with online, take-home exams is an extremely difficult problem. While some cheating can be identified through statistical analysis of all student responses, this analysis can easily be fooled by "smart cheaters'' actively attempting to hide evidence of their unauthorized collaboration. We demonstrate the effectiveness of watermarks combined with creative question design to provide evidence of cheating. We provide results from an initial deployment of our answer watermarking method and do a case study into how "smart cheaters'' attempt to cover their tracks, demonstrating the need for more advanced methods of catching cheating in online, take-home exams.

Interactive teaching methods often lead to higher levels of student engagement with course material. Yet, as class sizes increase, the demand on teaching staff becomes unsustainable. Our solution, Socratic Mind, employs Large Language Models to provide scalable, interactive oral assessments by functioning as a virtual instructor. This paper discusses the outcomes and user feedback from the preliminary implementation of our system in a large classroom environment with 600 students.

Examinator v3.0 detects cheating in online take-home exams by comparing answers and the timestamps they were entered. A web interface enables efficient manual inspection. Use of the tool reveals that certain question types substantially enhance cheating detection, demonstrating the potential of automated algorithmic detection at scale. Examinator v3.0 has analyzed 915,831 pairs of exam submissions across three courses over two semesters at a top U.S. institution, identifying 46 instances of cheating.