Summary of Ai-assisted Gaze Detection For Proctoring Online Exams, by Yong-siang Shih et al.
AI-assisted Gaze Detection for Proctoring Online Exams
by Yong-Siang Shih, Zach Zhao, Chenhao Niu, Bruce Iberg, James Sharpnack, Mirza Basim Baig
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an AI-assisted gaze detection system for detecting potential rule violations during high-stakes online exams. The system enables proctors to efficiently identify suspicious moments in exam videos by navigating between video frames and identifying instances where test takers look away from the screen. The authors propose an evaluation framework comparing human-only, ML-only, and AI-assisted proctoring methods, and conduct a user study to gather feedback from proctors. The system’s effectiveness is demonstrated through its ability to aid in detecting gaze directions and improving the efficiency of proctoring processes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps keep online exams safe by developing a tool that can detect when test takers look away from their screens. Right now, proctors have to watch entire videos to find these moments, which is time-consuming. The new system lets proctors jump between video frames and find the exact moments when test takers are looking in similar directions. This makes it easier for proctors to identify any suspicious activity during exams. |
