AI-assisted Gaze Detection for Proctoring Online Exams

TL;DR

This paper introduces an AI-assisted gaze detection system to help proctors efficiently identify when test takers look away during online exams, improving security and reducing review time.

Contribution

The study presents a novel AI-assisted gaze detection system and an evaluation framework for proctoring, enhancing the detection of suspicious behaviors in online exam videos.

Findings

System improves proctor efficiency in detecting gaze deviations.

Evaluation shows system outperforms human-only review.

User feedback indicates increased effectiveness and usability.

Abstract

For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system.