Online examinations have become a common part of modern education, especially with the growth of remote learning platforms. However, maintaining fairness and preventing malpractice in such environments remains a major challenge. In this work, we present an AI-based online proctoring system designed to monitor candidates during examinations using real-time video and audio analysis. The system combines face recognition, gaze tracking, head pose estimation, and audio monitoring to detect suspicious activities such as impersonation, presence of multiple individuals, and abnormal behavior. During our testing across multiple sessions and varying environmental conditions, we observed that the system achieved an overall detection accuracy of approximately 92.6% while maintaining real-time performance of 20–30 frames per second. The proposed system reduces dependency on human invigilators and provides a scalable solution for large-scale online examinations.
Patil et al. (Thu,) studied this question.