Proctoring Using AI

This paper presents a comprehensive AI-based online examination proctoring system designed to maintain academic integrity in remote assessment environments. The system employs multiple machine learning techniques including facial recognition, head pose estimation, voice detection, electronic device identification, and behavioral analysis to monitor students during online examinations. The proposed system integrates computer vision algorithms, deep learning models, and real-time monitoring capabilities to detect potential cheating behaviors with high accuracy. Experimental results demonstrate the system's effectiveness in identifying various forms of academic misconduct while maintaining user privacy and system reliability. The system achieved a detection accuracy of 94.2% for facial verification, 89.7% for head movement detection, and 91.3% for electronic device identification across diverse testing scenarios