A novel AI-powered cheating detection system for online examinations using computer vision and keystroke dynamics

Online examinations require reliable mechanisms to ensure academic integrity; however, many existing systems rely on rigid rule-based approaches that often generate false alerts and fail to account for individual behavioral differences. This paper presents ScoreHunt, an AI-based adaptive cheating detection system that analyzes personalized student behavior using computer vision and keystroke dynamics. The proposed system establishes a behavioral baseline for each student and continuously compares it with real-time activities, including facial presence, gaze patterns, and typing behavior. A multi-indicator validation mechanism is employed to improve detection accuracy while minimizing false positives. Additionally, the system provides automated alerts, detailed activity logs, and an administrative dashboard for efficient monitoring and analysis. Experimental evaluation conducted across 15 test cases demonstrates stable performance with reduced false positive rates, indicating the effectiveness of the proposed approach in enhancing the reliability and fairness of online examination systems.