ENHANCING ONLINE PROCTORING EFFICIENCY

The transition to online examinations has necessitated robust proctoring mechanisms to ensure integrity and fairness. Traditional online proctoring systems often rely on human invigilators or basic monitoring software, both of which are prone to inefficiencies, high operational costs, and scalability challenges. This paper proposes an AI-based proctoring system that addresses two critical challenges: detecting disruptive sounds and identifying pre-existing infractions in online examination environments. The system integrates sound classification using a convolutional neural network (CNN) and visual recognition techniques for infraction detection. Extensive testing across diverse scenarios demonstrated high detection accuracy, low false positive rates, and real-time responsiveness. The results highlight the system's potential to transform online proctoring practices, ensuring reliable examination monitoring while maintaining candidate privacy and convenience.