The increasing use of unfair practices in examinations has created a need for intelligent and automated monitoring systems. Traditional invigilation methods rely heavily on human supervision and conventional CCTV surveillance, which are prone to human error and inefficiency. This paper presents a review of an AI-Based Smart Exam Desk designed to enhance examination integrity using computer vision and embedded systems. The proposed system integrates a Raspberry Pi, camera modules, RF detection units, and AI-based behavior analysis to identify suspicious activities such as abnormal head movements, gaze deviation, object exchange, and unauthorized electronic device usage. Unlike traditional systems, the proposed approach focuses on real-time event-based detection and alert generation, reducing the need for continuous manual monitoring. This paper reviews existing research in AI-based cheating detection, stress monitoring, and smart surveillance systems, and highlights their limitations in practical implementation. The study emphasizes the feasibility, scalability, and cost-effectiveness of deploying such systems in Indian examination environments. This review provides a strong foundation for developing intelligent, automated, and reliable examination monitoring solutions.
Maske et al. (Fri,) studied this question.