An Intelligent AI-Based Examination Monitoring System Using Face Movement Detection: Design, Implementation, and Evaluation in Nigerian CBT Centres

Computer-Based Testing (CBT) has become widely adopted in higher educational institutions due to its efficiency, scalability, and rapid assessment capabilities. However, maintaining examination integrity during CBT sessions remains a major challenge because most existing invigilation systems rely heavily on manual supervision and conventional ClosedCircuit Television (CCTV) monitoring, which are often inefficient, time-consuming, and prone to human error. This study presents the design and implementation of an intelligent examination monitoring system using face movement detection techniques for real-time detection of suspicious behaviours during CBT examinations. The proposed system integrates artificial intelligence, computer vision, and face recognition technologies to monitor examinees’ facial orientation, head movement, and behavioural patterns during examinations. The system was developed using Python, OpenCV, and machine learning algorithms and was tested across selected CBT centres in Katsina State, Nigeria.