Online Proctoring System for Secure Remote Examinations Using MERN Stack

The increase in remote learning and digital examination models has created a strong need for secure and reliable online proctoring systems. Traditional face-to-face invigilation is not feasible in virtual environments, and institutions must ensure that students are evaluated fairly without cheating, impersonation, or unauthorized assistance. This review paper explores the potential of the MERN stack (MongoDB, Express, React, Node.js) for developing an online proctoring system capable of webcam monitoring, microphone audio analysis, browser activity tracking, and suspicious event logging. The paper examines existing proctoring methods, technological requirements, software architecture, and the advantages and disadvantages of this approach. The discussion highlights ethical considerations and future enhancements including artificial intelligence (AI), behavior analytics, and privacy-preserving security mechanisms. The findings indicate that a MERN-based proctoring system can provide a scalable and cost-effective solution for secure remote examinations and support academic integrity in online learning contexts.