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June 18, 20260 citationsOpen Access

AI Exam Proctoring System

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GNGayathri NMMMonica M

Key Points

  • The aim is to develop an AI-based system that enhances exam integrity through effective monitoring.
  • Developed an AI Exam Proctoring System utilizing webcam and microphone inputs for real-time monitoring.
  • Applied computer vision techniques to analyze face presence, eye movement, and detect suspicious activities.
  • Integrated audio analysis to identify disturbances during the examination.
  • The system achieved improved monitoring accuracy and enhanced exam security.
  • It effectively reduced manual invigilation effort while conducting online assessments.
  • Generated alerts for rule violations and provided a final integrity report on candidate behavior.

Abstract

Online examinations and virtual interviews face major challenges in maintaining fairness, preventing cheating, and ensuring candidate attention without physical supervision. To address these issues, an AI Exam Proctoring System is developed as an intelligent monitoring solution that uses webcam and microphone inputs to observe student behavior in real time. The system applies Computer Vision and Artificial Intelligence techniques to detect face presence, eye movement, head direction, screen focus, and suspicious activities such as looking away frequently, multiple person presence, and unusual movements. It also uses audio analysis to identify background noise, talking, or external disturbances during the examination. The application continuously monitors candidate behavior, generates alerts for rule violations, and provides a final integrity report based on focus level and suspicious events. Additionally, the system helps institutions and organizations conduct secure, transparent, and automated online assessments with reduced manual invigilation effort. The results show improved monitoring accuracy, enhanced exam security, and efficient remote assessment management.

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Cite This Study

N et al. (2026) studied this question.

synapsesocial.com/papers/6a338e3f630953a74978ee9ahttps://doi.org/10.5281/zenodo.20716958
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