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@article{189687,
        author = {Sasikala P and Sanjay R and Shivani B and Shobhana S},
        title = {Smart Student Monitoring System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {7505-7510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189687},
        abstract = {The rapid growth of online education and remote examinations has created significant challenges in maintaining academic integrity and ensuring fair assessment practices. Traditional online examination systems largely depend on manual invigilation or basic webcam monitoring, which are inefficient, error-prone, and difficult to scale. To address these limitations, this project presents an AI-Based Smart Student Monitoring System that automates exam supervision using artificial intelligence techniques such as computer vision, behavioral analysis, and real-time activity tracking.
By using large language models trained on vast and diverse codebases, the AI code reviewer provides context-aware feedback and actionable suggestions, improving code quality and maintainability while reducing human error and manual effort. Integrated with popular development environments and version control platforms like GitHub, the tool streamlines the review workflow by embedding feedback directly into pull requests and IDEs, facilitating real time collaboration and faster development cycles. Besides improving efficiency, the system supports continuous learning from user interactions to enhance its precision and effectiveness over time.
The system continuously monitors students during examinations to detect suspicious behaviors including face absence, multiple person presence, gaze deviation, tab switching, and abnormal environmental activity. By leveraging lightweight, browser-based AI models, the system ensures privacy-preserving monitoring without requiring additional software installations. Real-time alerts, detailed violation reports, and adaptive behavior analysis enhance fairness while reducing human effort. The proposed system provides a scalable, secure, and efficient solution for maintaining examination integrity in modern digital learning environments.},
        keywords = {},
        month = {December},
        }