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@article{200918,
        author = {S. Sharmila and C. Initha and T. Priyanga and R. Vijayalakshmi},
        title = {Eye Gaze Tracking in Online Examination},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {40-43},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200918},
        abstract = {Despite the growing popularity of online exams, maintaining academic integrity is still very difficult. Conventional online proctoring systems rely on labor-intensive and error-prone manual monitoring or simple camera recording. In order to track students' focus during online tests, this study suggests an AI-based eye movement analysis approach. The system uses computer vision and machine learning algorithms to track head movement, eye gaze direction, and blinking rate in order to identify suspicious behavior, such as often turning away from the screen. The suggested method guarantees equitable evaluation, lessens human interference, and enhances exam security.},
        keywords = {.},
        month = {May},
        }