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@article{169621,
        author = {S.L.Tambe and Devesh Patil and Yash Sable and Rohan Kamble and Aditya Patil},
        title = {A Review on ONLINE EXAMINATION PORTAL},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1788-1792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169621},
        abstract = {This research paper presents the design and development of an advanced online examination portal integrating AI-driven motion and object detection to strengthen exam security and uphold assessment integrity in remote settings. With the rapid shift toward online education, traditional proctoring methods—such as live invigilation and basic screen monitoring—often prove inadequate in preventing dishonest behaviors and are resource-intensive, requiring significant human oversight. The proposed system leverages motion detection to monitor candidate movements, identifying unusual behaviors such as leaving the camera frame, frequent gaze shifts, or excessive head movement that may indicate cheating attempts. Simultaneously, object detection technology is employed to recognize unauthorized items in the candidate’s environment, such as mobile devices, notes, or secondary screens, flagging potential infractions in real-time.
The system’s architecture is designed to be scalable, adaptable, and user-friendly, making it suitable for diverse institutions conducting online assessments at scale. The portal includes a secure interface for candidates, robust back-end AI modules, and a real-time alert and reporting system that reduces reliance on human proctors while ensuring accurate, unbiased monitoring. Experimental trials demonstrate the system’s effectiveness, accuracy, and reliability in detecting unauthorized behaviors and objects, highlighting its potential to provide a fair, privacy-compliant solution for remote examinations. By automating the proctoring process through advanced AI technology, this research addresses the limitations of traditional proctoring and contributes a viable framework for institutions seeking scalable and secure solutions for remote exams.},
        keywords = {Online examination, Motion detection, Object detection, Cheating prevention, Exam security, AI in education.},
        month = {November},
        }