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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM60027
Volume:
10 |
4 |
April 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of CSE (Artificial Intelligence & Machine Learning),
ACE Engineering College, Ghatkesar, Hyderabad – 501 301, Telangana, India
Department of CSE (Artificial Intelligence & Machine Learning),
ACE Engineering College, Ghatkesar, Hyderabad – 501 301, Telangana, India
Department of CSE (Artificial Intelligence & Machine Learning),
ACE Engineering College, Ghatkesar, Hyderabad – 501 301, Telangana, India
Department of CSE (Artificial Intelligence & Machine Learning),
ACE Engineering College, Ghatkesar, Hyderabad – 501 301, Telangana, India
IntegrityShield is a real-time classroom activity monitoring system designed to support fair and disciplined task execution in digital learning environments such as computer laboratories and online assessments. The system enables students to log in using their roll numbers and provide explicit consent for monitoring during active sessions, while faculty members manage and observe sessions through a web-based dashboard. It tracks task- related activities such as active applications and browser usage, and identifies violations of predefined rules without accessing personal data or capturing screen content, thereby maintaining user privacy. When a violation occurs, the system generates immediate alerts for faculty to enable timely intervention. IntegrityShield follows a client–server architecture with a JavaFX-based desktop application for students, a Spring Boot backend for authentication and validation, and a web interface for faculty control. It incorporates role-based access, secure session handling, and persistent data storage, along with a lightweight classification approach to reduce false alerts. The system provides a practical and scalable solution for maintaining academic integrity while ensuring transparency and user consent.
KEYWORDS
IntegrityShield is a real-time classroom activity monitoring system designed to address the challenges of maintaining discipline and academic integrity in modern digital learning environments. By integrating system- level monitoring with secure backend technologies, the system effectively tracks student activities, enforces session-based access control, and detects violations in real time. The project demonstrates the practical implementation of a rule-based monitoring approach combined with technologies such as Spring Boot for backend services and JavaFX for the desktop interface. Through continuous tracking of processes and active windows, the system ensures that only authorized applications are used during academic sessions, thereby reducing misuse and improving discipline. IntegrityShield successfully achieves its core objective of providing a reliable, scalable, and efficient monitoring solution. The integration of real-time violation detection, structured data storage using PostgreSQL, and a centralized faculty dashboard enables effective supervision and immediate response to unauthorized activities. Furthermore, the system establishes a strong foundation for future enhancements, including intelligent monitoring, mobile integration, and advanced analytics. By combining automation, security, and real-time feedback, IntegrityShield contributes to creating a transparent and controlled academic environment. In conclusion, the project fulfills its goal of delivering a practical and efficient solution for modern classroom monitoring, ensuring fairness, accountability, and improved academic discipline while providing opportunities.
[1] Smith, D., Johnson, M., & Lee, K. (2024). AI-Based Classroom Activity Monitoring Systems. International Journal of Educational Technology, 12(2), 45–58. [2] Kumar, R., Sharma, P., & Verma, S. (2023). Role-Based Access Control in Educational Systems. Journal of Information Security, 18(3), 210–225. [3] Chen, L., Wang, Y., & Zhao, H. (2023). Lightweight Machine Learning for Activity Classification in Monitoring Systems. IEEE Access, 11, 34567–34578. [4] Patel, A., Mehta, R., & Shah, D. (2022). Secure Desktop Monitoring for Computer Laboratories. International Journal of Computer Applications, 45(6), 112–120. [5] Anderson, J., Brown, K., & Wilson, P. (2021). Privacy-Preserving Monitoring Techniques in Digital Systems. Cybersecurity Journal, 9(4), 89–102. [6] Johnson, M., Lee, S., & Kim, H. (2024). Real-Time Behavioral Analytics in Smart Classrooms. IEEE Transactions on Learning Technologies, 17(1), 66–78 . [7] Singh, P., Gupta, R., & Verma, K. (2023). Blockchain-Based Secure Logging for Student Monitoring Systems. Journal of Distributed Systems, 14(2), 130–142. [8] Wang, Y., Zhang, X., & Liu, H. (2022). Deep Learning-Based Classroom Surveillance Systems. Pattern Recognition Letters, 158, 25–34.
Publication Date
4/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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