Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63551
Volume:
10 |
5 |
May 2026
Pages:
1-7
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.
MASTERS OF COMPUTER APPLICATIONS
Bapuji Institute of Engineering & Technology
MASTERS OF COMPUTER APPLICATIONS
Bapuji Institute of Engineering & Technology
Remote education has expanded rapidly, but ensuring academic integrity remains a significant challenge. Current online assessment tools often fail to balance security with student privacy, frequently falling short when scaled to large institutions. To address these vulnerabilities, this project develops a live proctoring ecosystem rooted in multimodal Generative AI and real- time computer vision. By automating continuous monitoring and anomaly detection, the system eliminates the need for constant human intervention while maintaining a high standard of oversight. Adaptive Intelligence vs. Rigid Rules Unlike traditional software that relies on static "if-then" logic, our platform utilizes models capable of synthesizing visual, auditory, and environmental cues simultaneously. Contextual Calibration: The AI dynamically adjusts to the student's physical environment, which drastically lowers the frequency of false flags. Live proctoring systems address this challenge by enabling real- time monitoring of online examinations through human supervisors supported by advanced technologies. Live proctoring is an advanced remote monitoring approach that ensures academic integrity by enabling real-time supervision of candidates during online examinations.
KEYWORDS
The “Revolutionizing Remote Education: Live Proctoring with Generative AI” project provides a comprehensive and forward-looking solution to the challenges posed by online examinations in today’s digital learning landscape. As remote education becomes increasingly widespread, institutions face mounting concerns related to academic integrity, fairness, and monitoring accuracyThe development of this Generative AI-Powered Proctoring Ecosystem marks a definitive departure from the rigid, surveillance-heavy models of the past. By addressing the fundamental friction between academic security and student privacy, the project provides a scalable blueprint for the future of global digital certification. The Shift from Surveillance to Contextual Intelligence The core innovation of this platform is its transition from "event-based flagging" to holistic behavioral interpretation.
1. S. D. King, A. Guyer, and R. Kessler, “Remote Proctoring in Higher Education: A Review of Challenges and Opportunities,” Journal of Online Learning Research, vol. 8, no. 2, pp. 145– 162, 2022. 2. M. Singh and P. Shukla, “AI-Driven Remote Examination Monitoring Systems: A Comprehensive Survey,” International Journal of Educational Technology, vol. 11, no. 4, pp. 55–68, 2023. 3. N. Brown and E. Larson, “Generative AI Models for Behavior Understanding: Applications in Proctoring and Surveillance,” IEEE Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 89–104, 2023. 4. J. Chatterjee, “Ethical Considerations in AI-Based Remote Proctoring,” ACM Journal on Computing and Society, vol. 7, no. 3, pp. 210–225, 2022. 5. A. Mahmood et al., “Multimodal Learning for Real-Time Behavior Analysis,” Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 6. K. Li and T. Zhao, “Privacy-Preserving Machine Learning in Real-Time Monitoring Systems,” IEEE Access, vol. 10, pp. 54321– 54335, 2022. 7. S. Das and G. Kumar, “A Review of Online Exam Integrity Challenges and Technology-Based Solutions,” Education and Information Technologies, vol. 28, pp. 1235–1256, 2023. 8. OpenAI, “Generative AI and Its Applications in Education,” OpenAI Technical Report, 2023. 9. Google Research, “Advances in Multimodal AI for Understanding Human Behavior,” Google AI Blog, 2023. 10. M. F. Ahmed, “Scalable Cloud Architectures for Remote Learning Platforms,” International Journal of Cloud Computing, vol. 14, no. 1, pp. 1–18, 2022.
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...