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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65189
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
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 Computer Engineering
JSPM Narhe Technical Campus , Pune.
Department of Computer Engineering
JSPM Narhe Technical Campus , Pune.
Department of Computer Engineering
JSPM Narhe Technical Campus , Pune.
Department of Computer Engineering
JSPM Narhe Technical Campus , Pune.
Department of Computer Engineering
JSPM Narhe Technical Campus , Pune.
The rapid expansion of digital education has created a strong demand for intelligent, scalable, and interactive platforms capable of supporting both learning and evaluation processes. This paper presents CourseMate, an AI-enabled Learning Management System (LMS) integrated, implemented using the MERN stack consisting of MongoDB, Express.js, React.js, and Node.js. The proposed system provides a unified framework for course administration, digital content delivery, learner progress analysis, and AI-assisted interview evaluation. Core functionalities include secure authentication, role-based access control, interactive learning modules, and automated feedback generation during interview sessions. The MERN architecture ensures high performance, responsiveness, and scalability, making the system suitable for academic institutions and training organizations. Experimental observations indicate that CourseMate improves assessment efficiency, reduces manual evaluation effort, and delivers reliable real-time feedback to users.
KEYWORDS
CourseMate presents an intelligent and scalable Learning Management System implemented using the MERN stack. The integration of Artificial Intelligence enhances user interaction and provides personalized support to learners. Experimental analysis confirms the efficiency and reliability of the system in terms of accuracy and response time. The proposed platform effectively addresses the limitations of conventional LMS systems by combining content management with intelligent features. Future enhancements may include recommendation systems, AI chatbots, speech-based interaction, predictive analytics, mobile application support, and cloud-based deployment for handling large-scale educational environments.
1. R. S. Pressman, Software Engineering: A Practitioner's Approach, 8th ed., McGraw Hill Education, 2015. 2. I. Sommerville, Software Engineering, 10th ed., Pearson Education, 2016. 3. A. Banks and E. Porcello, Learning React: Modern Patterns for Developing React Apps, O'Reilly Media, 2020. 4. K. Chodorow, MongoDB: The Definitive Guide, 3rd ed., O'Reilly Media, 2019. 5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed., Pearson Education, 2021. 6. T. M. Mitchell, Machine Learning, McGraw-Hill Education. 7. P. Brusilovsky, "Adaptive and Intelligent Educational Systems," International Journal of Artificial Intelligence in Education, 2003. 8. R. Ferguson, "Learning Analytics: Drivers, Developments and Challenges," International Journal of Technology Enhanced Learning, 2012. 9. React.js Documentation. Available: https://react.dev/ 10. Node.js Documentation. Available: https://nodejs.org/
Publication Date
6/19/2026
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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No funding information available for this research.
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