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AI Powered Student Management System
Subject area: Science,Engineering and Technology · Area of research: Computer Science and Engineering
DOI: https://doi.org/10.64388/IREV9I6-1712469
Abstract
Traditional Student Management Systems (SMS) typically function as passive data repositories, focusing primarily on administrative record-keeping such as attendance logs and grade storage. This paper proposes the development of an AI powered Student Management System, a comprehensive web-based platform that evolves the standard SMS into a proactive educational tool using Artificial Intelligence (AI) and Machine Learning (ML). The system is architected as a decoupled full-stack application, utilizing Angular 19 for a responsive user interface, Spring Boot for robust RESTful backend services, and MySQL for relational data persistence. Security is enforced through JWT-based stateless authentication with granular Role-Based Access Control (RBAC) for Administrators, Teachers, and Students. The core innovation lies in the integration of a dedicated Python AI microservice leveraging PyTorch and Hugging Face Transformers. This integration enables four key intelligent features: (1) Performance Prediction, which utilizes historical attendance and assessment data to calculate a "risk score" for student failure, enabling early educator intervention; (2) Personalized Recommendations, which dynamically suggests remedial study resources based on identified weak subjects; (3) An Interactive NLP Chatbot, providing students with instant, context-aware responses regarding their academic schedule and records; and (4) Sentiment Analysis, which aggregates student feedback to provide qualitative insights to administration. This paper demonstrates how integrating modern web frameworks with predictive modeling can significantly enhance student engagement and academic outcomes in higher education institutions.
Keywords
Student Management System, Artificial Intelligence, Machine Learning, Educational Data Mining, Spring Boot, Angular, Performance Prediction, NLP Chatbot.
References
[1] Romero, C., & Ventura, S. (2020). Educational Data Mining and Learning Analytics: An Updated Survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355
[2] Hellas, A., Ihantola, P., Petersen, A., Ajanovski, V. V., Gutica, M., Hynninen, T., ... & Van Gorp, P. (2018). Predicting Academic Performance: A Systematic Literature Review. In Proceedings of the 2018 Companion of the ACM Conference on Innovation and Technology in Computer Science Education (pp. 175-199).
[3] Srivastava, A., & Singh, V. (2021). Student Performance Prediction using Machine Learning Algorithms. International Journal of Engineering Research & Technology (IJERT), 10(5), 234-240.
[4] Smutny, P., & Schreiberova, P. (2020). Chatbots for Learning: A Review of Educational Chatbots for the Facebook Messenger. Computers & Education, 151, 103862. https://doi.org/10.1016/j.compedu.2020.103862
[5] Hutto, C.J., & Gilbert, E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media (ICWSM-14), 216-225.
[6] Kastrati, Z., Dalipi, F., Imran, A. S., & Nuci, K. P. (2021). Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986
[7] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention Is All You Need. In Advances in Neural Information Processing Systems (pp. 5998-6008). (Foundational paper for Transformers used in the Chatbot).
[8] Spring.io. (2024). Spring Boot Documentation. Retrieved from https://spring.io/projects/spring-boot
[9] Angular.io. (2024). Angular - The Modern Web Developer's Platform. Retrieved from https://angular.io/docs
[10] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems (pp. 8024-8035).
How to cite this paper
@article{1712469,
author = {Vidyasagar Kamble, Shreyas Mane, Prof. D. J. Waghmare},
title = {AI Powered Student Management System},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {6},
pages = {16-24},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712469.pdf},
abstract = {Traditional Student Management Systems (SMS) typically function as passive data repositories, focusing primarily on administrative record-keeping such as attendance logs and grade storage. This paper proposes the development of an AI powered Student Management System, a comprehensive web-based platform that evolves the standard SMS into a proactive educational tool using Artificial Intelligence (AI) and Machine Learning (ML). The system is architected as a decoupled full-stack application, utilizing Angular 19 for a responsive user interface, Spring Boot for robust RESTful backend services, and MySQL for relational data persistence. Security is enforced through JWT-based stateless authentication with granular Role-Based Access Control (RBAC) for Administrators, Teachers, and Students. The core innovation lies in the integration of a dedicated Python AI microservice leveraging PyTorch and Hugging Face Transformers. This integration enables four key intelligent features: (1) Performance Prediction, which utilizes historical attendance and assessment data to calculate a "risk score" for student failure, enabling early educator intervention; (2) Personalized Recommendations, which dynamically suggests remedial study resources based on identified weak subjects; (3) An Interactive NLP Chatbot, providing students with instant, context-aware responses regarding their academic schedule and records; and (4) Sentiment Analysis, which aggregates student feedback to provide qualitative insights to administration. This paper demonstrates how integrating modern web frameworks with predictive modeling can significantly enhance student engagement and academic outcomes in higher education institutions.},
keywords = {Student Management System, Artificial Intelligence, Machine Learning, Educational Data Mining, Spring Boot, Angular, Performance Prediction, NLP Chatbot.},
month = {December},
doi = {https://doi.org/10.64388/IREV9I6-1712469}
}