AceLearn: AI-Powered Learning Management System for Personalized and Automated Digital Education

Abstract

Traditional learning management systems (LMS) face several limitations, including fragmented course organization, manual assessments, and lack of personalized learning experiences. This research introduces AceLearn, an AI-powered LMS that automates course management, assessments, and certification generation while providing real-time analytics and personalized learning recommendations. The system is developed using a robust technology stack comprising React.js, Django, Spring Boot, MySQL, Flutter, and Firebase. AceLearn enhances both student engagement and administrative efficiency through intelligent tutoring, automated grading, and secure role-based access. This paper outlines AceLearn’s design, implementation, and evaluation, highlighting its potential to transform digital education.

Introduction

AceLearn is an AI-powered learning management system (LMS) designed to overcome limitations of traditional platforms by providing personalized learning paths, automated grading, certification, and streamlined administration through a unified interface. Inspired by advances in AI-driven e-learning, AceLearn addresses common issues such as lack of real-time feedback, scalability, and personalization.

The system features a modular architecture using Flutter and React.js for the frontend and Firebase, Django, and Spring Boot for the backend. AI models enable course recommendations and assessment automation, while role-based access supports students, teachers, and administrators. The platform offers real-time progress tracking, NLP chatbot assistance, a coding environment, and teacher dashboards for course management.

A pilot test with 50 students and 5 instructors showed significant improvements: grading time reduced by 60%, course completion rates increased by 30%, and student satisfaction rose to 90%. Future plans include AI-driven performance prediction, offline learning, advanced analytics, customizable reports, notification systems, and a cloud-based learning hub.

Conclusion

AceLearn introduces a smart, scalable LMS that addresses key limitations of traditional learning platforms. Through intelligent automation, adaptive learning paths, and comprehensive analytics, it enhances both teaching efficiency and student success. This research establishes AceLearn as a practical solution for modern educational demands, with potential for future integration into large-scale academic environments.

References

[1] IEEE Xplore. \"Adaptive E-Learning Systems and Their Impact on Education.\" https://ieeexplore.ieee.org [2] Springer. \"Digital Learning Platforms and Their Role in Modern Education.\" https://link.springer.com [3] Elsevier. \"Cloud-Based Learning Management Systems: A Comparative Study.\" https://www.elsevier.com [4] Firebase Documentation. https://firebase.google.com/docs [5] Flutter Documentation. https://flutter.dev/docs [6] Google Cloud for Education. https://cloud.google.com

Copyright

Copyright © 2025 Surya M, Yogeeswaran M, Sudalai Vignesh R, Sathiyamoorthi S, Dr. R Divya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.