INTELLIGENT LEARNING MANAGEMENT SYSTEM WITH AI-DRIVEN PERSONALIZED COURSE AND MENTOR RECOMMENDATIONS

Abstract

Learning Management Systems (LMS) have become indispensable in educational and corporate training ecosystems, providing tools for content delivery, assessments, progress tracking, and certification. However, traditional LMS platforms often lack contextual personalization, relying heavily on static course catalogs, reviews, and ratings for recommendations. This approach fails to accommodate individual learning preferences, leading to reduced engagement and higher dropout rates. This paper introduces an Intelligent Learning Management System (ILMS) enhanced with Natural Language Processing (NLP) and deep learning techniques to deliver AI-driven course and mentor recommendations. Specifically, the system employs Lexicon-Enhanced Long Short-Term Memory (LSTM) for sentiment analysis on course content, mentor reviews, and student feedback. The insights derived from this analysis enable personalized content delivery, accurate course matching, and mentor allocation based on teaching styles and student preferences. The iLMS further adapts assessments and progress tracking dynamically, optimizing the learning journey. Experimental results indicate significant improvements in user satisfaction, engagement, and learning outcomes. This solution addresses key limitations of traditional LMS platforms and contributes to more effective, tailored educational experiences.

Author

Sujitha A, Malini K, Monika S, Rajalakshmi S, Shalini S