Adaptive Learner-Centric LMS: Enhancing Student Engagement and Personalized Learning Experiences

Abstract

The continuous evolution of educational technology necessitates adaptive, learner-centric systems tailored to individual student needs. This paper introduces an advanced approach to enhancing student engagement and personalized learning through the integration of four key components: Personalized Quiz Generation, Adaptive Mind Map Generation, Study Techniques, and Document Assistant. Each component addresses critical challenges in modern education, including real-time adaptability, cognitive load management, and personalized learning support. The system incorporates evidence-based study techniques, such as the Pomodoro Technique and adaptive workload balancing, to optimize focus, retention, and productivity. Leveraging cutting-edge technologies such as Large Language Models (LLMs) and microservices architecture, the proposed system ensures a seamless and scalable learning environment. This paper details the system’s design, implementation, and potential educational impact, emphasizing its role in improving learning efficiency and student outcomes.