International Journal of Advanced Research in Computer and Communication Engineering

Abstract: This paper presents a novel Adaptive E-Learning framework designed to enhance student engagement and content retention through hyper-personalization. By combining Large Language Models (LLMs) via the Groq API for dynamic study planning and the Tavily Search API for real-time resource curation, the system addresses the critical challenges of information overload and static curriculum delivery. This multi-modal approach integrates a Retrieval-Augmented Generation (RAG) pipeline, allowing students to interact with PDF textbooks contextually ("Chat with PDF") while ensuring high factual accuracy. The system features a "white-box" approach to content delivery, where every AI-generated answer is cited from the user's uploaded material. Additionally, the platform includes a dynamic resource curator that filters web content to reduce cognitive load, bridging the gap between open-ended internet search and structured academic learning.

Keywords: Adaptive Learning, Retrieval-Augmented Generation (RAG), Large Language Models (LLM), Personalized Education, Cognitive Load Management, MERN Stack.

Downloads: | DOI: 10.17148/IJARCCE.2026.151113