Retrieval-Augmented Generation: Revolutionizing Personalized and Accessible Education
Abstract
Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing, yet their deployment in education is hindered by hallucinations and limited ability to process multimodal course content. Retrieval-Augmented Generation (RAG) addresses these issues by grounding model outputs in external knowledge, but existing systems often focus solely on text and overlook the importance of visual elements in learning materials. This paper proposes a Multimodal RAG (MuRAG) framework designed for educational platforms that host lecture videos, slides, and multilingual resources. The system leverages video transcripts as structured retrieval documents while simultaneously enabling visual data extraction and retrieval for graphs, charts, and other slide content. By aligning text and image modalities through joint embeddings, our framework preserves text–image continuity, reduces hallucinations, and delivers domain-specific, context-aware responses. Multilingual query support further ensures accessibility for diverse learners. Experimental evaluation on multiple educational QA formats demonstrates improved accuracy, relevance, and multimodal reasoning compared to baseline RAG approaches. This work highlights the potential of MuRAG to enable intelligent, visually aware, and inclusive AI tutors that enhance personalized learning experiences.
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