COURSE FORGE AI IN COMPUTERS: AN AGENTIC, MODULAR, AND EXPLAINABLE EDUCATIONAL ASSISTANT USING LLMS AND RAG

The demand for personalized, interactives learning remains a core challenge in modern education. This paper presents CourseForge AI, a modular and explainable academic assistant designed using Large Language Models (LLMs), autonomous agents, and Retrieval-Augmented Generation (RAG). The system includes specialized agents for curriculum-aligned Question and Answer, note generation, flashcards, tutoring, and coding assistance and all powered by semantic retrieval over academic content. Unlike static chatbots, Course Forge AI exhibits agentic reasoning and decision-making, allowing it to dynamically retrieve and contextualize content in response to user input. By leveraging vector-based semantic search model includes LanceDB and explainable agent workflows through Phidata, the system ensures that its outputs are grounded, interpretable, and relevant to syllabus-specific learning. Experimental validation shows strong performance in user engagement, relevance, and accuracy. This work contributes a scalable blueprint for AI-assisted education that is both intelligent and trustworthy