Retrieval-Augmented Generation (Rag) Chatbots for Education

Abstract

Retrieval-Augmented Generation (RAG) has emerged as a transformative approach for enhancing the capabilities of conversational artificial intelligence by integrating large language models with external knowledge retrieval mechanisms. In the educational domain, RAG-powered chatbots address limitations of traditional AI systems, such as factual inaccuracies, outdated knowledge, and hallucinated responses, by retrieving relevant information from trusted academic resources before generating answers. This chapter examines the principles, architecture, and applications of RAG chatbots in teaching, learning, and academic support. It discusses how these systems facilitate personalized learning, intelligent tutoring, automated question answering, curriculum assistance, and institutional support while improving response accuracy, transparency, and contextual relevance. The chapter also explores the technological components of RAG systems, including embedding models, vector databases, retrieval strategies, and large language models, alongside practical implementation considerations in e Retrieval-Augmented Generation (RAG), educational chatbots, artificial intelligence in education, large language models, intelligent tutoring systems, personalized learning, vector databases, learning analytics, conversational AI, educational technology.