In today’s educational landscape, personalized and adaptive learning have become critical, especially within e-learning platforms. Despite advancements, challenges remain in dynamically tailoring content to individual learners. Retrieval-Augmented Generation (RAG) offers a promising hybrid approach, merging external knowledge retrieval with generative AI to enhance personalized education. RAG allows real-time access to domain-specific information from external sources while generating custom content for learners. This paper explores how RAG can enhance e-learning by enabling real-time knowledge updates, dynamic learning pathways, and personalized content. We review architectures ranging from simple retrieval-to-generation pipelines to more advanced models incorporating knowledge graphs. While no specific solution is proposed, the integration of RAG into existing platforms is examined, along with potential challenges, such as computational complexity and data privacy concerns. This work contributes to the ongoing discussion about the future of personalized e-learning and the role of AI in shaping it, highlighting RAG’s potential to revolutionize education.