From Queries to Courses: SKYRAG’s Revolution in Learning Path Generation via Keyword-Based Document Retrieval

Abstract

Large Language Models (LLMs) hold immense potential for transforming education by automating the generation of personalized learning paths. However, traditional LLMs often suffer from hallucinations and content irrelevance. To address these challenges, we propose SKYRAG, a Separated Keyword Retrieval Augmentation Generation system that enhances the learning path generation process by integrating advanced retrieval mechanisms with LLMs. SKYRAG retrieves relevant course materials from Massive Open Online Course (MOOC) platforms, aligning them with individual learner profiles to provide personalized and coherent learning paths. Compared with Naïve RAG, SKYRAG demonstrates superior performance in terms of accuracy, relevance, and user satisfaction, as confirmed by human evaluations across four domains. By improving retrieval precision and addressing the limitations of traditional methods, SKYRAG represents a significant advancement in educational technology. This study contributes to the growing body of research on AI-driven learning systems and highlights SKYRAG’s potential for widespread adoption in dynamic educational environments.

Keywords

• Retrieval augmented generation
• personalized learning path
• large language models
• educational technology
• human-centric design