Personalized Learning Path Recommendation Based on Knowledge Graphs: A Survey

The rapid development of artificial intelligence is reshaping learning concepts and instructional practices. Online learning overcomes temporal and spatial constraints, providing flexible and autonomous learning environments, and has become a central component of educational digitalization. However, the physical separation of teachers and learners makes it difficult to monitor learning progress effectively, while the abundance of learning resources often leads to learner disorientation and reduced learning efficiency. Consequently, effective planning of personalized learning paths is essential for reducing learning costs and improving learning outcomes. Traditional one-size-fits-all instructional models are insufficient to meet learners’ needs. In this context, designing transparent, adaptive, and personalized learning paths for individual learners has become an urgent research challenge. This study presents a comprehensive review of personalized learning path recommendation based on knowledge graphs. It analyzes existing methods from interdisciplinary perspectives, with particular emphasis on the theoretical role of Bloom’s taxonomy in guiding the design of learning paths. The review further summarizes core algorithm approaches, examines the characteristics and applicability of commonly used public datasets, and identifies major limitations and challenges in current research. Finally, it outlines future research directions aimed at enhancing transparency, adaptability, and explainability to support educational digital transformation and the realization of individualized instruction.