# GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

arXiv: https://arxiv.org/abs/2506.22303 · HTML: https://mlsanity.com/paper/2506.22303/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2025.

## Built on by (most cited first, 6 of 6)

- [Uncertainty-aware Generative Learning Path Recommendation with Cognition-Adaptive Diffusion](https://mlsanity.com/paper/2604.14613/) (2026)
- [UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation](https://mlsanity.com/paper/2605.16750/) (2026)
- [ProPRL: Property-Aware Prerequisite Relation Learning in Educational Knowledge Graphs](https://mlsanity.com/paper/2608.03006/) (2026)
- [Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning](https://mlsanity.com/paper/2608.03123/) (2026)
- [Stochasticity Is Not the Hard Part: Reduction and Complexity in Instructional Sequencing over Prerequisite DAGs](https://mlsanity.com/paper/2608.05455/) (2026)
- [EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation](https://mlsanity.com/paper/2610.03273/) (2026)

## Abstract (arXiv)

Learning path recommendation seeks to provide learners with a structured sequence of learning items (eg, knowledge concepts or exercises) to optimize their learning efficiency. Despite significant efforts in this area, most existing methods primarily rely on prerequisite relationships, which present two major limitations: 1) Requiring prerequisite relationships between knowledge concepts, which are difficult to obtain due to the cost of expert annotation, hindering the application of current learning path recommendation methods. 2) Relying on a single, sequentially dependent knowledge structure based on prerequisite relationships implies that difficulties at any stage can cause learning blockages, which in turn disrupt subsequent learning processes. To address these challenges, we propose a novel approach, GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation (KnowLP), which enhances learning path recommendations by incorporating both prerequisite and similarity relationships between knowledge concepts. Specifically, we introduce a knowledge concept structure graph generation module EDU-GraphRAG that adaptively constructs knowledge concept structure graphs for different educational datasets, significantly improving the generalizability of learning path recommendation methods. We then propose a Discrimination Learning-driven Reinforcement Learning (DLRL) module, which mitigates the issue of blocked learning paths, further enhancing the efficacy of learning path recommendations. Finally, we conduct extensive experiments on three benchmark datasets, demonstrating that our method not only achieves state-of-the-art performance but also provides interpretable reasoning for the recommended learning paths.