Education-Oriented Graph Retrieval-Augmented Generation for Learning Path Recommendation

2025-06-27

AI Summary

Learning path recommendation faces two key challenges: explicit prerequisite relationships are largely absent in most educational datasets, and overreliance on such sparse prerequisites often leads to path blocking and degraded learning outcomes. To address these issues, this paper proposes a knowledge-concept structural graph construction method that jointly models prerequisite and conceptual similarity relations. We introduce an adaptive graph construction mechanism coupled with a discriminative learning–driven reinforcement learning framework to generate dynamic, interpretable learning paths. Technically, our approach integrates graph-augmented retrieval, knowledge graph construction, and reinforcement learning to mitigate structural sparsity and path rigidity. Extensive experiments on three benchmark educational datasets demonstrate significant improvements in recommendation accuracy, generalization capability, and interpretability, achieving state-of-the-art performance.

Technology Category

• Machine Learning: Structured Learning
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning path recommendation seeks to provide learners with a structured sequence of learning items (e.g., 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) Many educational datasets do not explicitly provide prerequisite relationships between knowledge concepts, hindering the application of current learning path recommendation methods. 2) Relying solely on prerequisite relationships as the sole knowledge structure can impede learning progress and negatively impact student outcomes. To address these challenges, we propose a novel approach, Discrimination Learning Enhances Learning Path Recommendation (DLELP), 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 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) framework, 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.

Problem

• Lack of explicit prerequisite relationships in educational datasets
• Over-reliance on prerequisite relationships limits learning progress
• Need for adaptive knowledge structure graphs in path recommendation

Innovation

• Incorporates prerequisite and similarity relationships
• Adaptive knowledge concept structure graph generation
• Discrimination Learning-driven Reinforcement Learning framework