BiLPR: Bidirectional Teacher-Student Agent Interaction for Context-Aware Learning Path Recommendation

Abstract

Learning path recommendation is a critical component of intelligent education systems, aiming to plan a personalized sequence of learning resources for each student based on their cognitive state. Existing methods predominantly rely on unidirectional modeling for recommendations, failing to adequately capture the bidirectional interaction between teachers and students. This leads to a lack of feedback-driven adaptation and difficulty in forming an effective instructional closed loop. Furthermore, current learning path recommendations are often limited to static student-exercise matching. They cannot perceive and respond to dynamic learning contexts, which results in insufficient adaptability. This limitation stems from an inadequate consideration of key contextual factors, including real-time cognitive states, interaction history, exercise semantics, and knowledge structures. To address these issues, this paper proposes a Bidirectional Teacher-Student Agent Interaction for Context-Aware Learning Path Recommendation (BiLPR), which implements a bidirectional, dynamic, and synergistic process. Specifically, the Teacher Agent integrates domain knowledge graphs with semantic reasoning to thoroughly mine features of the learning context. This enables dynamic exercise adaptation and recommendation strategies underpinned by knowledge transfer. The Student Agent simulates the evolution of dynamic cognitive states and behaviors during authentic learning processes, providing feedback on its performance. This interaction establishes a novel iterative closed loop of recommendation, feedback, and reflection. Evaluated on two real-world educational datasets, Junyi and ASSIST2009, the proposed method significantly outperforms baseline models in recommendation effectiveness. The code is available at https://github.com/czj9843/BiLPR.git.