CAKT-Agent: A Context-Adaptive Knowledge Tracing Agent Based on MoE

Knowledge tracing, as the core task of Intelligent Tutoring Systems, aims to predict students' future learning performance by analyzing their historical behavioral data, and provides a fundamental basis for personalized learning path planning. Current mainstream deep learning methods are constrained by knowledge boundaries and rely on single-feature modeling, often exhibiting issues such as overly simplified learning process modeling, inadequate contextual adaptability, and black-box prediction mechanisms that yield results lacking interpretability and difficult to translate into actionable teaching references. To address these challenges, this paper proposes CAKT-Agent, a context-adaptive knowledge tracing agent based on Mixture-of-Experts model, and constructs a three-tier collaborative architecture consisting of a unified memory module, a learning agent, and a companion agent: the unified memory module realizes global storage and sharing of learning data, educational priors and intermediate model states; The learning agent defines a five-dimensional context based on educational measurement theory and cognitive science, designs a hybrid expert architecture with dimension-aware capabilities, and achieves context-adaptive integration through a two-level gating mechanism; the companion agent generates learning diagnostic reports with educational comprehensibility based on expert activation weights and educational theory rules. Experimental results on real-world datasets show that the proposed model outperforms mainstream KT models in the prediction accuracy of students' learning performance, with excellent context-adaptive capability and result comprehensibility.