Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks

A reliable knowledge structure is a prerequisite for building effective intelligent tutoring systems(ITS). To achieve an explainable and trustworthy knowledge structure, we propose a specific method for constructing causal knowledge networks. This approach leverages Bayesian networks as a foundation and incorporates causal relationship analysis to derive a causal network. Additionally, we introduce a reliable knowledge-learning path recommendation technique based on this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.