A reliable knowledge structure is a prerequisite for building effective adaptive learning systems and intelligent tutoring systems. Pursuing an explainable and trustworthy knowledge structure, we propose a 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 dependable knowledge-learning path recommendationHuman-Centric eXplainable AI in Education technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.

翻译：可靠的知识结构是构建有效自适应学习系统和智能辅导系统的先决条件。为追求可解释且可信的知识结构，本文提出一种构建因果知识网络的方法。该方法以贝叶斯网络为基础，结合因果关系分析推导出因果网络。此外，我们基于该框架引入一种可靠的知识学习路径推荐技术——教育领域中以人为中心的可解释人工智能技术，在保持决策过程透明性的同时，提升教学与学习质量。