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.

建立可靠的知识结构是构建有效的自适应学习系统和智能辅导系统的前提。在追求可解释和可信赖的知识结构的基础上，我们提出了一种构建因果关系知识网络的方法。该方法利用贝叶斯网络作为基础，并结合因果关系分析来推导因果网络。此外，我们介绍了一种基于该框架的可靠知识学习路径推荐技术，提升教学和学习质量，同时保持决策过程的透明度。

增强知识学习路径的可解释性：因果知识网络

Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge  Networks

Automatic causal graph construction is of high importance in medical
research. They have many applications, such as clinical trial criteria design,
where identification of confounding variables is a crucial step. The quality
bar for clinical applications is high, and the lack of public corpora is a
barrier for such studies. Large language models (LLMs) have demonstrated
impressive capabilities in natural language processing and understanding, so
applying such models in clinical settings is an attractive direction,
especially in clinical applications with complex relations between entities,
such as diseases, symptoms and treatments. Whereas, relation extraction has
already been studied using LLMs, here we present an end-to-end machine learning
solution of causal relationship analysis between aforementioned entities using
EMR notes. Additionally, in comparison to other studies, we demonstrate
extensive evaluation of the method.

本文提出一种利用 EMR 病历笔记来实现因果关系分析的机器学习方法，通过应用大型语言模型（LLMs）来解决医学研究中因果图构建的难题。