Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks

2024/06/25 by Wei, Yuang, Zhou, Yizhou, Jiang, Yuan-Hao +1 · 1 citation
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Social and Information Networks (cs.SI)

paper · doi:10.48550/arxiv.2406.17518

Abstract

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 recommendation technique built upon this framework, improving teaching and learning quality while maintaining transparency in the decision-making process.

Cited by

• MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow

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