# Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks

arXiv: https://arxiv.org/abs/2406.17518 · HTML: https://mlsanity.com/paper/2406.17518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 35 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Graph Structure Learning for Robust Graph Neural Networks](https://mlsanity.com/paper/2005.10203/) (2020)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Towards Unsupervised Deep Graph Structure Learning](https://mlsanity.com/paper/2201.06367/) (2022)
- [Theory Refinement on Bayesian Networks](https://mlsanity.com/paper/1303.5709/) (2013)
- [A Bayesian Method for Constructing Bayesian Belief Networks from Databases](https://mlsanity.com/paper/1303.5714/) (2013)

## Built on by (most cited first, 2 of 2)

- [MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow](https://mlsanity.com/paper/2505.14126/) (2025)
- [The Advancement of Personalized Learning Potentially Accelerated by Generative AI](https://mlsanity.com/paper/2412.00691/) (2024)

## Abstract (arXiv)

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.