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

arXiv: https://arxiv.org/abs/2505.14126 · HTML: https://mlsanity.com/paper/2505.14126/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 8 of 8)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [AI Agent for Education: von Neumann Multi-Agent System Framework](https://mlsanity.com/paper/2501.00083/) (2024)
- [Graph Neural Networks in Vision-Language Image Understanding: A Survey](https://mlsanity.com/paper/2303.03761/) (2023)
- [TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution](https://mlsanity.com/paper/2306.06669/) (2023)
- [A Survey of Distributed Graph Algorithms on Massive Graphs](https://mlsanity.com/paper/2404.06037/) (2024)
- [Enhancing Explainability of Knowledge Learning Paths: Causal Knowledge Networks](https://mlsanity.com/paper/2406.17518/) (2024)
- [Counting Butterflies over Streaming Bipartite Graphs with Duplicate Edges](https://mlsanity.com/paper/2412.11488/) (2024)

## Built on by (most cited first, 3 of 3)

- [How Real Is AI Tutoring? Comparing Simulated and Human Dialogues in One-on-One Instruction](https://mlsanity.com/paper/2509.01914/) (2025)
- [DiaCDM: Cognitive Diagnosis in Teacher-Student Dialogues using the Initiation-Response-Evaluation Framework](https://mlsanity.com/paper/2509.24821/) (2025)
- [Agentic Workflow for Education: Concepts and Applications](https://mlsanity.com/paper/2509.01517/) (2025)

## Abstract (arXiv)

Knowledge components (KCs) are the fundamental units of knowledge in the field of education. A KC graph illustrates the relationships and dependencies between KCs. An accurate KC graph can assist educators in identifying the root causes of learners' poor performance on specific KCs, thereby enabling targeted instructional interventions. To achieve this, we have developed a KC graph structure learning algorithm, named MAS-KCL, which employs a multi-agent system driven by large language models for adaptive modification and optimization of the KC graph. Additionally, a bidirectional feedback mechanism is integrated into the algorithm, where AI agents leverage this mechanism to assess the value of edges within the KC graph and adjust the distribution of generation probabilities for different edges, thereby accelerating the efficiency of structure learning. We applied the proposed algorithm to 5 synthetic datasets and 4 real-world educational datasets, and experimental results validate its effectiveness in learning path recognition. By accurately identifying learners' learning paths, teachers are able to design more comprehensive learning plans, enabling learners to achieve their educational goals more effectively, thus promoting the sustainable development of education.