MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow
Project Overview
The document outlines the innovative application of a Multi-Agent System for Knowledge Component Graph Structure Learning (MAS-KCL) that leverages large language models (LLMs) and multi-agent systems to refine student learning pathways in educational settings. This approach involves constructing detailed knowledge component (KC) graphs, which enable educators to pinpoint the underlying causes of student performance challenges. By doing so, educators can customize instructional strategies to better address individual learning needs, thereby increasing the adaptability and efficiency of educational practices. The findings underscore the potential of generative AI technologies in fostering personalized learning experiences, contributing to improved educational outcomes and supporting sustainable development goals in education. Overall, the MAS-KCL system exemplifies how generative AI can transform teaching methodologies and enhance student engagement and success.
Key Applications
MAS-KCL (Multi-Agent System for Knowledge Component Graph Structure Learning)
Context: Educational settings focused on personalized learning paths for students.
Implementation: Implemented a multi-agent system that leverages LLMs for iterative optimization of KC graphs based on learners' performance data.
Outcomes: Improved accuracy in identifying learning paths, enabling teachers to design more effective instructional interventions and support learners in achieving educational goals.
Challenges: Complexity in accurately modeling the dependencies among knowledge components and the need for high-quality input data.
Implementation Barriers
Technical barrier
The inherent complexity of real-world knowledge structures and the difficulty in accurately representing them as graphs.
Proposed Solutions: Developing and enhancing algorithms like MAS-KCL that utilize multi-agent systems and LLMs to improve graph structure learning.
Data barrier
Limited availability of high-quality, labeled educational data for training and validating the models.
Proposed Solutions: Utilizing synthetic datasets alongside real-world data to validate the effectiveness of the learning algorithms.
Project Team
Yuan-Hao Jiang
Researcher
Kezong Tang
Researcher
Zi-Wei Chen
Researcher
Yuang Wei
Researcher
Tian-Yi Liu
Researcher
Jiayi Wu
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yuan-Hao Jiang, Kezong Tang, Zi-Wei Chen, Yuang Wei, Tian-Yi Liu, Jiayi Wu
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai