Extracting key concepts and their relationships from course information and materials facilitates the provision of visualizations and recommendations for learners who need to select the right courses to take from a large number of courses. However, identifying and extracting themes manually is labor-intensive and time-consuming. Previous machine learning-based methods to extract relevant concepts from courses heavily rely on detailed course materials, which necessitates labor-intensive preparation of course materials. This paper investigates the potential of LLMs such as GPT in automatically generating course concepts and their relations. Specifically, we design a suite of prompts and provide GPT with the course information with different levels of detail, thereby generating high-quality course concepts and identifying their relations. Furthermore, we comprehensively evaluate the quality of the generated concepts and relationships through extensive experiments. Our results demonstrate the viability of LLMs as a tool for supporting educational content selection and delivery.
@article{arxiv.2504.08856,
title = {Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship},
author = {Tianyuan Yang and Ren Baofeng and Chenghao Gu and Tianjia He and Boxuan Ma and Shinichi Konomi},
journal= {arXiv preprint arXiv:2504.08856},
year = {2025}
}