Related papers: Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship

Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship

URL: http://arxiv.org/abs/2504.08856v1
Date: Fri, 11 Apr 2025 05:03:12 GMT
Title: Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship
Authors: Tianyuan Yang, Ren Baofeng, Chenghao Gu, Tianjia He, Boxuan Ma, Shinichi Konomi,
Abstract summary: This paper investigates the potential of LLMs in automatically generating course concepts and their relations.<n>We provide GPT with the course information with different levels of detail, thereby generating high-quality course concepts and identifying their relations.<n>Our results demonstrate the viability of LLMs as a tool for supporting educational content selection and delivery.
Score: 0.2309018557701645
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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