Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship
This addresses the labor-intensive task of course content analysis for learners and educators, but it is incremental as it applies existing LLMs to a new educational domain.
The paper tackled the problem of manually extracting course concepts and relationships by investigating GPT's ability to automatically generate them, showing that GPT can produce high-quality outputs with different levels of course detail.
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.