Date
Publisher
arXiv
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.
Study at a glance
- Question: How well can GPT identify and map course concepts and their relationships using course information?
- Setting and data: Not stated in the abstract
- Method: Experiments testing how well GPT generates course concepts and their relationships using prompts with different levels of course detail
Why it matters
Extracting key concepts to recommend courses to learners is labor-intensive and time-consuming. Previous machine learning methods also rely heavily on detailed course materials, which requires labor-intensive preparation.
Key findings
- For learners choosing courses, GPT generated high-quality course concepts and identified their relationships directly from course information.
- The model successfully extracted concepts and their connections using course information provided at different levels of detail.
- Large language models proved viable as tools for supporting educational content selection and delivery.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
