Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909576361672704 |
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| author | Yang, Tianyuan Baofeng, Ren Gu, Chenghao He, Tianjia Ma, Boxuan Konomi, Shinichi |
| author_facet | Yang, Tianyuan Baofeng, Ren Gu, Chenghao He, Tianjia Ma, Boxuan Konomi, Shinichi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship Yang, Tianyuan Baofeng, Ren Gu, Chenghao He, Tianjia Ma, Boxuan Konomi, Shinichi Computers and Society Artificial Intelligence 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. |
| title | Examining GPT's Capability to Generate and Map Course Concepts and Their Relationship |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08856 |