Leveraging ChatGPT for Automated Knowledge Concept Generation
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| Title: | Leveraging ChatGPT for Automated Knowledge Concept Generation |
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| Language: | English |
| Authors: | Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma, Shin 'ichi Konomi |
| Source: | International Association for Development of the Information Society. 2024. |
| Availability: | International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org |
| Peer Reviewed: | Y |
| Page Count: | 8 |
| Publication Date: | 2024 |
| Document Type: | Speeches/Meeting Papers Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Concept Formation, Artificial Intelligence, Computer Uses in Education, MOOCs, Higher Education, Course Descriptions, Accuracy, Educational Technology, Natural Language Processing |
| Abstract: | As education increasingly shifts towards a technology-driven model, artificial intelligence systems like ChatGPT are gaining recognition for their potential to enhance educational support. In university education and MOOC environments, students often select courses that align with their specific needs. During this process, access to information about the knowledge concepts covered in a course can help students make more informed decisions. However, manually constructing this knowledge concept information is a labor-intensive and time-consuming task. In this paper, we explore the capability of ChatGPT in generating relevant knowledge concepts from course syllabi and evaluate the accuracy and consistency of these AI-generated concepts against course content using four assessment techniques at both the concept level and course level. We investigate the feasibility of using ChatGPT-generated concepts as a direct educational resource, as well as their potential integration into broader educational technologies, such as interpretable course recommendation systems. [For the full proceedings, see ED665357.] |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | ED665511 |
| Database: | ERIC |
| Abstract: | As education increasingly shifts towards a technology-driven model, artificial intelligence systems like ChatGPT are gaining recognition for their potential to enhance educational support. In university education and MOOC environments, students often select courses that align with their specific needs. During this process, access to information about the knowledge concepts covered in a course can help students make more informed decisions. However, manually constructing this knowledge concept information is a labor-intensive and time-consuming task. In this paper, we explore the capability of ChatGPT in generating relevant knowledge concepts from course syllabi and evaluate the accuracy and consistency of these AI-generated concepts against course content using four assessment techniques at both the concept level and course level. We investigate the feasibility of using ChatGPT-generated concepts as a direct educational resource, as well as their potential integration into broader educational technologies, such as interpretable course recommendation systems. [For the full proceedings, see ED665357.] |
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