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Title:
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LEVERAGING CHATGPT FOR AUTOMATED
KNOWLEDGE CONCEPT GENERATION |
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Author(s):
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Tianyuan Yang, Baofeng Ren, Chenghao Gu, Boxuan Ma and Shin'ichi Konomi |
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ISBN:
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978-989-8704-61-0 |
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Editors:
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Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías |
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Year:
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2024 |
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Edition:
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Single |
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Keywords:
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Educational Data Mining, Large Language Models, Knowledge Concepts |
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Type:
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Full |
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First Page:
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75 |
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Last Page:
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82 |
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Language:
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English |
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Cover:
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Full Contents:
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Paper Abstract:
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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. |
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