Paper
11 July 2024 Instructional framework for using ChatGPT to aid interdisciplinary courses
Lihua Chen, Desheng Zeng, Zhihong Pan
Author Affiliations +
Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 1321020 (2024) https://doi.org/10.1117/12.3034753
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China
Abstract
This paper explores using the AIGC tool ChatGPT to enhance interdisciplinary teaching in colleges and universities. Interdisciplinary courses, crucial for nurturing well-rounded talents, face challenges such as teachers' limited interdisciplinary knowledge, outdated teaching resources, and students' difficulties grasping complex concepts. ChatGPT offers extensive knowledge, instant response, and personalized learning to address these issues, supporting teachers with interdisciplinary teaching resources, instructional design, and assessment tools while providing students with customized learning materials. The proposed Interdisciplinary Practice Teaching Solution, comprising four core modules, facilitates the organic integration of interdisciplinary learning. A practical project, "Portfolio Analysis of Stock Funds," is used as a case study to demonstrate ChatGPT-aided interdisciplinary teaching. It showcases its effectiveness in cultivating students' abilities to deconstruct projects, clarify concepts, provide targeted hints, and critically validate and integrate projects. Experimental data confirm the framework's excellent performance across various indicators.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Lihua Chen, Desheng Zeng, and Zhihong Pan "Instructional framework for using ChatGPT to aid interdisciplinary courses", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 1321020 (11 July 2024); https://doi.org/10.1117/12.3034753
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KEYWORDS
Engineering

Data modeling

Data analysis

Statistical modeling

Statistical analysis

Visualization

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