Promoting students' creative and design thinking with generative AI-supported co-regulated learning: Evidence from digital game development projects in healthcare courses
Fostering students' problem-solving abilities and design thinking has been recognized as a crucial educational objective. In the conventional collaborative project-based learning approach, co-regulative learning (CRL) has been adopted to guide learners to plan and monitor the progress of their...
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| Published in: | Educational Technology ＆ Society Vol. 27; no. 4; pp. 487 - 502 |
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| Main Author: | |
| Format: | Journal Article |
| Language: | English |
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International Forum of Educational Technology ＆ Society
01.10.2024
International Forum of Educational Technology & Society, National Taiwan Normal University, Taiwan International Forum of Educational Technology & Society |
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| ISSN: | 1176-3647, 1436-4522, 1436-4522 |
| Online Access: | Get full text |
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| Abstract | Fostering students' problem-solving abilities and design thinking has been recognized as a crucial educational objective. In the conventional collaborative project-based learning approach, co-regulative learning (CRL) has been adopted to guide learners to plan and monitor the progress of their projects. However, without providing adequate supports or feedback to individual teams, the innovation and design quality of the project outcomes could be disappointing. In this study, a generative AI (artificial intelligence)-based co-regulative learning (GAI-CRL) approach is proposed to address this issue. Moreover, an experiment was conducted in a digital game development (DGD) to assess this approach. A total of 46 university students from two classes were recruited in this study. The experimental group adopted the GAI-CRL approach, while the control group adopted the conventional CRL (C-CRL) approach. The results showed that the GAI-CRL approach significantly enhanced students' project design performance, learning motivation, self-efficacy, and creative thinking compared to the C-CRL approach. This study serves as a reference not only for implementing DGD projects but also for applying CRL to other educational domains. |
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| AbstractList | Fostering students' problem-solving abilities and design thinking has been recognized as a crucial educational objective. In the conventional collaborative project-based learning approach, co-regulative learning (CRL) has been adopted to guide learners to plan and monitor the progress of their projects. However, without providing adequate supports or feedback to individual teams, the innovation and design quality of the project outcomes could be disappointing. In this study, a generative AI (artificial intelligence)-based co-regulative learning (GAI-CRL) approach is proposed to address this issue. Moreover, an experiment was conducted in a digital game development (DGD) to assess this approach. A total of 46 university students from two classes were recruited in this study. The experimental group adopted the GAI-CRL approach, while the control group adopted the conventional CRL (C-CRL) approach. The results showed that the GAI-CRL approach significantly enhanced students' project design performance, learning motivation, self-efficacy, and creative thinking compared to the C-CRL approach. This study serves as a reference not only for implementing DGD projects but also for applying CRL to other educational domains. Fostering students' problem-solving abilities and design thinking has been recognized as a crucial educational objective. In the conventional collaborative project-based learning approach, co-regulative learning (CRL) has been adopted to guide learners to plan and monitor the progress of their projects. However, without providing adequate supports or feedback to individual teams, the innovation and design quality of the project outcomes could be disappointing. In this study, a generative AI (artificial intelligence)-based co-regulative learning (GAI-CRL) approach is proposed to address this issue. Moreover, an experiment was conducted in a digital game development (DGD) to assess this approach. A total of 46 university students from two classes were recruited in this study. The experimental group adopted the GAI-CRL approach, while the control group adopted the conventional CRL (C-CRL) approach. The results showed that the GAI-CRL approach significantly enhanced students' project design performance, learning motivation, self- efficacy, and creative thinking compared to the C-CRL approach. This study serves as a reference not only for implementing DGD projects but also for applying CRL to other educational domains. Keywords: Digital game development (DGD), Generative AI partner, Co-regulated learning, Project-based learning, Gemini |
| Audience | Academic |
| Author | Hsin Huang |
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| SubjectTerms | co-regulated learning Computational linguistics digital game development (dgd) gemini generative ai partner Generative artificial intelligence in education: Theories, technologies, and applications Innovations Language processing Learning strategies Natural language interfaces Project-based learning Students |
| Title | Promoting students' creative and design thinking with generative AI-supported co-regulated learning: Evidence from digital game development projects in healthcare courses |
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