Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study
In recent years, AI technologies have been developed to promote students' self-regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI-based (SRLbot) and rule-based AI chatbots (Nemobot) in a 3-week science...
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sg-ntu-dr.10356-1800212024-09-13T15:36:38Z Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study Ng, Davy Tsz Kit Tan, Chee Wei Leung, Jac Ka Lok School of Computer Science and Engineering Computer and Information Science Chatbot ChatGPT In recent years, AI technologies have been developed to promote students' self-regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI-based (SRLbot) and rule-based AI chatbots (Nemobot) in a 3-week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule-based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT-enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self-regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule-based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. Nanyang Technological University Published version This project is supported by the EdeX Grant (No. 03INS001595C130) from the Centre forTeaching, Learning and Pedagogy, NTU. 2024-09-10T02:00:03Z 2024-09-10T02:00:03Z 2024 Journal Article Ng, D. T. K., Tan, C. W. & Leung, J. K. L. (2024). Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study. British Journal of Educational Technology, 55(4), 1328-1353. https://dx.doi.org/10.1111/bjet.13454 0007-1013 https://hdl.handle.net/10356/180021 10.1111/bjet.13454 2-s2.0-85189074625 4 55 1328 1353 en 03INS001595C130 British Journal of Educational Technology © 2024 The Authors. British Journal of Educational Technology published by John Wiley & Sons Ltd on behalf of British Educational Research Association. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. application/pdf |
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Computer and Information Science Chatbot ChatGPT Ng, Davy Tsz Kit Tan, Chee Wei Leung, Jac Ka Lok Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
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In recent years, AI technologies have been developed to promote students' self-regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI-based (SRLbot) and rule-based AI chatbots (Nemobot) in a 3-week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule-based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT-enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self-regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule-based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ng, Davy Tsz Kit Tan, Chee Wei Leung, Jac Ka Lok |
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Article |
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Ng, Davy Tsz Kit Tan, Chee Wei Leung, Jac Ka Lok |
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Ng, Davy Tsz Kit |
title |
Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
title_short |
Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
title_full |
Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
title_fullStr |
Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
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Empowering student self-regulated learning and science education through ChatGPT: a pioneering pilot study |
title_sort |
empowering student self-regulated learning and science education through chatgpt: a pioneering pilot study |
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2024 |
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https://hdl.handle.net/10356/180021 |
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1814047354489143296 |