Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study

Empowering student self-regulated learning and science education through ChatGPT: A pioneering pilot study

• Ng, Tsz Kit The University of Hong Kong
• Tan, Chee Wei Nanyang Technological University
• Leung, Jac Ka Lok Hong Kong University of Science and Technology

British Journal of Educational Technology, 55(4), 1328-1353, 2024

Wiley

2024

• G Curriculum, Teaching & Learning: Specific Disciplines
  • GD Science & Technology
    • GDC Teaching Methods & Their Outcomes
• F Learning & Teaching
  • FD Application of Communicative Media, IT in Learning & Teaching

Chatbot ChatGPT Generative AI Large Language Model Science Education Self-regulated Learning

• Hong Kong

• 1997.7 onwards

• Secondary Education

• View Full Text 全文連結

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. Copyright © 2024 The Authors.

• English

Journal Articles

• 00071013

• 10.1111/bjet.13454

https://bibliography.lib.eduhk.hk/en/bibs/bb0c49ef

2025-05-12