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What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study

Author

Listed:
  • Yuhui Jing

    (Zhejiang University of Technology)

  • Haoming Wang

    (East China Normal University)

  • Xiaojiao Chen

    (Zhejiang University of Technology)

  • Chengliang Wang

    (East China Normal University)

Abstract

The emergence of ChatGPT has sparked new expectations for AI-empowered educational transformation. However, it remains unknown which factors affect its effectiveness in empowering learners to solve programming problems. Therefore, this study employed a quasi-experimental research design and used Python graphing in programming education as an example to investigate the factors influencing the effectiveness of learners in applying ChatGPT to problem-solving. Findings: AI literacy significantly influences learners’ effectiveness in using ChatGPT to solve problems, with AI awareness and AI usage being key factors. The knowledge base of programming language significantly affects learners’ effectiveness in applying ChatGPT to solve programming problems. Learners’ cognitive level of ChatGPT significantly influences their effectiveness in applying ChatGPT to problem-solving, while usage intention does not have a significant impact. However, learners’ intention to use ChatGPT significantly improves after application. Based on these findings, this study proposes that in the process of empowering education with Artificial Intelligence Generated Content (AIGC) products, the focus on learners should shift from cultivating their AI usage to AI literacy, laying the foundation for empowering learning with AIGC products. It is suggested to shift from mastering specific knowledge to graph-based rules as a method for empowering learning with AIGC products. Additionally, the focus should shift from enhancing learners’ intention to use the technology to strengthen their technological awareness, thereby creating practical pathways for empowering learning with AIGC products.

Suggested Citation

  • Yuhui Jing & Haoming Wang & Xiaojiao Chen & Chengliang Wang, 2024. "What factors will affect the effectiveness of using ChatGPT to solve programming problems? A quasi-experimental study," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02751-w
    DOI: 10.1057/s41599-024-02751-w
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    References listed on IDEAS

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    3. Dowling, Michael & Lucey, Brian, 2023. "ChatGPT for (Finance) research: The Bananarama Conjecture," Finance Research Letters, Elsevier, vol. 53(C).
    4. Gaur, Ajai & Kumar, Mukesh, 2018. "A systematic approach to conducting review studies: An assessment of content analysis in 25years of IB research," Journal of World Business, Elsevier, vol. 53(2), pages 280-289.
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