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Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education

Author

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  • Yanwei You

    (School of Social Sciences, Tsinghua University, Beijing 100084, China
    Division of Sports Science & Physical Education, Tsinghua University, Beijing 100084, China
    These authors have contributed equally to this work.)

  • Yuquan Chen

    (Institute of Medical Information/Medical Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100020, China
    These authors have contributed equally to this work.)

  • Yujun You

    (School of Educational Sciences, Yangzhou University, Yangzhou 225009, China
    These authors have contributed equally to this work.)

  • Qi Zhang

    (Undergraduate Department, Taishan University, Taian 250111, China)

  • Qiang Cao

    (School of Pharmacy, Macau University of Science and Technology, Macao 999078, China)

Abstract

As an emerging research area since generative artificial intelligence (represented by Chat Generative Pre-trained Transformer (ChatGPT)) has been accessible to the public, especially in education, appropriate AI application could bring numerous benefits to education; however, its abuse has the potential to be harmful. In this paper, we aimed to explore the potential of AI in the future of education with the analytical method of evolutionary game analysis (EGA). By studying the behavior of two agents, the school and the students, EGA can be used to identify strategies that can be used to improve the effectiveness of the education model in the context of the AI era. A stable evolutionary strategy for the school and students was devised under a variety of scenarios. Additionally, we conducted a numerical analysis to further explore the impact of several key factors on the stable strategy. The results indicated that schools should adopt positive supervision to standardize the use of AI in education, and students should be more active in becoming involved in AI technology. Based on this study, we believe that the school has the ability to provide effective suggestions and practical guidelines to help students succeed academically and embrace future trends in AI education.

Suggested Citation

  • Yanwei You & Yuquan Chen & Yujun You & Qi Zhang & Qiang Cao, 2023. "Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education," Sustainability, MDPI, vol. 15(12), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9355-:d:1167770
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    References listed on IDEAS

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    1. Daniel Friedman, 1998. "On economic applications of evolutionary game theory," Journal of Evolutionary Economics, Springer, vol. 8(1), pages 15-43.
    2. Jiahuan He & Xinggang Luo & Zhongliang Zhang & Yang Yu, 2021. "Strategic Analysis of Participants in the Provision of Elderly Care Services—An Evolutionary Game Perspective," IJERPH, MDPI, vol. 18(16), pages 1-27, August.
    3. Eva A. M. van Dis & Johan Bollen & Willem Zuidema & Robert van Rooij & Claudi L. Bockting, 2023. "ChatGPT: five priorities for research," Nature, Nature, vol. 614(7947), pages 224-226, February.
    4. Elvio Accinelli & Filipe Martins & Jorge Oviedo, 2019. "Evolutionary Game Theory: A Generalization of the ESS Definition," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 1-19, December.
    5. Brian Owens, 2023. "How Nature readers are using ChatGPT," Nature, Nature, vol. 615(7950), pages 20-20, March.
    6. Dapeng Yang & Eung-Soo Oh & Yingchun Wang, 2020. "Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot," Sustainability, MDPI, vol. 12(19), pages 1-14, September.
    Full references (including those not matched with items on IDEAS)

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