Evolutionary Game Analysis of Artificial Intelligence Such as the Generative Pre-Trained Transformer in Future Education
Yanwei You, Yuquan Chen, Yujun You, Qi Zhang, Qiang Cao
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.