Factors influencing the adoption of generative artificial intelligence into classroom teaching by university teachers: An empirical study using SPSS PROCESS macros - Yong Xiang; Chenxin Yang; Zhigang Jin; Wanshu Zhao (PLoS ONE, 2025)
With the development of science and technology, higher education faces the challenge of AIGC. As one of the central bodies of higher education, it is essential to understand whether teachers accept this new technology and what factors influence their choice. This study collected survey data from teachers at 42 universities in China and constructed a structural equation model based on social cognitive theory to explore the factors influencing the adoption of AIGC by university teachers. In this study, partial least squares (PLS) were used to analyze the validity and reliability of the data and process macro model 4 and model 61 based on SPSS software were used to verify the mechanism of the influence of each factor in the structural equation modeling on willingness to choose. It was found that self-efficacy is a key factor in the willing to choose from the perspective of college teachers; the positive influence of self-efficacy on willingness to choose is more significant when the level of outcome expectation is higher; external environmental factors will strengthen the positive influence of self-efficacy on outcome expectation and willing to choose; and the ability of self-improvement will also enhance the positive influence of self-efficacy on outcome expectation. This study provides an in-depth exploration of the critical factors influencing teachers’ adoption of AIGC, providing valuable insights and empirical evidence for decision-making on technology integration in education and providing educational administrators and policymakers with references on how to promote teachers’ adoption of new technologies.