A study on influencing factors of generative artificial intelligence adoption by ideological and political teachers in universities

Abstract

Generative Artificial Intelligence (GenAI), as a key representative of the next generation of AI technology, is profoundly changing the way educational resources are supplied, knowledge production models, and teaching organization, providing new development opportunities for the digital transformation of ideological and political education in universities. University ideological and political education teachers, as the core subjects driving and realizing AI-driven educational reform, play a crucial role in the deep integration of AI and education. Given the limited focus of existing research on university teachers in my country, this study uses the UTAUT2 model to discuss the influencing factors and configuration paths of university ideological and political education teachers' willingness to use generative AI. SEM research found that performance expectations and AI literacy have the greatest impact on university teachers' willingness to use generative AI, while social influence, effort expectations, and facilitating conditions also have significant positive effects; gender, academic background, and job position have significant moderating effects on individual paths. Analysis using fsQCA revealed five configurations of university teachers' willingness to use generative AI. Based on this, the study suggests that universities need to provide comprehensive support to teachers at multiple levels and through multiple pathways when promoting the deep integration of AI and education