A dual-pathway model of teacher-AI collaboration based on the job demands-resources theory

Artificial intelligence (AI) not only provides new opportunities for the high-quality development of education, but also brings multiple challenges such as psychological anxiety, workload, and role conflicts to teachers. In order to explore the complex interaction mechanism between teachers and AI in the process of human-machine collaboration, this study draws on the job demands-resources (JD-R) model and proposes a dual-pathway model. SPSS-PROCESS Macro software was used to test the research hypotheses. This study found that: (1) AI had both negative and positive effects on teacher-AI collaboration (TAC). Perceived risk, through the partial mediating effect of AI anxiety, negatively influenced TAC. Technology acceptance and AI-TPACK had a positive influence on TAC through the mediation of human-machine compatibility. (2) The support of facilitating conditions buffered the positive association between perceived risk and AI anxiety. (3) Technology acceptance helped teachers cope with AI anxiety induced by perceived risk, and perceived risks, to some extent, promoted the improvement of teachers’ human-machine compatibility brought about by technology acceptance. The present study reveals the double-edged sword effect of AI faced by teachers, providing strong support for the constructive interaction and deep integration between teachers and AI.