Psychological responses and cognitive mechanisms of university teachers in using generative AI in teaching: a configurational path analysis based on the MOA framework

ObjectiveThe rapid integration of generative artificial intelligence (GenAI) into educational contexts has prompted significant attention to teachers' psychological responses and cognitive mechanisms as users of AI in teaching. While existing studies often focus on linear models that examine the net effects of single factors on technology acceptance, there is a lack of research exploring the complex psychological mechanisms behind teachers' GenAI use behaviors from a multi-factorial perspective. This study, based on the Motivation, Opportunity, Ability (MOA) framework, investigates the multi-path psychological drivers of university teachers' acceptance of GenAI in teaching.MethodsA survey was conducted with 258 teachers from Longyan University who have experience using GenAI in teaching. Using fuzzy-set qualitative comparative analysis (fsQCA), the study examined how motivational factors (hedonic motivation, performance expectancy), opportunity factors (social influence, facilitating conditions, and interactivity), and ability factors (AI literacy, technical self-efficacy) interact through different combinations to trigger high levels of GenAI acceptance in teaching.ResultsThe findings reveal that no single psychological or situational factor within the MOA framework independently explains high levels of GenAI adoption. Teachers' use of GenAI results from the synergistic interaction of multiple psychological responses and cognitive conditions. Six effective configurational paths were identified, categorized into three psychological models: performance expectancy-driven under technical self-efficacy, hedonic motivation-driven under ability support, and hedonic motivation-driven in high-interaction contexts.ConclusionThis study uncovers the multi-path psychological mechanisms behind university teachers' adoption of GenAI in teaching, expanding the explanatory power of the MOA framework in educational psychology and human-AI interaction research. The results offer empirical evidence for understanding teachers' psychological responses and decision-making processes in AI-supported teaching and provide theoretical insights for promoting healthy and effective AI usage behaviors among teachers.