ArticleFrontiers in psychology2026
AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: As generative artificial intelligence (GenAI) increasingly enters higher education, understanding how teachers integrate GenAI into innovative teaching is critical. Prior research has mainly focused on intentions to use AI tools. Guided by the AI-Technological Pedagogical Content Knowledge (AI-TPACK) framework, Social Cognitive Theory, and professional identity theory, this study explored the relationships surrounding GenAI-supported teaching innovation behavior, with a focus on teaching self-efficacy, professional identity, and AI literacy. Methods: A total of 898 Chinese university teachers from eight comprehensive universities completed standardized scales assessing AI-TPACK dimensions, teaching self-efficacy, professional identity, AI literacy, and AI teaching innovation behavior. Data were analyzed via structural equation modeling and multi-group analysis. Results: The results showed that among the AI-TPACK dimensions, all except AI technological knowledge and integrative knowledge were significantly associated with AI teaching innovation behavior. Professional identity and AI literacy partially mediated the relationship between competence structures and innovative behavior, whereas the mediating effect of teaching self-efficacy was only partially supported. Further multi-group analysis showed that the relationship between professional identity and innovative behavior was stronger among teachers with lower reported frequencies of GenAI tool use. Discussion: These findings provide empirical evidence on the associations of competence- and psychology-related factors with GenAI-supported teaching innovation. The study highlights the roles of professional identity, AI literacy, and teaching self-efficacy in promoting innovative teaching and offers practical implications for faculty development and AI integration in higher education.
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