From AI literacy to AI-integrated inquiry-based science teaching: the serial mediating roles of AI-TPACK and science teaching self-efficacy among Chinese pre-service science teachers

Introduction: Artificial intelligence (AI) is increasingly being integrated into science education, creating new opportunities for visualization, simulation, data interpretation, inquiry-based learning, and scientific explanation. However, it remains unclear how pre-service science teachers translate AI literacy into the professional knowledge and confidence required for AI-supported inquiry-based science teaching. Drawing on AI-TPACK and teacher self-efficacy perspectives, this study examined the serial mediating roles of AI-TPACK and science teaching self-efficacy in the association between AI literacy and Chinese pre-service science teachers' intention to integrate AI into inquiry-based science teaching. Methods: A total of 548 Chinese pre-service science teachers from universities participated in the survey. Data were analyzed using partial least squares structural equation modeling. Results: AI literacy was positively associated with AI-TPACK, AI-TPACK was positively associated with science teaching self-efficacy, and science teaching self-efficacy was positively associated with AI integration intention. Further analysis supported a serial indirect pathway linking AI literacy to AI integration intention through AI-TPACK and science teaching self-efficacy. Discussion: The findings suggest that AI literacy does not automatically translate into an intention to integrate AI into teaching; rather, this association operates through contextualized technological, pedagogical, and content knowledge and positive capability beliefs. This study extends the application of the AI-TPACK framework to pre-service science teacher education and offers practical implications for systematically developing pre-service science teachers' AI-related pedagogical competence and integration confidence.