In the context of artificial intelligence (AI) integration in higher education—such as AI-supported learning platforms, automated feedback and grading systems, and generative AI tools like ChatGPT—teachers’ innovative teaching behaviors (ITB) are a key factor influencing students’ learning engagement (LE). While previous research has confirmed the positive effect of ITB on LE, the underlying psychological mechanisms, especially in AI-mediated environments, remain insufficiently understood. Guided by student engagement theory and social-cognitive perspectives on technology use, this study examined the parallel mediating roles of trust in AI (AIT) and learning satisfaction (LS) in the relationship between ITB and LE. A survey was conducted among 513 vocational university students in Guangdong, China—a region with advanced digital infrastructure and early AI adoption. The results showed that ITB significantly and positively predicted LE. Both AIT and LS acted as partial mediators in this relationship, suggesting that enhancing students’ satisfaction yields slightly stronger gains in engagement than focusing on AI trust alone. The findings indicate that teachers’ innovative practices enhance engagement both directly and indirectly by fostering learning satisfaction and cultivating calibrated, critical trust in AI—that is, confidence in AI’s usefulness combined with awareness of its limitations. This study is among the first to model AI trust and learning satisfaction as parallel mediators between innovative teaching behaviors and learning engagement, thereby enriching our understanding of how pedagogical innovation and students’ psychological perceptions jointly shape engagement in the AI era. These results imply that higher education should not only encourage innovative, AI-supported teaching designs but also deliberately nurture students’ emotional experiences (satisfaction) and their thoughtful, critical trust in AI tools to maximize learning engagement.
Zhiwen et al. (Wed,) studied this question.