Henan Wu, Zilin Wang, Xinyuan Wen
As generative artificial intelligence (GenAI) becomes increasingly integrated into educational contexts, learners' interactions with AI systems have emerged as a crucial determinant of learning effectiveness. Yet, prior studies have primarily emphasized performance outcomes or technology acceptance, overlooking the underlying cognitive and psychological mechanisms. Grounded in Human-Computer Interaction (HCI) and metacognitive theory, this study develops and validates a comprehensive model that positions learners' perceived interactivity with AI as the antecedent, positive and negative AI metacognition as dual mediators, and cognitive flexibility as a moderator influencing interdisciplinary learning self-efficacy. Survey data were collected from 820 university students. Structural modeling results revealed that perceived interactivity positively predicted interdisciplinary learning self-efficacy, partially through AI metacognition. Moreover, cognitive flexibility negatively moderated the link between interactivity and negative AI metacognition while strengthening the positive effects of interactivity on both positive AI metacognition and learning self-efficacy. Gender differences further indicated that females benefited more strongly from interactive AI experiences than males. These findings extend the theoretical integration of HCI and metacognitive frameworks within AI-enhanced learning environments and provide practical implications for designing interactive learning systems that promote adaptive metacognitive regulation and cognitive flexibility training.