The effects of responsiveness, perceived warmth, and anthropomorphism on university students' use of conversational AI for learning support: a chain mediation analysis based on S-O-R framework

Abstract

Introduction: Conversational artificial intelligence (C-AI) is increasingly used by university students for learning support, yet the mechanisms through which its affective attributes shape adoption behaviors remain insufficiently understood. Drawing on the Stimulus-Organism-Response (S-O-R) framework, this study examines how AI responsiveness, anthropomorphism, and perceived warmth influence students' adoption of C-AI through AI attachment and AI trust. Methods: A cross-sectional survey was conducted among 538 Chinese university students. The proposed model tested the relationships among AI responsiveness, anthropomorphism, perceived warmth, AI attachment, AI trust, and adoption-related learning behaviors. Results: The results showed that AI responsiveness and anthropomorphism significantly strengthened students' AI attachment and AI trust, which in turn promoted their adoption of C-AI for learning support. Perceived warmth also facilitated sustained interaction and learning engagement through attachment and trust. Overall, AI attachment and AI trust served as key mediating mechanisms linking affective AI attributes to students' learning behaviors. Discussion: The findings suggest that university students' adoption of C-AI is shaped not only by technological functionality but also by emotional and relational cues embedded in AI interaction. This study extends the S-O-R framework in the context of educational AI and offers practical implications for designing human-centered, emotionally responsive, and pedagogically effective intelligent learning systems.

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