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

响应性、感知温暖度和拟人化对大学生使用对话式人工智能进行学习支持的影响:基于SOR框架的链式中介分析

阅读:1

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。