Trust and reliance are often treated as coupled constructs in human-AI interaction research, with the assumption that calibrating trust will lead to appropriate reliance. We challenge this assumption in educational contexts, where students increasingly turn to AI for learning support. Through semi-structured interviews with graduate students (N=8) comparing AI-generated and human-generated responses, we find a systematic dissociation: students exhibit high trust but low reliance on human experts due to social barriers (fear of judgment, help-seeking anxiety), while showing low trust but high reliance on AI systems due to social affordances (accessibility, anonymity, judgment-free interaction). Using Mutual Theory of Mind as an analytical lens, we demonstrate that trust is shaped by epistemic evaluations while reliance is driven by social factors -- and these may operate independently.

翻译：在人机交互研究中，信任与依赖常被视为耦合的构念，其假设是校准信任将带来恰当的依赖。我们在教育情境中挑战这一假设——学生正日益转向AI寻求学习支持。通过对研究生（N=8）比较AI生成与人类生成回答的半结构化访谈，我们发现一种系统性分离：学生因社会性障碍（对被评判的恐惧、求助焦虑）而对人类专家表现出高信任但低依赖；同时因社会性可供性（可及性、匿名性、无评判互动）而对AI系统表现出低信任但高依赖。借助相互心智理论作为分析视角，我们证明信任由认知评价塑造，而依赖则由社会因素驱动——二者可能独立运作。