Do We Know What They Know We Know? Calibrating Student Trust in AI and Human Responses Through Mutual Theory of Mind

2026-01-23

AI Summary

This study challenges the prevailing assumption in human–AI interaction that trust and reliance are inherently coupled, revealing their decoupling in educational contexts. Through semi-structured interviews analyzed via thematic coding within a reciprocal mindreading theoretical framework, the research identifies distinct pathways through which students exhibit high trust but low reliance on human experts—driven by social anxiety—and conversely, low trust but high reliance on AI systems, attributed to AI’s anonymity and accessibility. The findings introduce “social barriers” and “social enablers” as key mechanisms shaping reliance behaviors, demonstrating that social factors exert an independent influence on technology dependence beyond trust. This work underscores the necessity of accounting for socio-emotional dynamics when designing and deploying AI in educational settings.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• trust
• reliance
• human-AI interaction
• education
• Mutual Theory of Mind

Innovation

• Mutual Theory of Mind
• trust-reliance dissociation
• human-AI interaction
• social affordances
• epistemic evaluation