What Shapes User Trust in ChatGPT? A Mixed-Methods Study of User Attributes, Trust Dimensions, Task Context, and Societal Perceptions among University Students

TL;DR

This study explores how university students' trust in ChatGPT is influenced by user attributes, trust dimensions, task context, and societal perceptions, highlighting the importance of transparency, perceived expertise, and societal impact on trust levels.

Contribution

It provides a comprehensive mixed-methods analysis identifying key factors affecting student trust in ChatGPT, including task-specific trust and societal perceptions, with new insights into trust dynamics in academic contexts.

Findings

Behavioral engagement increases trust.

Perceived expertise and ethical risk are primary trust predictors.

Trust varies significantly across different tasks.

Abstract

This mixed-methods inquiry examined four domains that shape university students' trust in ChatGPT: user attributes, seven delineated trust dimensions, task context, and perceived societal impact. Data were collected through a survey of 115 UK undergraduate and postgraduate students and four complementary semi-structured interviews. Behavioural engagement outweighed demographics: frequent use increased trust, whereas self-reported understanding of large-language-model mechanics reduced it. Among the dimensions, perceived expertise and ethical risk were the strongest predictors of overall trust; ease of use and transparency had secondary effects, while human-likeness and reputation were non-significant. Trust was highly task-contingent; highest for coding and summarising, lowest for entertainment and citation generation, yet confidence in ChatGPT's referencing ability, despite known inaccuracies, was the single strongest correlate of global trust, indicating automation bias. Computer-science students surpassed peers only in trusting the system for proofreading and writing, suggesting technical expertise refines rather than inflates reliance. Finally, students who viewed AI's societal impact positively reported the greatest trust, whereas mixed or negative outlooks dampened confidence. These findings show that trust in ChatGPT hinges on task verifiability, perceived competence, ethical alignment and direct experience, and they underscore the need for transparency, accuracy cues and user education when deploying LLMs in academic settings.