From human to AI: University students’ trust in generative AI as reference librarians
DOI:
https://doi.org/10.47989/ir31iConf64203Keywords:
Trust in AI, University students, Reference services, University libraryAbstract
Introduction. We report an investigation to compare students’ trust in GAI versus human librarians in the scenario of university libraries’ reference services and explore factors affecting students’ trust in GAI.
Method. A within-subject design experiment with five tasks covering five disciplines was carried out based on a research model of antecedents for trust in AI, including AI (trustee) characteristics, human (trustor) characteristics, and decision situation (context) characteristics. Two versions of feedback (ChatGPT versus human) were generated for each task and randomly presented to participants without indicating feedback providers’ identities.
Analysis. Quantitative analyses were carried out on the data of 146 participants, including descriptive statistics and reliability tests, bivariate correlations, and hierarchical multiple linear regression.
Results. Students’ trust in GAI versus human librarians shows no significant difference. AI characteristics emerged as the strongest predictors for students’ trust in GAI as reference librarians, especially the perceived knowledge of AI. Human and decision situation characteristics have limited effects on students’ trust in GAI.
Conclusion. The findings refine AI trust antecedents, emphasising system-centric factors over user-centric ones. It supports a shift toward designing ‘trustworthy by design’ AI systems and prioritises verifiable knowledge bases and clarified knowledge presentation ability in developing GAI-embedded reference service systems.
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Copyright (c) 2026 Di Wang , Xizhou Deng , Xinyu Lu , Jianting Guo
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