From credibility to reliance: Trust in generative AI as an academic information source

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Authors: Sanjaya Pudasaini
Cited by: 1
References: 42

Abstract

Generative AI is increasingly used by students as a source of academic information, yet less is known about how they judge, verify, and rely on AI-generated content. Grounded in Source Credibility Theory and informed by information literacy and digital verification perspectives, this study examines associations among perceived credibility, trust, verification behaviour, and reliance. Survey data from 650 undergraduate and postgraduate students across four universities in Nepal were analysed using covariance-based structural equation modelling. Perceived credibility was positively associated with trust, while trust and verification behaviour were positively associated with reliance. The direct association between perceived credibility and reliance was not significant after trust was included, whereas the indirect association through trust was significant. Because the data are cross-sectional, this pattern is interpreted as an indirect association rather than causal mediation. The study positions generative AI as an academic information source requiring credibility judgement, verification, and cautious academic incorporation.

Fields of Study & Subjects

Misinformation and Its Impacts Social Media and Politics Educational Strategies and Epistemologies

Publication details

Journal / source
Information Development
Publisher
SAGE Publications
Publication date
9 July 2026
Language
EN