Trust in AI, Cognitive Offloading and Learning Outcomes in Higher Education: A Moderated Mediation Model of Critical AI Literacy
Keywords:
Trust in AI, Cognitive offloading, Critical AI literacy, Self-regulated learning, Higher education, PLS-SEM, Moderated mediationAbstract
Purpose – This study develops and pilots a moderated mediation model of how university students’ trust in AI-generated knowledge relates to learning outcomes through cognitive offloading, and how critical AI literacy conditions this indirect process. Design/methodology/approach – Survey data from students who use generative AI for academic work (N = 27) were analysed using partial least squares structural equation modelling (Smart-PLS 4), with the interaction estimated via the two-stage approach and significance assessed by bootstrapping. Findings – Trust positively predicted cognitive offloading. Contrary to expectation, offloading positively predicted self-reported learning; combined with a negative direct trust–learning path, this produced competitive mediation. Critical AI literacy was the strongest predictor of learning and showed a directionally consistent but non-significant buffering of the offloading–learning path, so moderated mediation was supported in direction only. Research limitations/implications – The small pilot sample underpowers the interaction; estimates are preliminary and require confirmation with roughly 160 cases and objective learning measures. Practical implications – Critical AI literacy merits treatment as a core curricular competency rather than an optional add-on. Originality/value – The paper reframes the AI-in-learning debate as a conditional process with a specified mechanism and boundary condition and validates a new instrument for confirmatory testing.
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