Students’ Acceptance of Generative AI Tools and Their Perceived Learning Effectiveness in Higher Education: The Mediating Role of AI Literacy
Keywords:
generative AI; technology acceptance; AI literacy; perceived learning effectiveness; mediation analysis; higher educationAbstract
Although the use of generative AI tools is rapidly spreading among university students, little is known about why these tools might bring positive learning experiences to university students. This study investigates whether the perceived usefulness and perceived ease of use of generative AI tools is mediated by AI literacy with the perceived learning effectiveness of the students.This study aims to explore the mediation of AI literacy between students' acceptance of generative AI tools as perceived usefulness and perceived ease of use with the ones' perceived learning effectiveness. The survey will be a quantitative, cross-sectional online self-administered instrument that will be completed by a sample of about 270 university students (with a sampling target of about 300) who have used generative AI for academic purposes, using methods of purposive and convenience sampling. Items of the adapted technology acceptance model will be used to measure the acceptance, items from validated AI literacy scales will be used to measure the AI literacy, and items from a pilot-tested self-report scale that is explicitly different from an objective measure of learning effectiveness will be used to measure perceived learning effectiveness. Data screening, reliability assessment and confirmatory factor analysis will be performed, followed by testing the hypotheses with regression-based mediation procedures (PROCESS Model 4) with 5,000 random bootstrap resamples, with latent variable based structural equation modelling as a robustness check. A Monte Carlo power analysis revealed that there was approximately 80% power to detect indirect effects in this study of size a = b = .20 at a sample size of N = 250. The study aims to better understand the relationship between technology acceptance and perceived learning through competence-based mechanisms and to provide insights for AI-literacy programs in higher education.
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Copyright (c) 2026 Ahmed Siddique

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