Role of AI characteristics, AI literacy, and institutional policy in shaping generative AI use and learning outcomes among higher education students

Bibliographic Data

ID21632843
AuthorsAli Tarhini (0000-0002-8698-1764, Sultan Qaboos University, corresponding author), Mohammed A Al-Sharafi (0000-0003-0726-6031, King Fahd University of Petroleum and Minerals), Hidaya Al Lawati (0000-0001-8192-2100, Sultan Qaboos University)
Year2026
Pages1-26
Publication date2026-03-07
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2026.2628249
OpenAlexW7134133338
LanguageEN
References cited122

This study develops and empirically tests a comprehensive model explaining generative AI (GenAI) use among higher education students by integrating AI characteristics (perceived animacy, intelligence, and anthropomorphism) and user characteristics (self-efficacy, personal innovativeness, and AI literacy). It further examines the moderating role of institutional policy on the relationships between GenAI use, learning performance, and satisfaction. Survey data were collected from 509 university students in Oman and analyzed using a hybrid PLS-SEM and artificial neural network (ANN) approach. The results show that perceived intelligence, AI literacy, self-efficacy, and perceived anthropomorphism significantly influence GenAI use, with perceived intelligence emerging as the strongest predictor, while perceived animacy and personal innovativeness are not significant. GenAI use has a positive effect on learning performance and satisfaction. Moderation analysis reveals a significant negative interaction, indicating that stronger institutional policy increases overall learning performance and satisfaction but reduces the marginal impact of GenAI use on these outcomes. ANN results corroborate the SEM findings and further highlight perceived intelligence and AI literacy as the most influential predictors. The study advances understanding of GenAI adoption by combining sociotechnical and institutional perspectives and offers practical implications for universities and policymakers seeking to promote responsible and effective AI use in education

Educational technology · Electronic learning · Experiential learning · Generative grammar · Generative model · Higher education · AI in Service Interactions · Artificial Intelligence in Healthcare and Education · Ethics and Social Impacts of AI

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