Acceptance and Utilization of Artificial Intelligence in Student Learning: Analysis with the UTAUT Model - Ranti, Erivan Afri; Susanti, Nova; Kurniati, Erisa (Educational Leadership and Management Journal, 2026)
Artificial Intelligence (AI) has increasingly been integrated into educational environments and has transformed the way students access information, solve problems, and support learning activities. Understanding the factors that influence students’ acceptance of AI technologies is therefore essential for promoting effective and sustainable implementation. This study investigates the determinants of students’ Behavioral Intention to use Artificial Intelligence for learning by extending the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative research approach was employed, and data were collected from 257 students with experience using AI technologies for educational purposes. The proposed model incorporates Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Perceived Compatibility, Self-Efficacy, Perceived Information Quality, and Actual Use as predictors of Behavioral Intention. Data were analyzed using Structural Equation Modeling (SEM). The results indicate that Performance Expectancy, Social Influence, Facilitating Conditions, and Perceived Information Quality significantly influence students’ Behavioral Intention to use AI. In contrast, Effort Expectancy, Perceived Compatibility, Self-Efficacy, and Actual Use do not significantly affect Behavioral Intention. The findings suggest that students’ acceptance of AI is primarily driven by perceived educational benefits, social support, institutional readiness, and information quality. This study extends the application of UTAUT in the context of Artificial Intelligence in Education and provides practical implications for educators, policymakers, and technology developers seeking to promote responsible and effective AI integration in educational settings.