<![CDATA[This study aims to examine students’ acceptance of Generative Artificial Intelligence (AI) in academic tasks using the Unified Theory of Acceptance and Use of Technology (UTAUT). The rapid integration of Generative AI tools in higher education raises important questions regarding the determinants of students’ behavioral intention and actual usage. This study argues that performance-related perceptions are the primary drivers of adoption. Design/methods/approach – A quantitative explanatory design was employed using a survey of 210 undergraduate students who had experience using Generative AI for academic purposes. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Measurement evaluation included outer loadings, Cronbach’s Alpha, Composite Reliability, and Average Variance Extracted (AVE), while structural relationships were tested using bootstrapping with 5000 resamples. Findings – Performance Expectancy significantly influenced Behavioral Intention (β = 0.41, p < 0.001), followed by Effort Expectancy (β = 0.27, p < 0.001) and Social Influence (β = 0.18, p = 0.003). Behavioral Intention strongly affected Use Behavior (β = 0.53, p < 0.001), and Facilitating Conditions also had a significant direct effect (β = 0.29, p < 0.001). The model explained 62% of the variance in Behavioral Intention and 58% in Use Behavior. Research implications/limitations – The study was limited to a single institution and relied on self-reported cross-sectional data, which may restrict generalizability and causal inference. Originality/value – This study extends UTAUT to the context of Generative AI in academic assignments and provides empirical evidence of its predictive power in emerging AI-based educational technologies.]]>
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