Prediction of the Use of Generative Artificial Intelligence Through ChatGPT Among Costa Rican University Students: A PLS Model Based on UTAUT2

Abstract

The rise in generative artificial intelligence (GenAI) is transforming education, with tools like ChatGPT enhancing learning, content creation, and academic support. This study analyzes ChatGPT’s acceptance among Costa Rican university students using the UTAUT2 model and partial least squares structural equation modeling (PLS-SEM). The research examines key predictors of AI adoption, including performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and actual usage. The findings from 194 students indicate that performance expectancy (β = 0.596, p < 0.001) is the strongest predictor of behavioral intention, followed by effort expectancy (β = 0.241, p = 0.005), while social influence (β = 0.381, p < 0.001) and facilitating conditions (β = 0.217, p = 0.008) play a smaller role. Behavioral intention significantly influences actual usage (β = 0.643, p < 0.001). Gender and age differences emerge, with male students and those aged 21–30 years showing higher acceptance levels. Despite positive attitudes toward ChatGPT, the students report insufficient training for effective use, underscoring the need for AI literacy programs and structured pedagogical strategies. This study calls for further research on AI training programs and their long-term impact on academic performance to foster responsible GenAI adoption in higher education.

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