Future-ready learning: Investigating students' intentions to embrace OpenAI through extended UTAUT 2

Abstract

The research paper explores the diverse factors that influence students' behavioral intentions to adopt OpenAI technologies. Utilizing a comprehensive methodology, the study surveyed 509 participants from various academic levels through both paper and online questionnaires. Structural Equation Modeling was employed for data analysis. The key determinants analyzed include effort expectancy, performance expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, perceived risk, and trust. Gender was also considered a potential moderating factor. The findings underscore the pivotal roles of trust, perceived performance benefits, and facilitating conditions in promoting the adoption of OpenAI among students. The study found strong positive correlations between these factors and students' intentions to use OpenAI technologies. Despite examining gender as a moderating factor, it did not significantly impact the relationship between these determinants and behavioral intention, indicating that these factors influence students' intentions similarly across genders. These insights are crucial for educators and policymakers who aim to foster OpenAI adoption, as they highlight the importance of building trust, demonstrating performance benefits, and ensuring supportive conditions. By addressing these areas, efforts can be more effectively directed towards promoting future-ready learning environments that integrate OpenAI technologies.