Examining Generative Artificial Intelligence Adoption In Academia: A Utaut Perspective

Artificial Intelligence (AI) technology has seen rapid growth in higher education institutions over the past few years, prompting questions into the usage and underlying acceptance factors of these computer systems. This study investigates characteristics of student adoption within generative AI tools, also known as chatbots, utilizing the previously established Unified Theory of Acceptance and Use of Technology (UTAUT) model. A partial least squares regression (PLSR) model is deployed using data collected from a survey of 74 respondents to examine which UTAUT constructs are influencing undergraduate usage behavior of generative AI tools. Understanding factors of AI acceptance is of value to educators as they can design classroom interventions for adoption to enhance academic and professional potential within student populations, especially delayed users. In addition, insights developed into attributes of adoption may be of use to understand generative AI acceptance under the UTAUT framework. Results indicate that productivity gains, mentor perspective, peer usage, and broadness of tasks performed drive generative AI adoption in academic settings. Additionally, empirical results found that demographics, such as gender and age, are not factors influencing generative AI use. Future research is suggested to compare results found in this study with the Value-based Adoption Model (VAM) to corroborate characteristics of student adoption in a marginal benefit vs marginal cost trade-off setting.