Artificial Intelligence Tool Adoption in Higher Education: A Structural Equation Modeling Approach to Understanding Impact Factors among Economics Students

Abstract

The integration of Artificial Intelligence (AI) in higher education has the potential to significantly enhance the educational process and student outcomes. However, there is a limited understanding of the factors influencing AI adoption among university students, particularly in economic programs. This study examines the relationship between students’ perceptions of the efficacy and usefulness of AI tools, their access to these tools, and their concerns regarding AI usage. A comprehensive survey of Romanian university students, focusing on economics students, was undertaken. This study identifies critical latent factors and investigates their interrelationships by employing advanced analytical techniques, such as Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM), with robust standard errors. The results suggest that formal training and integration, concerns regarding AI, perceived utility, and positive attitudes towards AI are positively influenced by general awareness and familiarity with AI tools. The frequency of AI tool usage is substantially increased by perceived usefulness, positive attitudes, and formal training and integration. Conversely, positive attitudes and perceived utility are adversely affected by AI-related concerns. Indirect effects suggest that formal training and positive attitudes indirectly increase the usage frequency by increasing general awareness. This research is relevant to computer science, as it helps to build strategies to integrate AI technologies into educational processes. Increasing students’ awareness and access to AI tools and addressing their concerns can facilitate the widespread adoption and effective integration of AI technologies, improving academic experiences and outcomes.