Acceptance of Artificial Intelligence Tools Among Undergraduates: An Application of the Technology Acceptance Model
This study investigated the application of the Technology Acceptance Model (TAM) to assess the acceptance and adoption of Artificial Intelligence tools in educational contexts. The research focused on analyzing the attitudes of university students towards the implementation of AI technologies in teaching and learning processes. Methodology: The study used the TAM theoretical framework, focusing on two main constructs: perceived usefulness and perceived ease of use as predictors of intention to use AI tools in education. Correlational and median difference statistical analyses were applied to examine the relationships between these variables in a sample of students. Key findings: Results revealed significant correlations between perceived usefulness and intention to use AI, as well as between perceived ease of use and behavioural intention. Inferential analysis demonstrates that the external variables prior experience with technology and institutional support influence perceived usefulness, perceived ease of use and intention to use AI tools in university higher education. In addition, hierarchical regression was used to analyze the moderation of external variables in the TAM model, finding that previous experience with technology significantly enhances the relationship between perceived usefulness and intention to use (β = .35, p = .001), increasing the explained variance to 53% in the final model. On the other hand, student participants, grouped into academic faculties, show significant differences in the perception of the TAM variables. Conclusions: The study confirms the applicability of the TAM model in the educational context for AI technologies, suggesting that both perceived benefits and usability and institutional support are critical factors in promoting the successful adoption of these tools in academic settings.