As artificial intelligence (AI) transforms workplaces, understanding how future graduates engage with AI technologies is crucial for enhancing employability. This study investigates higher education students’ familiarity with and perceptions of generative artificial intelligence (GenAI) in their learning. Using the Technology Acceptance Model (TAM) and incorporating personal innovativeness in information technology, we examined factors influencing students’ adoption of GenAI. An online survey was conducted between April 30 and May 11, 2024, with 233 students from a college in northern Israel completing the questionnaire. Results revealed significant positive correlations, supporting the study’s theoretical framework. Personal innovativeness was strongly related to TAM variables. Perceived usefulness, perceived ease of use, attitude toward use and behavioural intention to use the technology were each significant predictors of actual GenAI use. Gender and field of study influenced adoption, with both males and students studying information systems and economics showing higher usage rates. To the best of our knowledge, this study is the first to integrate TAM with personal innovativeness and demographic factors to assess student engagement with GenAI. The findings provide a theoretical and empirical foundation for understanding student responses to new technologies in higher education. The identified gender gap and field-based differences suggest that tailored approaches are necessary to enhance student engagement with GenAI tools. Overall, the findings imply that teaching practices should include scaffolded, inclusive strategies that foster GenAI literacy, adaptability and ethical awareness. Such approaches may strengthen students’ preparedness for AI-enhanced workplaces and support higher education’s role in assuring graduate employability.
Rispler et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: