Generative artificial intelligence (GenAI) has the potential to significantly impact educational practices. Despite the apparent increase in the adoption among students, empirical research on understanding AI use and acceptance among university students is scarce. To address this gap, this study employed an extended technology acceptance model (TAM) incorporating subjective norms, perceived enjoyment, self-efficacy, and compatibility with 943 undergraduate students from 140 universities across Turkey. Findings revealed that attitude toward use, perceived usefulness, self-efficacy, and compatibility significantly predicted behavioral intention to use GenAI tools. Notably, subjective norms had a limited influence on students’ intentions to use GenAI tools, challenging traditional assumptions about social pressures in technology adoption. These insights highlight that students prioritize personal perceptions over social influences in adopting GenAI technologies. Theoretically, the study provides insights into the proposed extended TAM, by validating it in a unique socio-cultural context. Practically, the study provides recommendations on how to foster acceptance of GenAI by enhancing the usability, compatibility, and enjoyment of GenAI tools while addressing potential perceived risks and ethical concerns. The study contributes to understanding GenAI’s role in education, providing a foundation for future investigations into additional factors influencing the use and acceptance of GenAI tools in higher education settings.

Purpose – The purpose of this study is to assess the efficiency of the technology acceptance model (TAM) to explain pre-service teachers' intention to use technology in Turkey. Design/methodology/approach – A total of 197 pre-service teachers from a Turkish university completed a survey questionnaire measuring their responses to four constructs which explain their intention to use technology: attitude towards computer use, perceived usefulness, and perceived ease of use. A structural equation modeling (SEM) approach was employed for modeling and data analysis. Findings – Results revealed that the TAM is an efficient model to explain the intention to use technology of Turkish pre-service teachers. The proportion of variance explained in pre-service teachers' intention to use technology by its antecedents was 51 percent. In addition, four out of five hypotheses were supported in this study. Overall, the data in this study provided support that the TAM is a fairly efficient model with a potential to help in understanding technology acceptance pre-service teachers in Turkey. Originality/value – The TAM is a well-tested and validated model to explain the intention to use technology. However, information on its cross-cultural validity is limited. This study validated the TAM on a sample of pre-service teachers in Turkey and the results provided initial support for the cross-cultural validity of the TAM.

This study aims to explore the digital natives among a sample of pre-service teachers and in the process, examine the validity of a Turkish adaptation of the digital native assessment scale (DNAS) [Teo, T., & Fan, X. (2013). Coefficient alpha and beyond: Issues and alternatives for educational research. The Asia-Pacific Education Researcher, 22(2), 209–213]. Five hundred and fifty-seven participants completed a survey questionnaire in Turkish measuring their responses on the DNAS, developed from a proposed four-factor research. Confirmatory factor analysis and multiple indicators, multiple causes modelling were used to analyse the data and an acceptable fit was found, suggesting that the DNAS is a valid measure for participants who are older living in another culture. While the pre-service teachers had mostly regarded themselves as digital natives, significant differences were only found by years of computer use and perceived competence but no significant differences were found for gender and age.