Examination of teachers' acceptance levels towards using artificial intelligence technologies in the context of the technology acceptance model

The purpose of this study is to determine teachers' acceptance levels of artificial intelligence (AI) technologies within the framework of the Technology Acceptance Model (TAM), and to examine how these levels differ according to various variables. The research population consists of teachers working in public and private schools in Diyarbakır and its districts during the 2023–2024 academic year. The sample includes a total of 1,212 teachers from different subject areas selected through random sampling; 307 of them participated in the scale development phase, and 905 took part in the implementation phase. The study employed a predictive correlational research design as one of the quantitative research methods. Data were collected using the Artificial Intelligence Technologies Acceptance Scale, developed by the researcher, and analyzed using Structural Equation Modeling (SEM). The findings indicate that teachers have overall positive acceptance levels toward AI technologies. In particular, the mean scores for perceived usefulness (M = 3.80), attitude (M = 3.82) and intention (M = 3.84) demonstrate that teachers hold a favorable perception of AI technologies and are willing to use them. However, the mean scores for perceived ease of use (M = 3.42), self-efficacy (M = 3.40) and subjective norm (M = 3.27) point to a more limited level of acceptance in these dimensions. The analyses revealed a significant difference in attitude in favor of female teachers in relation to gender. Regarding educational level, significant differences were found in perceived usefulness, attitude, and intention in favor of preschool, primary, and middle school teachers. Teachers working in private schools showed significantly higher acceptance in terms of perceived ease of use, self-efficacy, and anxiety dimensions. Teachers who use computers had significantly higher acceptance levels than those who do not. In terms of subject areas, teachers in vocational and technical fields exhibited higher acceptance levels compared to other branches; additionally, in the attitude dimension, language teachers scored higher than social sciences teachers. Educational attainment was found to have no significant effect on AI technology acceptance. Increasing age negatively affected perceived usefulness, whereas teachers with higher technological literacy saw AI technologies as more beneficial and were more eager to use them. Conversely, teachers with higher levels of professional burnout had lower acceptance levels. Overall, the results contribute to understanding the key factors shaping teachers' technology adoption decisions and provide valuable insights for future research. Furthermore, the introduction of a valid and reliable measurement tool offers strong support for both theoretical and applied studies in the field. The study also contributes to the extension of TAM within the educational context and provides an original and current contribution to the literature on the integration of AI technologies in education.