FACTORS AFFECTING TEACHERS' ACCEPTANCE OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES: ANALYZING TEACHER PERSPECTIVES WITH STRUCTURAL EQUATION MODELING

Recent rapid advances in artificial intelligence (AI) technologies have brought to the agenda how to encourage the use of these technologies in education. Teachers' acceptance of artificial intelligence technologies has an important place in this context. This study, based on the Technology Acceptance Model (TAM), investigates the factors affecting teachers' acceptance of artificial intelligence technologies. For this purpose, a five-structure structural model for AI technology was proposed by adding Self-Efficacy and Anxiety to TAM. Data were obtained from teachers working in Turkey. A trial form consisting of 21 items was prepared for EFA. 18 items were confirmed by CFA. Structural Equation Modeling (SEM) was used to analyze the data. In the proposed model, 7 hypotheses related to Self-Efficacy (SE), Artificial Intelligence Anxiety (AIA), Perceived Ease of Use (PEU), Perceived Utility (PU) and Behavioral Intention (BI) were tested. Six of the seven proposed hypotheses were confirmed while one was not. There was a significant negative effect of teachers' SE on their AIA, AIA on their PEU, and AIA on their BI (H1, H2, H7). In addition, a significant positive effect of teachers' PEU on their PU, SE on their BI and PU on their BI was found (H3, H4, H6). However, the hypothesis (H5) that teachers' PEU has a significant positive effect on their BI was not supported. In the study, it was found that teachers' acceptance of using AI technologies in teaching is predictable by teachers' self-efficacy towards AI, AI anxiety and perceived usefulness. The results of this study contributed to the extension of TAM. In addition, the results of the study can help future educational planning in the use of educational technologies.