Trust in AI-based Educational Technologies as a Predictor of Generative AI Acceptance in Science Teacher Education: A Cross-Sectional Survey Study

As the integration of artificial intelligence (AI)–based educational technologies into teaching and learning processes accelerates, trust has emerged as a key determinant of whether preservice teachers are willing to adopt these technologies. The present study aimed to examine the relationship between the perceived trust of pre-service science teachers in AI-based educational technologies and generative AI acceptance and to determine the predictive power of trust on acceptance. Grounded in the TAM, the study employed a cross-sectional correlational survey design to investigate the role of trust in acceptance processes. Data were collected from 270 pre-service science teachers enrolled in a science teacher education program at a university via an online questionnaire. The Trust in AI-Based Educational Technologies Scale (TAI-EdTech Trust) and the Generative AI Acceptance Scale (GAIA) were used for data collection. The findings revealed a moderate, positive, and statistically significant relationship between trust in AI-based educational technologies and GA acceptance, indicating that trust significantly predicted GA acceptance. The results highlight the importance of developing content in science teacher education programs that supports the safe, ethical, and pedagogically responsible use of generative AI tools.