This study explores the adoption of Artificial Intelligence (AI) in STEM education by proposing a new conceptual model that integrates UTAUT 2 and GETAMEL frameworks. Data collected from 582 science teachers in Turkey were analyzed using Structural Equation Modeling. The results demonstrated that the proposed model outperformed the original GETAMEL and UTAUT 2 models in predicting teachers' intentions to adopt AI in STEM education. Key factors influencing adoption included subjective norm, experience, perceived enjoyment, anxiety, and self-efficacy, which significantly impacted perceived usefulness and perceived ease of use. These factors, in turn, positively influenced attitudes and intentions toward using AI-powered tools. Additionally, price value, facilitating conditions, and habit were identified as significant predictors of intention. The mediating roles of perceived ease of use, perceived usefulness, and attitude were confirmed in explaining adoption intentions. This model offers valuable insights for promoting effective AI integration in STEM education, aiding policymakers, educators, and researchers in understanding the factors driving technology adoption. It highlights actionable strategies for enhancing the acceptance and utilization of AI-powered tools in educational settings.
Stored only when both papers are in the corpus — 33 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.