The influence of artificial intelligence (AI) competency and teaching self-efficacy on teachers’ ability to implement STEM learning in inclusive schools

The development of Artificial Intelligence (AI) brings new opportunities in education, particularly in supporting inclusive STEM learning. However, its implementation in inclusive schools still faces substantial challenges, especially related to teachers’ AI competency and professional confidence (self-efficacy) in integrating AI into instructional practices. This study aimed to examine the influence of AI competency and teaching self-efficacy on teachers' ability to implement STEM learning in inclusive schools. A quantitative explanatory survey design was employed. A total of 1115 inclusive school teachers in Indonesia were selected through purposive sampling. Data were collected using online questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The results indicate that AI competency has a significant influence on teachers’ ability to implement STEM learning in inclusive schools (β = 0.570, p < 0.05). Likewise, teaching self-efficacy significantly affects teachers’ ability to implement STEM (β = 0.693, p < 0.05). The proposed model demonstrates strong predictive power (R 2 = 0.792; SRMR = 0.013). This study contributes to the existing literature by empirically validating an integrated model that positions AI competency and teaching self-efficacy as critical predictors of inclusive STEM implementation. From a practical standpoint, the findings emphasize the need for targeted professional development initiatives and supportive policy frameworks to enhance teachers’ AI-related competencies and instructional confidence in inclusive educational contexts, particularly within developing countries.