Extending the UTAUT Model with External Variables in Inclusive Schools: An Analysis of Teacher Satisfaction and AI-STEM Use Using SEM and ANN Models

Abstract This study investigates teacher satisfaction and the adoption of AI in inclusive STEM education in Indonesia. Despite the growing emphasis on AI integration in schools, there is a lack of studies on the specific issues of inclusive education for instructors and students. Out of 50,000 people, a statistically viable sample of 1,004 inclusive school teachers and students responded. SEM and ANN are used to analyse linear and nonlinear correlations between digital culture, facilitating conditions, AI literacy, and performance expectancy. SEM research indicates that digital culture, conducive environments, AI literacy, and performance expectancy all contribute to improved teacher satisfaction, with digital culture being the most significant factor. The ANN analysis emphasizes digital culture and institutional support, ranking each predictor. These findings indicate that organizational readiness and AI literacy are more crucial than ease of use or social influence for the adoption of AI. The study emphasizes the importance of a supportive digital environment and tailored AI literacy programs to enhance teacher satisfaction and facilitate the sustainable integration of AI in inclusive STEM classrooms. Policymakers and educational leaders seeking digital transformation and equity in education should consider institutional and cultural measures to support teachers in the AI-driven learning age.