General and special education teachers’ readiness for artificial intelligence in classrooms: A structural equation modeling study of knowledge, attitudes, and practices in select UAE public and private schools - Mohammad Fteiha; Mohammad Al-Rashaida; Mohammed Ghazal (PLoS ONE, 2025)
As artificial intelligence (AI) reshapes global education systems, understanding educators’ readiness to integrate AI into classroom practices is essential. This study examines the knowledge, attitudes, and practices (KAP) of general and special education teachers in the United Arab Emirates (UAE) regarding AI in education. Drawing on the Concerns-Based Adoption Model (CBAM) and Universal Design for Learning (UDL), we used structural equation modeling (SEM) to assess the relationships among KAP domains, including the moderating effects of demographic factors such as teaching experience, academic role, and prior exposure to AI tools. Data were collected from 161 educators in selected public and private schools across four UAE emirates, with the majority representing private and urban school settings. The findings revealed that teachers’ attitudes significantly predicted AI-related classroom practices, whereas knowledge had a weaker, but positive association. Mediation analysis further showed that knowledge had a significant indirect effect on practice through attitudes, confirming the hypothesized KAP pathway. Moderation analyses highlighted the variability in AI engagement based on gender and academic position, suggesting differentiated readiness across the subgroups. This study contributes to global conversations on teacher preparedness by offering a model for assessing institutional and pedagogical readiness for AI integration in urban school contexts. Implications for professional development, inclusive curriculum design, and educational technology policy are discussed, with relevance to digitally transforming educational systems in comparable settings.