Unpacking the AI-STEM Gap: A Study of Teacher Readiness and Implementation Challenges in Rural Schools

Abstract

This study investigates the integration of Artificial Intelligence (AI) within STEM education in rural schools, focusing on teachers’ instructional readiness, technology adoption beliefs, and contextual constraints. Drawing on an explanatory, cross-sectional survey of 150 rural STEM teachers in Khanewal district, Pakistan. The research examines how Technological Pedagogical Content Knowledge (TPACK), perceptions of AI usefulness and ease of use (TAM), and institutional and infrastructural factors influence AI-STEM implementation. Reliability and correlation analyses revealed strong internal consistency across constructs, with TPACK exhibiting the highest association with AI-STEM enactment (r = .66, p < .001). Multiple regression results indicated that teachers’ instructional readiness is the strongest predictor of AI-STEM implementation, followed by technology adoption beliefs and contextual factors, collectively explaining 52% of the variance (R² = .52). The findings highlight that effective AI-STEM integration is pedagogically driven, yet moderated by teachers’ beliefs and rural-specific infrastructural challenges. Implications emphasize the necessity of targeted professional development, context-responsive frameworks, ethical and digital literacy training, and enhanced institutional support to ensure equitable and sustainable AI-STEM adoption. By adopting a socio-technical perspective, this study provides actionable insights for policymakers and educators aiming to bridge the AI-STEM gap and foster high-quality, innovative, and inclusive STEM learning in under-resourced rural schools.