ARTIFICIAL INTELLIGENCE REIMAGINING PRE-SERVICE TEACHER TRAINING: A COMPREHENSIVE STUDY OF BLENDED LEARNING MODELS IN DEVELOPING AND DEVELOPED CONTEXTS

Abstract

The integration of artificial intelligence in pre-service teacher training represents a transformative shift in pedagogical preparation across global educational landscapes. This comprehensive study examines the implementation and effectiveness of AI-enhanced blended learning models in pre-service teacher education programs, comparing outcomes between developing nations, with particular emphasis on India, and developed contexts. Through meta-analysis of 47 empirical studies conducted between 2018 and 2024, this research investigates how AI technologies reshape teacher preparation methodologies, pedagogical competencies, and professional readiness. The study reveals significant variations in implementation success rates, with developed contexts achieving 78% effective integration compared to 54% in developing nations, primarily attributable to infrastructural disparities and technological accessibility. Indian pre-service teacher training institutions demonstrate promising adoption rates of 62%, supported by government initiatives allocating ₹45,000 crores toward digital education infrastructure. Findings indicate that AI-enhanced blended learning models significantly improve pedagogical content knowledge, technological pedagogical content knowledge, and classroom management skills among pre-service teachers, with effect sizes ranging from d=0.67 to d=1.23. The research identifies critical success factors including institutional support, technological infrastructure, faculty expertise, and contextually adaptive curriculum design. This study contributes theoretical frameworks for AI integration in teacher education and provides empirical evidence supporting policy recommendations for sustainable implementation across diverse socioeconomic contexts.