A Proposed Conceptual Framework for an AI-Based Educational System to Develop Teacher Preparation Programs in Colleges of Education

Abstract

This study explores the integration of Artificial Intelligence (AI) within the educational framework of Colleges of Education to enhance the quality of teacher preparation programs. As global education undergoes a profound digital transformation, traditional pedagogical methods often fail to address individual learning needs or bridge the gap between theoretical knowledge and practical application. The primary objective of this research is to design a comprehensive, AI-based conceptual system tailored specifically for teacher education. Adopting a descriptive-analytical approach, the study reviews contemporary literature to identify how AI can foster personalized learning, intelligent assessment, and data-driven decision-making. The proposed system consists of five core components: an intelligent learning platform, AI-powered tutoring systems, learning analytics, virtual training and simulation environments, and smart assessment tools. These components work in synergy to provide student-teachers with immediate feedback, adaptive content, and safe spaces for practicing classroom management. Findings indicate that such a system significantly improves teaching effectiveness and aligns teacher training with modern digital demands. However, successful implementation requires overcoming challenges related to technical infrastructure, faculty readiness, and ethical considerations regarding data privacy. The study concludes that AI integration is a strategic necessity for improving educational outcomes and recommends institutional support and continuous professional development for faculty to ensure a successful transition into AI-enhanced environments.