Artificial Intelligence (AI) is reshaping education by enabling personalized feedback, predictive analytics, and automated pedagogy, necessitating reforms in teacher education to equip educators with both practical skills and critical evaluation abilities. Current research largely focuses on teacher perceptions, competence, and behaviors toward AI and learning analytics, highlighting AI’s benefits in curriculum planning, real-time monitoring, assessment efficiency, and professional development initiatives. AI supports personalized learning, addresses class-wide weaknesses, and improves accessibility, especially in under-resourced settings. However, significant gaps remain, including limited device access, poor connectivity, lack of institutional AI policies (with fewer than 10% of universities having formal guidelines), and insufficient AI exposure in pre-service programs. Without structured training, AI integration risks increasing workload and anxiety. Challenges such as algorithmic bias, data privacy, misinformation, plagiarism, and chatbot inaccuracy persist, underscoring the need for robust policies, training, and ethical frameworks in teacher education. The main objective of this study is to investigate the integration of Artificial Intelligence (AI) in teacher education by examining its potential applications, impact on teacher competence and professional development, educators’ perceptions and attitudes, and the challenges and barriers that influence its effective adoption. This study follows a qualitative systematic review design, synthesizing peer-reviewed research from 2018–2025 to analyze opportunities, challenges, and frameworks for integrating AI in teacher education. A thematic synthesis approach was employed, studies were screened and analyzed for themes (e.g., AI tools, teacher competence, ethics, barriers), key findings were grouped into categories and insights were synthesized using narrative integration and cross-comparison of regional and methodological trends. Findings indicate that integrating AI in teacher education offers significant benefits, including improved instructional design, personalized professional development, adaptive learning, simulation-based practice, and enhanced teacher competence through digital and AI literacy. However, challenges such as inadequate infrastructure, ethical concerns, mixed teacher perceptions, and insufficient AI literacy training hinder effective adoption. Addressing these issues requires structured AI literacy programs, sustained professional development, institutional policies, and equity-focused infrastructure to ensure responsible and effective integration.
