This scoping review explores the application of Artificial Intelligence (AI) in personalized education by synthesizing findings from 40 peer-reviewed academic articles. The review follows the methodological framework proposed by Arksey and O’Malley, with enhancements introduced by Levac et al. and Daudt et al., to ensure a rigorous and comprehensive mapping of the field. The main objectives were to identify the types of AI technologies employed to support personalized learning, the strategies and models implemented, and the outcomes and limitations reported in the literature. Articles were selected from two curated reference documents and analyzed through a structured data matrix encompassing variables such as AI type, educational level, personalization approach, and methodological design. Results reveal a growing interest in AI-driven tools such as intelligent tutoring systems, machine learning models, and natural language processing, with applications ranging from adaptive content delivery to learner feedback personalization. However, gaps remain in the integration of pedagogical theories, ethical considerations, and empirical validation across diverse educational settings. The findings highlight both the opportunities and the current challenges in advancing AI-driven personalized learning and provide a foundation for future empirical and theoretical research in this evolving domain.