This dissertation explores the strategies of personalized learning through AI and its effectiveness, with a focus on third-grade mathematics education. The primary objective is to investigate how AI tools can tailor learning experiences to individual needs, particularly for students who struggle academically, and thereby help reduce educational disparities. The study thoroughly examines the perceptions of both students and teachers towards AI-based learning platforms. The research is guided by two key questions: What strategies do a teacher use to provide personalized learning through AI? What are both students' and teacher's perceptions of AI in their educational environment? The findings highlight the potential of AI to enhance personalized learning and improve academic outcomes. This study also provides insights into the perceived benefits of AI systems by students and teachers, noting the value added through AI-recommended tasks and interactive features that foster motivation and engagement. Moreover, the research underscores the complementary roles of AI and teachers. While AI provides essential support in delivering personalized content, teachers remain crucial for refining curricula and meeting students' emotional and educational needs. The study also addresses the challenges faced by struggling students, emphasizing the need for diverse strategies and early intervention. Overall, this dissertation contributes significant insights into the integration of AI in education, offering practical recommendations for enhancing personalized learning and promoting educational equity. Future research should continue to explore the expansive potential of AI-enhanced education across different settings. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]