<![CDATA[The integration of Artificial Intelligence (AI) into the educational landscape opens new avenues for educators to apply differentiated instruction in a more structured and efficient manner. Through AI, teachers can now run diagnostic evaluations quickly, map out students' individual learning profiles, and subsequently devise teaching strategies that truly align with each learner's readiness level, interests, and learning styles. This qualitative case study focuses on the implementation of AI-based differentiated instruction at the elementary school level. Its objective is to deeply analyze the strategies employed, their influence on the learning process and outcomes, and the practical challenges encountered in the field. The findings reveal that AI plays a critical role as a supportive tool for teachers in conducting initial assessments, automatically grouping students based on their profiles, offering a diversity of learning activities, and creating assessment rubrics based on the Criteria for Learning Goal Attainment (KKTP). Significantly, this implementation has triggered an increase in motivation, active student engagement, a more equitable grasp of the material, and it also fosters teacher creativity in lesson planning. However, several obstacles emerged in the field, including limitations in technological infrastructure, unstable electricity supply issues, varying levels of digital literacy among teachers, and the increased duration required for lesson preparation. Therefore, this study concludes that AI's potential to strengthen differentiated instruction will only be optimally realized provided that adequate infrastructure support, continuous professional training, and a strong culture of collaboration among educators are ensured.]]>
