<![CDATA[The development of artificial intelligence and the demands of 21st-century competencies have encouraged learning that is more meaningful, adaptive, and student-centered. This study aims to analyze the implementation of AI-based Deep Learning in optimizing the quality of education. This research used a qualitative method with a literature study approach. The findings show that AI-based Deep Learning can improve learning personalization, student engagement, conceptual understanding, learning performance analysis, and the effectiveness of assessment. The strengths of this approach lie in its ability to make learning more adaptive, interactive, and measurable. However, its implementation still faces several challenges, including limited infrastructure, teacher competence, data quality, institutional readiness, and ethical and privacy issues related to student data. This study concludes that AI-based Deep Learning has the potential to improve the quality of education when supported by the overall readiness of the education system.]]>
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