<![CDATA[This study explores the foundational theories of Deep Learning (DL), its implementation challenges, and its impact on personalization, learning adaptation, and student outcomes. Using a qualitative descriptive approach based on a literature review, secondary data was gathered from scientific articles and relevant publications. The findings highlight DL's significant potential in enhancing education quality through personalized learning, rapid feedback, and material adaptation. However, challenges such as infrastructure limitations, insufficient teacher training, and data privacy issues remain major obstacles. DL can also improve student engagement through curriculum integration. This study provides new insights into the application of DL in education and offers recommendations for further research. With collaboration among stakeholders, DL holds the potential to transform educational systems into more inclusive and adaptive frameworks]]>
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