<![CDATA[The development of artificial intelligence, particularly deep learning, has brought significant changes to the field of education. This technology enables the development of adaptive learning systems and intelligent tutoring systems that can adjust learning content, methods, and pace according to learners’ characteristics and needs. This study aims to examine the potential, challenges, and strategies for implementing deep learning in education through a qualitative approach using document analysis of relevant scholarly literature. The findings indicate that deep learning has the potential to enhance the effectiveness and efficiency of learning, strengthen personalized learning, and support data-driven decision-making in educational processes. However, the implementation of this technology still faces several challenges, including limitations in technological infrastructure, low levels of digital literacy among educators and learners, and insufficient regulations related to ethics, data security, and privacy protection. Therefore, comprehensive strategies are required, including strengthening digital infrastructure, improving human resource competencies, and developing policies that support the ethical and sustainable use of artificial intelligence in education. Collaboration among governments, educational institutions, academics, and the technology industry is a key factor in ensuring that deep learning can be optimally utilized to improve the quality and equity of education in the digital era.]]>
