<![CDATA[The advancement of artificial intelligence technologies, particularly deep learning, offers new opportunities for transforming education, including efforts to enhance digital literacy at the elementary school level (SD/MI). However, the implementation of this technology is accompanied by various complex challenges, encompassing technical, pedagogical, and policy-related aspects. This study aims to comprehensively examine the challenges, prospects, and the need for strategic integration in the application of deep learning to improve students’ digital literacy in primary education. The study employs a library research method with a descriptive qualitative approach, analyzing relevant scientific literature published within the last five years (2020–2025). The data were analyzed using content analysis techniques to identify patterns related to the implementation of deep learning in elementary education. The findings reveal that the main challenges include limited technological infrastructure, low levels of teacher competence, misalignment of pedagogical approaches, as well as ethical and data security concerns. On the other hand, deep learning demonstrates significant potential in supporting personalized, adaptive, and data-driven learning, as well as in enhancing student engagement and the quality of digital literacy. Furthermore, the successful implementation of this technology depends on strategic integration among technological, pedagogical, human resource, and policy dimensions. This study provides a conceptual contribution to understanding the dynamics of deep learning implementation in primary education and offers practical implications for developing contextual and sustainable technology-based learning innovations. Future research is recommended to conduct empirical studies to evaluate the effectiveness of deep learning implementation in real classroom settings.]]>
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