Email this article Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities
), Muhammad Ridha(2), Jamiu Temitope Sulaimon(3)
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(1) Elementary School Teacher Education Study Program, Universitas Ahmad Dahlan, Indonesia
(2) Elementary School Teacher Education Study Program, Universitas Ahmad Dahlan, Indonesia
(3) Department of Adult and Primary Education, University of Ilorin, Nigeria
Corresponding Author
10.23960/jpp.v15i3.pp1785-1810
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Copyright (c) 2025 Ricki Fahma Adi Saputra, Muhammad Ridha
Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities. Objectives: Despite significant advances in healthcare and finance, Deep Learning (DL) remains underutilized in primary education, with only about 12% of AI-related studies focusing on K–6. Given the formative role of early schooling in cognitive and social development, this systematic review analyzed recent empirical studies to identify key trends, challenges, and opportunities in applying DL to primary education. Methods: Following the PRISMA 2020 guidelines, 21 studies published between 2021 and 2025 across 11 countries were analyzed using a structured coding sheet and further examined with bibliometric mapping, Microsoft Excel, and Python-based visualization tools. The review included diverse sources focusing on DL use cases across global primary education systems. Findings: The findings reveal a global increase in DL adoption, led by China and South Korea, with growing contributions from Indonesia, India, and the Philippines. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer-based models are among the most commonly applied architectures to tasks such as personalized learning, early detection of learning difficulties, assessment automation, and curriculum development. However, there are still problems in the context of ethical concerns, including data privacy, algorithmic bias, and equity of access. Technical barriers involve dataset complexity, model generalization, and resource limitations, while pedagogical issues center on aligning DL applications with developmental needs and classroom realities. Despite these obstacles, DL demonstrates significant potential to enhance personalization, foster engagement, and support holistic educational outcomes. Conclusion: This review contributes a strategic roadmap for integrating DL in primary education by balancing innovation with pedagogy and ethics. Future research should prioritize cross-disciplinary collaboration, greater geographic diversity, and improvements in scalability and interpretability to ensure DL supports equitable, future-ready learning.
Keywords: deep learning, primary education, personalized learning, systematic literature review, educational technology.
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