Artificial intelligence in primary education: systematic review (2000-2024)

Abstract

The integration of artificial intelligence (AI) into primary education has gained significant attention due to its potential to transform teaching and learning. However, its implementation lacks a solid empirical foundation to validate its pedagogical benefits at this stage of education. Existing research remains limited, raising critical questions about how AI can be effectively and pedagogically integrated into primary education. In this context, the objective of this article is to analyze the current state of research on AI in primary education, identifying trends, challenges, and significant gaps in scientific literature. A systematic review of the literature published between 2000 and April 2024 was conducted in accordance with the PRISMA protocol. The review included 15 studies sourced from WoS, Scopus, and Dialnet databases, selected based on rigorous quality and relevance criteria. Data were analyzed using tools such as VOSViewer to explore methodological, pedagogical, and bibliometric dimensions. The findings reveal a limited body of academic work focused on areas such as AI literacy, curriculum design, and technological applications. However, most studies are descriptive and exploratory, lacking experimental research to assess the actual impact of AI on learning outcomes. This review underscores the urgent need for empirical studies and the promotion of collaboration among educators, researchers, and policymakers to develop inclusive, ethical, and evidence-based curricula. Such efforts are critical to fully harnessing the educational potential of AI in primary education.