A Decade of Artificial Intelligence and Machine Learning in School Education: A Bibliometric Analysis (2015–2024)

Abstract

In the past decade, the use of Artificial Intelligence (AI) and Machine Learning (ML) in school education has experienced rapid growth. This study aims to analyze trends in annual scientific publications, average citations, thematic evolution, keyword co-occurrence, and author collaboration (co-authorship) related to this topic. Data were retrieved from the Scopus database for the period 2015–2024 and analyzed using Biblioshiny and VOSviewer. The analysis reveals a significant surge in publications after 2020, with a high level of global collaboration and a dominance of contributions from developed countries such as the United States and Finland. Thematic evolution reveals a shift from technical topics such as machine learning and data science toward more applied and contextual issues, including AI literacy, ChatGPT, and secondary education. Keyword mapping identifies five key clusters, emphasizing AI integration in education systems, personalized learning technologies, and the intersection of AI with social and ethical concerns. This study offers a comprehensive overview of the scholarly landscape and highlights challenges such as unequal adoption in developing countries, algorithmic bias, and the need for inclusive, ethical education policies. The findings aim to guide researchers, educators, and policymakers in promoting AI-informed teacher training, developing relevant AI literacy curricula, and fostering international collaboration to support equitable and effective use of intelligent technologies in school education.