AI in Education: Mapping its Influence on Student Achievement through Bibliometric Insights

Abstract

This study investigates the transformative influence of artificial intelligence (AI) integration on student achievement in educational contexts between 2019 and 2025 using a bibliometric analysis approach. Data comprising 1,247 publications were retrieved from the Web of Science Core Collection and systematically analyzed using MATLAB and VOSviewer through collaboration network, co-citation, and co-word analyses. The findings reveal a significant paradigm shift toward generative AI applications after 2023, accompanied by a sharp rise in publication output over the years. Thematic mapping identifies five major research clusters, ranging from traditional machine learning approaches in earlier studies to more advanced applications of generative AI in recent years. Heat map analyses further indicate a shift in research emphasis, where generative AI contributes more substantially to creativity and critical thinking than to standardized testing outcomes. Moreover, international collaboration patterns highlight that partnerships between the United States and China achieve higher citation performance compared to single-country outputs. The study concludes by outlining future research directions in multimodal learning analytics and ethical AI frameworks, emphasizing the revolutionary potential of AI to foster higher-order cognitive skills and reshape educational practices.