Bibliometric analysis of scientific publications on artificial intelligence in educational management and leadership: Web of Science and Scopus sources between 1986-2026

Oct 15, 2026

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This study maps the academic literature at the intersection of artificial intelligence and educational management/leadership using bibliometric methods on 1,072 articles from Web of Science and Scopus. Analyses with R and Biblioshiny show the field is exceptionally young (average document age 2.7 years) and that output rose sharply after 2020, likely linked to COVID-19 and generative AI diffusion. Publication geography shifts toward an Eurasian axis; at the institutional level Kazan Federal University and RUDN University dominate, while China leads citation impact. Keyword and thematic mapping highlight artificial intelligence, machine learning, and education management as motor themes. Database bias may affect results. Recommendations urge policymakers and leadership programs to incorporate data literacy and AI ethics as the discipline transitions into mainstream data-driven management.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

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Abstract

Introduction

Contemporary Educational Technology, 2026, 18(2), ep658 ISSN: 1309-517X (Online) OPEN ACCESS Review Article Bibliometric analysis of scientific publications on artificial intelligence in educational management and leadership: Web of Science and Scopus sources between 1986-2026 Adem Yurdunkulu 1* 0000-0001-7879-3184 1 Ibn Haldun University, Istanbul, TURKEY * Corresponding author: [adem.yurdunkulu@ihu.edu.tr](adem.yurdunkulu@ihu.edu.tr) Citation: Yurdunkulu, A. (2026) . Bibliometric analysis of scientific publications on artificial intelligence in educational management and leadership: Web of Science and Scopus sources between 1986-2026. Contemporary Educational Technology, 18(2), Article ep658. [https://doi.org/10.30935/cedtech/18577](https://doi.org/10.30935/cedtech/18577) ARTICLE INFO ABSTRACT | Received: 7 Dec 2025
Accepted: 4 May 2026 | This study aims to map the academic literature at the intersection of artificial intelligence (AI) and educational management/leadership through bibliometric methods, using a dataset of 1,072 articles retrieved from the Web of Science and Scopus databases. Analyses conducted with R software and the Biblioshiny package reveal that the field is exceptionally young and dynamic, with an average document age of only 2.7 years, and that scientific output has increased substantially in the post-2020 period. This dynamic structure can be attributed to the COVID-19 pandemic and the widespread diffusion of generative AI tools. The geographical distribution of publication output indicates orientation towards new geographical directions with a growing concentration along a Eurasian axis. At the institutional level, Kazan Federal University (99 publications) and RUDN University (62 publications) emerge as dominant contributors, while China holds a global leadership position in citation impact (1,885 citations) . This result should be cautiously handled as the database bias and publication concentration effects might have influenced the search outputs. In terms of conceptual structure, “artificial intelligence” and“machine learning” appear as dominant terms, and the thematic map identifies these concepts—together with “education management”—as motor themes. This finding suggests that the field has moved beyond purely theoretical debates and is transitioning into the mainstream of datadriven educational management. Considering this transformation, it is recommended that policymakers and leadership-development programs urgently integrate modules on data literacy and AI ethics. Overall, the study concludes that the discipline is undergoing a transition period, where future leaders will be responsible for governing algorithmic resources alongside human resources.
Keywords: artificial intelligence, bibliometric, education, leadership, management | | --- | --- | | INTRODUCTION | | In the first quarter of the 21st century, educational technologies have evolved from simple digitization of instructional materials into holistic, data-driven systems that reshape institutional management and leadership. Rapid advances in artificial intelligence (AI) have transformed both pedagogical and administrative dimensions of schooling (Zawacki-Richter et al., 2019) . AI now functions not merely as a “teaching assistant”but as a strategic “management partner” that enhances institutional efficiency, resource allocation, and decision-making (Popenici & Kerr, 2017) . This study is theoretically anchored in established educational leadership frameworks. Instructional leadership theory (Hallinger, 2011) emphasizes leaders’ core responsibility for curriculum, instruction, and student outcomes—functions that AI can significantly augment through predictive analytics and personalized learning pathways. Distributed leadership theory (Hartley, 2007; Lumby, 2013; Timperley, 2005) highlights leadership as a shared, collective practice distributed across individuals and tools, offering a lens for unde

FAQs

What dataset and method does the study use to map AI and educational management literature?

The study analyzes 1,072 articles retrieved from the Web of Science and Scopus databases using bibliometric methods with R and the Biblioshiny package.

What do the results suggest about how quickly this field has developed?

The field is exceptionally young and dynamic, with an average document age of 2.7 years and a substantial rise in output after 2020.

Which themes and leading contributors are identified in the literature mapping?

“Artificial intelligence” and “machine learning” are dominant terms, and “education management” functions as a motor theme; Kazan Federal University and RUDN University lead by publication count, while China leads citation impact.

Oct 15, 2026

Uploaded by OmBimo