Artificial Intelligence in Education (AIEd): Publication Patterns, Keywords, and Research Focuses

Abstract

Since the advent of generative AI, research on AI in Education (AIEd) has experienced explosive growth. This study systematically explores publication dynamics, keyword evolution, and research focuses in AIEd by analyzing 2952 papers from the Web of Science (1990–2024). Using bibliometric methods, 2800 English publications were screened, with analyses conducted via VOSviewer v1.6.20 and Python v3.11.5. Findings show a surge in publications post-2020, reaching 612 in 2023 and 1216 by November 2024. The US and China are leading contributors, with the University of London and the University of California system as core institutions. Keywords evolved from “AI” and “machine learning” (2018–2020) to “ChatGPT” and “ethics” (post-2022), reflecting dual focuses on technological applications and ethical considerations. Notably, 68% of highly cited papers address ethical controversies, while higher education and medical education emerge as primary application domains, involving personalized learning and intelligent tutoring systems. Cross-disciplinary research is evident, with education studies comprising the largest category. The study reveals AIEd’s shift toward socio-technical integration, highlighting generative AI’s transformative role yet identifying gaps in ethical governance and K-12 research. These insights inform policymakers, journals, and institutions, advocating for enhanced interdisciplinary collaboration and long-term impact research to balance innovation with educational ethics.

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