Artificial Intelligence, Education, and Ethics: A Bibliometric Perspective
Artificial Intelligence, Education, and Ethics: A Bibliometric Perspective

Summary/Abstract: This study analyzes the structure, trends, and focal points of scholarly production in the field of artificial intelligence (AI) and ethics through a bibliometric approach using VOSviewer, based on 1,307 documents in the Web of Science (WoS) database. Analyses reveal the growth trend in publications from 2010 to 2024, with a notable rise in output after 2020, and highlight the conceptual “yellow core” of the topic: transparency, privacy, academic integrity, decision-making, social justice, robot ethics, and AI security. Countrylevel analyses indicate that the United States and China form high-output, high-impact clusters, while Europe functions as a normative production center despite relatively lower publication volumes. At the journal level, AI & Society, Science and Engineering Ethics, IEEE Access, and Journal of Medical Ethics play central roles in the ideation and citation networks, while education-focused journals such as Education and Information Technologies, Studies in Higher Education, and Frontiers in Education show rising influence as of 2024. Keyword co-occurrence and co-citation maps support the view that AI ethics has become an applied, context-sensitive, and interdisciplinary field with multiactor engagement. Findings suggest that the expansion of AI in education intensifies ethical discourse, with large language models like ChatGPT bringing evaluation and academic integrity concerns back into focus. This study provides a reference for tracing current trends and guiding future research; it is suggested that comparative bibliometric studies using alternative databases (Scopus, Dimensions, or YÖK Theses), along with qualitative fieldwork and policy analyses, would offer a more comprehensive illumination of the sociological dimensions of AI ethics.

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