Artificial Intelligence and Research Ethics: a bibliometric study

An era of accelerated scientific and technological development is underway, marked by the exponential growth of scientific publications (articles, books, proceedings, etc.) (Kraus et al., 2022). Artificial intelligence (AI), while recognized as a field within Computer Science, is also broadly defined as a set of technologies capable of performing tasks typically associated with human intelligence, such as perception, judgment, decision-making, and creative problem-solving (Gabriel Filho, 2023; Resnik & Hosseini, 2024). Its rapid development has impacted diverse societal sectors, including industry, commerce, medicine, and education. Within scientific research, the expanding use of AI offers numerous benefits but also raises complex ethical dilemmas (Bouhouita-Guermech et al., 2023; Resnik & Hosseini, 2024). This study investigates the current growth of scientific publications at the intersection of AI and research ethics, posing the central question: How is this field of publication evolving? This overarching question led to the following general objective: to analyze the scientific literature on AI and research ethics, utilizing the Web of Science (WoS) database, with a specific focus on the field of education. The study’s specific objectives were to: (1) determine the growth rate of scientific publications on AI and research ethics; (2) identify the leading journals, authors, countries, and publication languages contributing to this body of knowledge; (3) map the most frequently cited articles on this topic; and (4) identify both prominent and underexplored research areas within this domain. To achieve these objectives, a pragmatic paradigm was adopted, employing bibliometric analysis as the primary research method. Bibliometric analysis serves as a crucial tool for analyzing extensive datasets of diverse scientific publications (Kraus et al., 2022; Öztürk et al., 2024; Subroto et al., 2024). Data collection was conducted within the WoS database, limited to articles. The search query employed was: ALL = ((“artificial intelligence” OR “AI”) AND (“research ethics” OR “ethics in research” OR “research integrity” OR “integrity in research”)). While acknowledging that terms such as “machine learning,” “machine intelligence,” “deep learning,” “deep networks,” “expert systems,” “intelligent systems,” “fuzzy logic,” and “genetic algorithms” are often used synonymously with or in association with “artificial intelligence” (Gabriel Filho, 2023; Subroto et al., 2024), this study specifically focused on publications explicitly using the terms “artificial intelligence” or “AI.” No restrictions were placed on publication date or language. Data analysis, encompassing both performance analysis and scientific mapping, followed the methodology described by Öztürk et al. (2024). The R programming language (version 4.4.2) (Wickham et al., 2023), particularly the bibliometrix package (Aria & Cuccurullo, 2017; Büyükkidik, 2022), was utilized for data processing and visualization. The results identified 404 articles published across 245 journals, demonstrating an annual growth rate of 17.39%. These articles span the period from 2004 to 2025, with accelerated growth observed from 2019 onward. The average article age of 2.95 years indicates the recency of this field. The ten journals with the highest publication output are BMJ Open, IEEE Access, Research Ethics, JMIR Research Protocols, Cureus Journal of Medical Science, Journal of Empirical Research on Human Research Ethics, Accountability in Research-Ethics Integrity and Policy, AI & Society, American Journal of Bioethics, and Learned Publishing. The most prolific authors include Hackshaw, A; Jamal-Hanjani, M; Karasaki, T; Moore, DA; Swanton, C; Veeriah, S; Grigoriadis, K; Hiley, CT; McGranahan, N; and Pich, O. The most productive countries are the United Kingdom, USA, China, Australia, Canada, Germany, Spain, South Africa, Brazil, and Malaysia. The top ten most cited articles exhibit citation counts ranging from 84 to 404, highlighting their influence within the field. The primary publication language is English (98.0%), followed by Spanish (1.0%), German (0.50%), Turkish (0.25%), and Ukrainian (0.25%). The most frequently addressed topics within the articles are artificial intelligence, research ethics, ethics, machine learning, ChatGPT, research integrity, deep learning, COVID-19, informed consent, and generative AI. Finally, the study identified several underexplored areas within education requiring further research, including the ethics of higher education, AI in higher education, AI toys in early childhood education, education decision-making, educational technologies, engineering education, K-12 education, lifelong vocational education, machine learning education, and medical education. This study underscores the significant potential for future research on the intersection of AI and research ethics, particularly within the field of education. References Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007 Bouhouita-Guermech, S., Gogognon, P., & Bélisle-Pipon, J.-C. (2023). Specific challenges posed by artificial intelligence in research ethics. Frontiers in Artificial Intelligence, 6, 1149082. https://doi.org/10.3389/frai.2023.1149082 Büyükkidik, S. (2022). 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