A comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development provides an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape.
Abstract
Generative Artificial Intelligence (AI) has become a rapidly expanding area of scientific research, generating a growing body of literature across technical and applied domains. This study provides a comprehensive bibliometric analysis of global generative AI research to characterize its publication growth, disciplinary and geographical distribution, institutional participation, funding patterns, citation performance, and thematic development. The analysis covers 22,758 Scopus-indexed journal articles and conference papers published between 2020 and 2025, identified using the phrase “generative artificial intelligence” enclosed in double quotation marks in TITLE-ABS-KEY fields. A reproducible computational workflow was used to examine publication output, document types, subject areas, countries, institutions, funding sponsors, citation patterns, and keyword co-occurrence. Citation analysis incorporated annualized citation rates and cohort-normalized annual citation rates to improve comparisons across publication years. Results show a pronounced acceleration in publication output after 2022, with an approximate 105% compound annual growth rate over 2020–2025. Computer Science remained the largest subject area, while substantial representation extended across Engineering, Social Sciences, Medicine, Mathematics, and other domains. Publication activity was concentrated among leading countries and institutions, with the United States and China recording the highest output. Funding analysis identified major national and international sponsors through publication–sponsor associations. Citation performance varied substantially across cohorts, with the 2023 cohort exhibiting the highest cohort-normalized annual citation rate (1.58). Keyword analysis revealed a thematic shift from early AI and GAN-related research toward generative AI and large language model themes, alongside education, innovation, human–AI interaction, and responsible AI. The findings provide an evidence-based, multidimensional characterization of the rapidly evolving generative AI research landscape.
Artificial Intelligence (AI) has become an increasingly important aspect in research writing that has significantly influenced scholarly communication and academic publishing. The results of this paper are the bibliometric analysis of publications indexed in Scopus between 2015 and 2025 to explore the global research trends, thematic development networks and networks of collaboration in the topic area. Based on bibliometric inquiry, in consideration of publication expansion, citation impact, authorship patterns, country-level and institutional factors, as well as co-occurrence of keywords analysis of 5,241 publications, including the study analyses journal articles, conference papers and reviews. The results reveal a strong increasing trend in post-2019 publications due to the accelerated progress of natural language processing, machine learning, and generative systems such as ChatGPT. China is the largest producer in terms of global output, followed by the United States and India, which reflect the growing strength of Asian research communities. Such common interests as are seen in the analysis of keywords include language model, text generation, AI-assisted writing, and scholarly communication. In general, the findings point to the fact that AI is no longer a supportive tool, but a vital component of contemporary research writing, enhancing effective and cooperative research writing and increasing visibility. The analysis sheds light on the cognitive framework and world process of AI-driven research communication and implications for future academic and policy efforts.
P. Tyagi, S. Sharma· International Journal of Inf...· 0 citations
The rapid advancement of Generative Artificial Intelligence (GenAI) has significantly transformed educational practices by introducing new possibilities for personalized learning, intelligent tutoring systems, automated feedback, and AI-supported knowledge creation. This study aims to explore the global research landscape of Generative AI for Learning through a bibliometric analysis of scientific publications indexed in the Scopus database. A comprehensive literature search was conducted to identify relevant publications, followed by performance analysis and science mapping using VOSviewer. The analysis examined publication trends, highly cited literature, keyword co-occurrence, citation networks, author collaboration, institutional contributions, and international research patterns. The findings reveal that research on generative AI in learning has experienced substantial growth, particularly following the emergence of ChatGPT and large language models. The intellectual structure of the field is dominated by three interconnected themes: technological advancement of artificial intelligence, educational integration of AI-based learning systems, and human-centered considerations including AI literacy, critical thinking, ethics, and responsible adoption. Influential publications highlight both the opportunities and challenges of generative AI, including improvements in learning effectiveness, academic transformation, assessment challenges, and potential cognitive impacts. Furthermore, collaboration analysis indicates that the United States plays a central role in global research networks, while contributions from countries across Asia, Europe, and other regions continue to expand. This study provides a comprehensive understanding of the evolution, current trends, and future directions of Generative AI for Learning research, emphasizing the importance of interdisciplinary collaboration and responsible AI implementation to support sustainable educational innovation.
L. Judijanto· West Science Interdisciplina...· 0 citations
A five-phase framework comprising Strategic Conceptualisation, Systematic Literature Synthesis, Methodology Selection, Governance, Ethics and Trust, and Continuous Reflection and Feedback is proposed, positioning the human touch, curiosity, critical thinking, critical thinking, creativity, contextual expertise, and ethical judgement, as the foundation of responsible AI-assisted research planning.
Niranjan Devkota, M. Siddique, Dipendra Karki et al.· International Research Journ...· 0 citations
Technological advancements have fundamentally altered digital commerce, with artificial intelligence (AI) undertaking an increasingly influential role. Despite the growing volume of academic work in this area, integrative and comprehensive review studies remain limited, particularly in bibliometric and cluster analysis to synthesize accumulated knowledge. This research aims to fill the literature gap by providing a detailed understanding of the emerging trends and patterns in AI's influence on digital commerce. Using the Scopus database, 3751 documents published between 2015 and 2025 were included, and the PRISMA framework was followed. Only documents classified as articles and conference papers in English were included, whereas document types such as books, reviews, editorials, letters, and notes were excluded to ensure consistency in the analysis. BiblioMagika and VOSviewer were employed to perform a detailed bibliometric analysis, covering trend and citation analyses, main contributors and institutions, as well as keyword co-occurrence examination. The findings indicated that the number of publications increased gradually from 76 in 2015 to 1006 in 2025. Interestingly, Robert D. Cairns from McGill University stands out as a key contributor, with seven publications in AI and digital commerce-related research. Among the listed institutions, Jadara University, Jordan, was recognized as the most prolific contributor, with eight publications, followed by Universitas Padjadjaran, Indonesia, with five publications. The keyword co-occurrence network analysis identified seven primary theme clusters, offering valuable insights into future research on AI's role in the digital commerce literature. This study presents an in-depth synthesis for researchers, policymakers, management educators, and industry practitioners by emphasizing the growing influence of artificial intelligence in redefining commercial practices and strategic orientations.
M. Chan, Yee-Yann Yap, A. Seow et al.· International Conference on...· 0 citations
Artificial intelligence (AI) is rapidly transforming multiple domains, yet its integration into social work remains at an early stage. To address the lack of systematic and evidence-based understanding, this article applies bibliometric methods to examine 528 publications on AI in social work indexed in Web of Science and Scopus between 2016 and 2025. Using the Bibliometrix R package and Biblioshiny, the analysis explores publication trends, country collaboration, key word co-occurrence, content structures, and thematic evolution. Findings reveal a sharp growth trajectory, with annual outputs increasing nearly tenfold and peaking in 2024. Country networks confirm the dominance of the United States, China, Germany, and Australia, alongside emerging contributions from Asia and Latin America. Key word mapping shows that, while “artificial intelligence” and “social work” remain central anchors, new directions—including education, equity, healthcare, decision making, and generative AI—have gained prominence. Thematic evolution indicates a shift from exploratory discussions of digital transformation toward applied and evaluative concerns linked to ethics, disparities, and professional training. The present article provides actionable insights for scholars, practitioners, and policymakers, emphasizing the need for inclusive collaboration, ethical governance, and education-driven adoption to ensure that AI strengthens social sustainability and justice in social work.
Hafize Nurgül Durmuş Şenyapar· Social work research (Print)· 0 citations
Artificial Intelligence (AI) is transforming Circular Economy (CE) initiatives, yet the field's intellectual structure and thematic evolution remain insufficiently mapped. This study evaluates the scientific development of AI-driven CE research. Using Bibliometrix and VOSviewer, a comprehensive bibliometric analysis was conducted on 8,327 Scopus-indexed publications following an adapted PRISMA 2020 workflow. Results show scientific production surged post-2018, with logistic growth modeling indicating rapid maturation. China leads global publication output and international collaboration networks. Thematic evolution and co-occurrence mapping reveal a significant structural shift: early foundational research focused heavily on basic machine learning and recycling algorithms, whereas current frontiers emphasize system-level optimization, including intelligent manufacturing, life cycle assessment, waste valorization, and remanufacturing. Ultimately, AI-driven CE research has evolved from isolated technological applications into an interdisciplinary domain centered on data-driven sustainability. Sustaining this trajectory requires broader cross-institutional collaboration and the exploration of emerging paradigms, such as explainable and generative AI, to develop the resilient, sustainable production ecosystems necessary for global circularity goals.
Jennifer Orozco, Josephine A. Basadre· Journal of Intelligent Decis...· 0 citations
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