Generative AI in the arts and humanities: A bibliometric analysis of global research trends and thematic clusters (2021-2026)
DOI:
https://doi.org/10.47264/idea.lassij/10.1.6Keywords:
Bibliometric analysis, AI-generated art, Generative AI, Artificial intelligence, Art and technology, Web of Science, SSCI indexed, A&HCI indexedAbstract
The rapid advancement of generative artificial intelligence (AI) has fundamentally transformed the creation, perception, and societal implications of visual and literary art. The present study presents the first comprehensive bibliometric analysis of SSCI- and A&HCI-indexed scholarship on AI-generated art, drawing on 290 publications retrieved from the Web of Science Core Collection. Bibliometric methods, including performance analysis, Bradford’s law, keyword clustering, and citation analysis, were applied to examine publication trends, core journals, leading authors and institutions, geographical distributions, thematic clusters, and intellectual influence. Results reveal an explosive growth trajectory: from 4 publications in 2021 to 111 in 2025 (CAGR = 129.5%), reflecting a field in rapid institutionalisation. Keyword analysis identifies four thematic clusters: (1) aesthetic perception and human–AI evaluation, (2) generative AI technology and text-to-image systems, (3) societal and ethical implications, and (4) media, journalism, and communication. The h-index of 24 and g-index of 45 indicate a rapidly maturing, though still nascent, evidence base.
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
Arango, L., Singaraju, S. P., & Niininen, O. (2023). Consumer responses to AI-generated charitable giving ads. Journal of Advertising, 52(4), 486–503. https://doi.org/10.1080/00913367.2023.2183285
Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85–86. https://doi.org/10.1177/016555158501000407
Chatterjee, A. (2022). Art in an age of artificial intelligence. Frontiers in Psychology, 12, 769734. https://doi.org/10.3389/fpsyg.2021.769734
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152. https://doi.org/10.1007/s11192-006-0144-7
Gangadharbatla, H. (2022). The role of AI attribution knowledge in the evaluation of artwork. Empirical Studies of the Arts, 40(2), 125–142. https://doi.org/10.1177/0276237421994697
Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing, 42(1), 40–58. https://doi.org/10.1016/j.ijresmar.2024.09.002
Hirsch, J. E. (2005). An index to quantify an individual's scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
Hitsuwari, J., Ueda, Y., Yun, W., & Nomura, M. (2023). Does human-AI collaboration lead to more creative art? Computers in Human Behavior, 139, 107502. https://doi.org/10.1016/j.chb.2022.107502
Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, 106553. https://doi.org/10.1016/j.chb.2020.106553
Lee, U.-G., Han, A.-R., Lee, J.-J., Lee, E.-S., Kim, J., Kim, H., & Lim, C. (2024). Prompt Aloud! Education and Information Technologies, 29(8), 9889–9918. https://doi.org/10.1007/s10639-023-12184-0
Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.
Millet, K., Buehler, F., Du, G., & Kokkoris, M. D. (2023). Defending humankind: Anthropocentric bias in the appreciation of AI art. Computers in Human Behavior, 143, 107707. https://doi.org/10.1016/j.chb.2023.107707
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
Natale, S., & Henrickson, L. (2024). The Lovelace effect: Perceptions of creativity in machines. New Media & Society, 26(4), 2142–2160. https://doi.org/10.1177/14614448221091607
Oppenlaender, J. (2024). A taxonomy of prompt modifiers for text-to-image generation. Behaviour & Information Technology, 43(13), 2763–2781. https://doi.org/10.1080/0144929X.2023.2286532
Oppenlaender, J., Linder, R., & Silvennoinen, J. (2025). Prompting AI art: An investigation into the creative skill of prompt engineering. International Journal of Human-Computer Interaction, 41(1), 57–72. https://doi.org/10.1080/10447318.2023.2276406
Shank, D. B., Stefanik, C., Stuhlsatz, C., Kacirek, K., & Belfi, A. M. (2023). AI composer bias. Journal of Experimental Psychology: Applied, 29(3), 555–567. https://doi.org/10.1037/xap0000491
Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2024). Smiling women pitching down. Journal of Computer-Mediated Communication, 29(1), zmad045. https://doi.org/10.1093/jcmc/zmad045
Thomson, T. J., Thomas, R. J., & Matich, P. (2025). Generative visual AI in news organizations. Digital Journalism, 13(1), 1–24. https://doi.org/10.1080/21670811.2024.2341203
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Zhang, Y., & Gosline, R. (2023). Human favoritism, not AI aversion. Judgment and Decision Making, 18, e41. https://doi.org/10.1017/jdm.2023.41
Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967–972. https://doi.org/10.1002/asi.23437
Zipf, G. K. (1949). Human behavior and the principle of least effort. Addison-Wesley.
Zupic, I., & ?ater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
He, Q. (1999). Knowledge discovery through co-word analysis. Library Trends, 48(1), 133–159. https://www.ideals.illinois.edu/items/8226/bitstreams/28129/data.pdf
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