Web of Science-Based Bibliometric Analysis of Academic Studies on Artificial Intelligence Literacy

Abstract

This study presents a bibliometric analysis of publications indexed in the Web of Science under the topic “artificial intelligence literacy” for the period 2020–2025. The research aims to map the quantitative characteristics of the artificial intelligence literacy literature, its interdisciplinary distribution, author and institutional collaboration networks, and core concept clusters. Data were collected from the WoS Topic field on 16 August 2025, yielding a corpus of 119 documents. Analyses employed descriptive statistics and VOSviewer-based visualizations to examine publication and citation counts, country and institutional distributions, and keyword co-occurrence patterns. Findings indicate a rapid expansion of the field since 2020, a concentration of studies within educational contexts, and prominent contributions from China, the United States, and Turkey. The literature also shows rising attention to themes such as AI ethics, generative AI, and scale development, while author and institutional collaboration networks remain fragmented and regionally clustered. The results discuss research trends and gaps and underscore the importance of more inclusive database searches and the promotion of multicentric international collaborations.

Keywords

• Artificial Intelligence Literacy
• Bibliometric Analysis
• VOSviewer
• AI Ethics
• Education

WoS Veri Tabanındaki Yapay Zekâ Okuryazarlığı Konulu Akademik Çalışmaların Bibliyometrik Analizi

Abstract

Bu çalışma, Web of Science veri tabanında indekslenen “artificial intelligence literacy” başlıklı yayınların 2020-2025 dönemi bibliyometrik analizini sunmaktadır. Araştırmanın amacı, yapay zekâ okuryazarlığı literatürünün nicel karakteristiklerini, disiplinler arası dağılımını, yazar ve kurum işbirliği ağlarını ile anahtar kavram kümelerini haritalamaktır. Veri toplama 16 Ağustos 2025 tarihinde WoS’un Topic alanı kullanılarak gerçekleştirilmiş; toplam 119 belge analiz edilmiştir. Analizlerde yayın sayıları, atıf sayıları, ülke ve kurum dağılımları ile anahtar kelime eş-görünümüne ilişkin görselleştirmeler için VOSviewer yazılımı ve betimleyici istatistikler kullanılmıştır. Bulgular, 2020’den itibaren alanın hızlı bir büyüme gösterdiğini, çalışmaların ağırlıklı olarak eğitim bağlamında yoğunlaştığını ve Çin, ABD ile Türkiye’nin öne çıkan ülkeler olduğunu ortaya koymaktadır. Ayrıca literatürde “AI ethics”, “generative AI” ve “scale development” gibi temaların yükselişte olduğu; yazar ve kurum işbirliğinin ise hâlâ parçalı, bölgesel kümelenmelerle sınırlı kaldığı saptanmıştır. Sonuçlar, alandaki araştırma eğilimlerini ve boşlukları tartışarak, daha kapsayıcı veri tabanı taramalarının ve çok merkezli iş birliklerinin teşvik edilmesinin önemini vurgulamaktadır.

Keywords

• Yapay Zekâ Okuryazarlığı
• Bibliyometrik Analiz
• VOSviewer
• Yapay Zekâ Etiği
• Eğitim

References

1. Acemoglu, D. & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
2. Alkan, A. (2024). Yapay zekâ: Eğitimdeki rolü ve potansiyeli. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 13(1), 483–497. https://dergipark.org.tr/tr/download/article-file/3280453
3. Al-Kfairy, M., Mustafa, D., Kshetri, N., Insiew, M. & Alfandi, O. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11(3), 58. https://doi.org/10.3390/ informatics11030058 Altıntop, M. (2025). Wos Veri Tabanında İçerik Analizi Bağlamında Yapılan Bibliyometrik Analizlerin Bibliyometrik Analizi. Arşiv Dünyası, 12(1), 1-27. https://doi.org/10.53474/ad.1651659
4. Buolamwini, J. & Gebru, T. (2018). Gender Shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 77–91. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
5. Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. U.S. Department of Education. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf
6. Coşkun, F. ve Gülleroğlu, H. D. (2021). Yapay zekânın tarih içindeki gelişimi ve eğitimde kullanılması. Ankara Üniversitesi Eğitim Bilimleri Fakültesi Dergisi, 54(3), 947–966. https://dergipark.org.tr/tr/download/article-file/1707037
7. Çetin, M., Karakuş, A. ve Geçgel, Ş. (2024). Gelişen bir paradigma: Yapay zekâ okuryazarlığı. International Journal of Active Learning, 8(1), 50–63. https://dergipark.org.tr/tr/pub/ijal/issue/83378/1422876
8. El Cassai, A., Ng, J.Y., Liu, H., Masood, M., Syed, N., Stephen, D., Ayala, A. P., Sabé, M., Solmi, M., Waltman, L., Haustein & S., Moher, D. (2024). Guidance List for repOrting Bibliometric AnaLyses (GLOBAL): A Research Protocol. https://doi.org/10.1101/2024.08.26.24312538v1

9. Elsayed, A. M., & Abusharhah, M. M. (2025). Artificial intelligence adoption, perceptions, and ethical literacy among Arab academic librarians: A survey. The Journal of Academic Librarianship, 51(5), 103083. https://doi.org/10.1016/j.acalib.2025.103083
10. Gonzales, S. (2024). AI literacy and the new digital divide — A global call for action. UNESCO. https://www.unesco.org/en/articles/ai-literacy-and-new-digital-divide-global-call-action
11. Guıdry, K. R. (2024). AI literacy literature summary. California State University Generative AI. Retrieved from https://genai.calstate.edu/communities/faculty/ethical-and-responsible-use-ai/ai-literacy-literature-summary
12. Güler, T. ve Canbaz Akça, T. (2021). Yeni Medya Eğitiminin Yükseköğretim Boyutunun Değerlendirilmesi. Bilecik Şeyh Edebali Üniversitesi Sosyal Bilimler Dergisi, 6(1), 109–122. https://doi.org/10.33905/bseusbed.944121
13. Henry, J., Hernalesteen, A., & Collard, A-S. (2020). Designing digital literacy activities: An interdisciplinary and collaborative approach. 2020 IEEE Frontiers in Education Conference (FIE 2020), 21–24 October 2020, Uppsala, Sweden. IEEE. https://doi.org/10.1109/FIE44824.2020.9274165
14. Hoang, A. (2025). Evaluating Bibliometrics Reviews: A Practical Guide for Peer Review and Critical Reading. OSF Preprints. https://doi.org/10.31219/osf.io/wdkp9_v1
15. Karaağaçlı, M. (2025). Yapay Zekâ Uygulamalarında Etik Gereksinimi. International Journal of Information Systems and Applications, 1(1). https://dergipark.org.tr/tr/download/article-file/4723781
16. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105. http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
17. Lancho Barrantes, B. S. (2025). Academic perspectives on bibliometrics in a leading UK research university. Education for Information, 41(2), 88–106. https://doi.org/10.1177/01678329251323443
18. Lim, W. M., Kumar, S. & Donthu, N. (2024). How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research. Journal of Business Research, 182, Article 114760. https://doi.org/10.1016/j.jbusres.2024.114760
19. Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, 1–11. https://doi.org/10.1038/s41539-024-00264-4
20. Liu, X., Zhang, Lo. & Wei, X. (2025). Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance. Behavioral Sciences (Basel, Switzerland), 15(6), 811. https://doi.org/10.3390/bs15060811
21. Long, D. & Magerko, B. (2020). What is AI Literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
22. Marzi, G., Balzano, M., Caputo, A. & Pellegrini, M. M. (2025). Guidelines for bibliometric-systematic literature reviews: 10 steps to combine analysis, synthesis and theory development. International Journal of Management Reviews, 27(1), 81–103. https://doi.org/10.1111/ijmr.12381
23. McCarthy, J., Minsky, M. L., Rochester, N. & Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Dartmouth College.
24. Mukherjee, D., Lim, W. M., Kumar, S. & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of Business Research, 148, 101–115. https://doi.org/10.1016/j.jbusres.2022.04.042
25. Öztürk, O., Kocaman, R. & Kanbach, D. K. (2024). How to design bibliometric research: An overview and a framework proposal. Review of Managerial Science, 18, 3333–3361. https://doi.org/10.1007/s11846-024-00738-0
26. Passas, I. (2024). Bibliometric Analysis: The Main Steps. Encyclopedia, 4(2), 1014–1025. https://doi.org/10.3390/ encyclopedia4020065
27. Peng, Y., Shi, J., Fantinato, M. & Chen, J. (2017). A study on the author collaboration network in big data. Information Systems Frontiers, 19, 1329–1342. https://doi.org/10.1007/s10796-017-9771-1
28. Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529, 484–489. https://doi.org/10.1038/nature16961
29. Solak Berigel, D. & Şılbır L. (2024). A bibliometric analysis of AI literacy: Trends, topics and future directions. Near East University Online Journal of Education, 7(1), 42–53. https://dergi.neu.edu.tr/index.php/neuje/ article/view/970 Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J. & Thomas, A. (2023). Developing a model for AI across the curriculum: Transforming the higher education landscape via innovation in AI literacy. Computers and Education: Artificial Intelligence, 4, 100127. https://doi.org/10.1016/ j.caeai.2023.100127
30. Touretzky, D. S. & Gardner-Mccune, C. (2021). Artificial Intelligence Thinking in K-12. AI4K12. https://ai4k12.org/wp-content/uploads/2021/08/Touretzky_Gardner-McCune_AI-Thinking_2021.pdf
31. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. https://courses.cs.umbc.edu/471/papers/turing.pdf
32. Unesco (2021). AI and education: Guidance for policy-makers. Unesco Publishing. https://www.unesco.org/ en/digital-education/artificial-intelligence
33. Wang, B., Rau, P-L. P. & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
34. Wood, E. A., Ange, B. L. & Miller, D. D. (2021). Are we ready to integrate artificial intelligence literacy into medical school curriculum: Students and faculty survey. Journal of Medical Education and Curricular Development, 8, 23821205211024078. https://doi.org/10.1177/23821205211024078
35. Yılmaz, M. (2019). Bibliyometriye Eleştirel Bir Bakış. Türk Kütüphaneciliği, 33(1), 43–49.
36. Yükseköğretim Kurulu (YÖK) (2024). Yükseköğretim kurumları bilimsel araştırma ve yayın faaliyetlerinde üretken yapay zekâ kullanımına dair etik rehber (Mayıs 2024). YÖK. https://eski.yok.gov.tr/Documents/2024/ yapay-zeka-kullanimina-dair-etik-rehber.pdf