Review Article

Artificial intelligence literacy in social research: A bibliometric analysis of conceptual evolution and emerging trends

Eluard Alexander Mendoza-Zenozain 1 * , Franklin Cordova-Buiza 2
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1 Faculty of Engineering, Universidad Privada del Norte, Lima, PERU2 Department of Research and Innovation, Universidad Privada del Norte, Lima, PERU* Corresponding Author
Contemporary Educational Technology, 18(4), October 2026, ep698, https://doi.org/10.30935/cedtech/19597
Published: 13 October 2026
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ABSTRACT

In contemporary digital society, artificial intelligence literacy (AI literacy) has become a key competence that enables citizens to understand and actively participate in increasingly automated environments. However, despite the rapid growth of scientific output, partly driven by tools such as ChatGPT, the literature reveals substantial conceptual fragmentation that hinders an integrated understanding of its educational and ethical dimensions. In this context, this study aimed to analyze the intellectual evolution and conceptual structure of research on AI literacy, based on Scopus-indexed literature. A bibliometric methodology was applied to a corpus of 2,645 documents using scientific mapping techniques to identify publication patterns, collaboration networks, and dominant thematic streams. The results reveal an accelerated expansion of the field and a shift from early approaches focused primarily on educational contexts such as K-12 education and computational thinking toward more complex frameworks that incorporate algorithmic ethics, psychological acceptance of technology, critical thinking, and academic integrity challenges associated with generative AI (GenAI). Overall, this study contributes to the educational technology literature by providing a longitudinal bibliometric interpretation of the evolution of AI literacy and by identifying emerging research agendas related to GenAI, ethical reasoning, and critical digital competencies in contemporary education.
Keywords: AI literacy, artificial intelligence, generative AI, educational technology, bibliometric analysis, higher education

CITATION (APA)

Mendoza-Zenozain, E. A., & Cordova-Buiza, F. (2026). Artificial intelligence literacy in social research: A bibliometric analysis of conceptual evolution and emerging trends. Contemporary Educational Technology, 18(4), ep698. https://doi.org/10.30935/cedtech/19597

REFERENCES

  1. Alharthi, R. H. E., Salleh, N. Z., Abdullah, M., Ali, A., Faisal, F., & Mohd Nor, R. (2023). Research trends, developments, and future perspectives in brand attitude: A bibliometric analysis utilizing the Scopus database (1944-2021). Heliyon, 9(1), Article e12765. https://doi.org/10.1016/j.heliyon.2022.e12765
  2. Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019-2023). Computers & Education Open, 6(1), Article 100173. https://doi.org/10.1016/j.caeo.2024.100173
  3. 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
  4. Baas, J., Schotten, M., Plume, A., Côté, G., & Karimi, R. (2020). Scopus as a curated, high-quality bibliometric data source for academic research in quantitative science studies. Quantitative Science Studies, 1(1), 377-386. https://doi.org/10.1162/qss_a_00019
  5. Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1-13. https://doi.org/10.1080/1369118X.2016.1216147
  6. Biagini, G. (2025). Towards an AI-literate future: A systematic literature review exploring education, ethics, and applications. International Journal of Artificial Intelligence in Education, 35, 2616-2666. https://doi.org/10.1007/s40593-025-00466-w
  7. Börner, K., Chen, C., & Boyack, K. W. (2010). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179-255. https://doi.org/10.1002/aris.1440370106
  8. Bornmann, L., & Daniel, H.-D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45-80. https://doi.org/10.1108/00220410810844150
  9. Boyack, K. W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389-2404. https://doi.org/10.1002/asi.21419
  10. Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351-374. https://doi.org/10.1007/s11192-005-0255-6
  11. Černý, M. (2024). University students’ conceptualisation of AI literacy: Theory and empirical evidence. Social Sciences, 13(3), Article 129. https://doi.org/10.3390/socsci13030129
  12. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), Article 43. https://doi.org/10.1186/s41239-023-00411-8
  13. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377. https://doi.org/10.1002/asi.20317
  14. Chen, C. (2017). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1-40. https://doi.org/10.1515/jdis-2017-0006
  15. Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, Article 100197. https://doi.org/10.1016/j.caeai.2023.100197
  16. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study. Journal of the American Society for Information Science and Technology, 62(7), 1382-1402. https://doi.org/10.1002/asi.21525
  17. Cordova-Buiza, F., Jimenez-Del-Carpio, P. S., Conde-Galindo, H. J., & Rosas-Falconi, D. (2026). Contribution of the digitisation of cultural tourism to the resilience of local communities in the face of global crises: An integrative review. In Proceedings of the International Conference on Tourism Research (pp. 99-106). https://doi.org/10.34190/ictr.9.1.4476
  18. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press. https://doi.org/10.12987/9780300252392
  19. Dai, Y., Chai, C.-S., Lin, P.-Y., Jong, M. S.-Y., Guo, Y., & Qin, J. (2020). Promoting students’ well-being by developing their readiness for the artificial intelligence age. Sustainability, 12(16), Article 6597. https://doi.org/10.3390/su12166597
  20. 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
  21. Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771-1796. https://doi.org/10.1007/s11948-020-00213-5
  22. Ioannidis, J. P. A., Boyack, K. W., & Baas, J. (2020). Updated science-wide author databases of standardized citation indicators. PLOS Biology, 18(10), Article e3000918. https://doi.org/10.1371/journal.pbio.3000918
  23. Kalantzis, M., & Cope, B. (2025). Literacy in the time of artificial intelligence. Reading Research Quarterly, 60, Article e591. https://doi.org/10.1002/rrq.591
  24. Kandlhofer, M., Steinbauer-Wagner, G., Hirschmugl-Gaisch, S., & Huber, P. (2016). Artificial intelligence and computer science in education: From kindergarten to university. In Proceedings of the Frontiers in Education Conference (pp. 1-9). IEEE. https://doi.org/10.1109/FIE.2016.7757570
  25. Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students’ conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology and Society, 26(1), 16-30. https://doi.org/10.30191/ETS.202301_26(1).0002
  26. Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, Article 100101. https://doi.org/10.1016/j.caeai.2022.100101
  27. Lee, I., Ali, S., Zhang, H., DiPaola, D., & Breazeal, C. (2021). Developing middle school students’ AI literacy. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 191-197). ACM. https://doi.org/10.1145/3408877.3432513
  28. Leydesdorff, L. (2007). Mapping interdisciplinarity at the interfaces between the science citation index and the social science citation index. Scientometrics, 71(3), 391-405. https://doi.org/10.1007/s11192-007-1694-z
  29. Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://doi.org/10.1145/3313831.3376727
  30. Long, D., Blunt, T., & Magerko, B. S. (2021). Co-designing AI literacy exhibits for informal learning spaces. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW2), Article 293. https://doi.org/10.1145/3476034
  31. Mahadewi, M. P., Aysya, A. A. A., Sofiyani, Z., & Fahmi, F. (2025). The importance of literacy on artificial intelligence for higher education students: A systematic literature review. International Journal of Advances in Data and Information Systems, 6(1), 1-14. https://doi.org/10.59395/ijadis.v6i1.1350
  32. Mhlanga, D., & Dzingirai, M. (2024). Bibliometric study on organizational resilience: Trends and future research agenda. International Journal of Corporate Social Responsibility, 9, Article 9. https://doi.org/10.1186/s40991-024-00098-8
  33. Moed, H. F. (2005). Citation analysis in research evaluation. Springer. https://doi.org/10.1007/1-4020-3714-7
  34. 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
  35. Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de la Información, 29(1), Article e290103. https://doi.org/10.3145/epi.2020.ene.03
  36. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021a). AI literacy: Definition, teaching, evaluation and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509. https://doi.org/10.1002/pra2.487
  37. Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021b). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
  38. Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137-161. https://doi.org/10.1007/s11423-023-10203-6
  39. Olortegui-Alcalde, L. M., & Cordova-Buiza, F. (2025). Digital innovation and performance of student startups in Peru: A PLS-SEM evaluation. Problems and Perspectives in Management, 23(4), 407-420. https://doi.org/10.21511/ppm.23(4).2025.30
  40. 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
  41. Pangrazio, L., Godhe, A.-L., & Ledesma, A. G. L. (2020). What is digital literacy? A comparative review of publications across three language contexts. E-Learning and Digital Media, 17(6), 442-459. https://doi.org/10.1177/2042753020946291
  42. Pinski, M., & Benlian, A. (2024). AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects. Computers in Human Behavior: Artificial Humans, 2(1), Article 100062. https://doi.org/10.1016/j.chbah.2024.100062
  43. Rasul, T., Nair, S., Kalendra, D., Robin, M., Santini, F. O., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. https://doi.org/10.37074/jalt.2023.6.1.29
  44. Rodriguez-Fernandez, I. C., Jurado-Vera, S. Y., Ramos-Cavero, M. J., & Cordova-Buiza, F. (2026). Digital transformation and operational efficiency in fast-moving consumer goods companies: A systematic review of Industry 4.0 applications. International Journal of Sustainable Development and Planning, 21(1), 51-60. https://doi.org/10.18280/ijsdp.210105
  45. Schleiss, J., Laupichler, M. C., Raupach, T., & Stober, S. (2023). AI course design planning framework: Developing domain-specific AI education courses. Education Sciences, 13(9), Article 954. https://doi.org/10.3390/educsci13090954
  46. Su, J., Ng, D. T. K., & Chu, S. K. W. (2023). Artificial intelligence (AI) literacy in early childhood education: The challenges and opportunities. Computers and Education: Artificial Intelligence, 4, Article 100124. https://doi.org/10.1016/j.caeai.2023.100124
  47. Sugimoto, C. R., & Larivière, V. (2018). Measuring research: What everyone needs to know. Oxford University Press. https://doi.org/10.1093/wentk/9780190640118.001.0001
  48. Swart, J. (2021). Experiencing algorithms: How young people understand, feel about, and engage with algorithmic news selection on social media. Social Media + Society, 7(2). https://doi.org/10.1177/20563051211008828
  49. Tadimalla, S. Y., & Maher, M. L. (2025). AI literacy as a core component of AI education. AI Magazine, 46, Article e70007. https://doi.org/10.1002/aaai.70007
  50. Tenório, K., Olari, V., Chikobava, M., & Romeike, R. (2023). Artificial intelligence literacy research field: A bibliometric analysis from 1989 to 2021. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education (pp. 1083-1089). ACM. https://doi.org/10.1145/3545945.3569874
  51. Thaha, A. R., Riau, D. P., Antoro, A. F. S., & Aziz, F. (2025). Digital economy and SME research landscape: A bibliometric analysis of emerging themes and trends. Journal of Scientometric Research, 14(1), 272-282. https://doi.org/10.5530/jscires.20251711
  52. Traga Philippakos, Z. A., & Rocconi, L. (2025). AI literacy: Elementary and secondary teachers’ use of AI tools, reported confidence, and professional development needs. Education Sciences, 15(9), Article 1186. https://doi.org/10.3390/educsci15091186
  53. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. https://doi.org/10.1007/s11192-009-0146-3
  54. van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice (pp. 285-320). Springer. https://doi.org/10.1007/978-3-319-10377-8_13
  55. Verma, R., Kumar, A., Mashroofa, M. M., & Gupta, N. (2025). A comprehensive bibliometric analysis of research trends in sustainable inclusive economic growth within the SDG 8 framework from 2015 to 2025 using Biblioshiny and VOSviewer. Discover Sustainability, 6, Article 1221. https://doi.org/10.1007/s43621-025-02124-6
  56. Walter, Y. (2024). Embracing the future of artificial intelligence in the classroom: The relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21, Article 15. https://doi.org/10.1186/s41239-024-00448-3
  57. Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365-391. https://doi.org/10.1016/j.joi.2016.02.007
  58. 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
  59. Yang, Y., Zhang, Y., Sun, D., He, W., & Wei, Y. (2025). Navigating the landscape of AI literacy education: Insights from a decade of research (2014-2024). Humanities and Social Sciences Communications, 12, Article 374. https://doi.org/10.1057/s41599-025-04583-8
  60. Zhang, S., Prasad, P. G., & Schroeder, N. L. (2025). Learning about AI: A systematic review of reviews on AI literacy. Journal of Educational Computing Research, 63(5), 1292-1322. https://doi.org/10.1177/07356331251342081
  61. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization: A review. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629