Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap

Mehmet Cabir Akkoyunlu; Oğuz Onat; Yasemin Bertiz

doi:10.17275/per.26.86.13.6

Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap

Abstract

This study investigates the relationship between university students’ digital literacy and artificial intelligence (AI) literacy. Participants were 770 undergraduate students (227 male, 543 female; Mage = 21.27, SD = 2.75) recruited through online convenience and snowball sampling, with recruitment centered at Karamanoğlu Mehmetbey University (Karaman, Türkiye), representing more than 120 academic programs. Data were collected with the Turkish forms of Ng’s (2012) Digital Literacy Scale (α = .931) and Wang et al.’s (2022) AI Literacy Scale (α = .803). Three findings stand out. First, digital literacy and AI literacy were strongly and positively related (r = .596, p < .001). A three-step hierarchical regression analysis identified digital literacy as the only significant predictor of AI literacy (β = .599, p < .001), explaining 35.5% of the variance (R² = .355, F(1, 768) = 422.57, p < .001); adding gender and age (ΔR² = .002) and internet use frequency (ΔR² = .000) did not significantly improve the model. Second, a small gender difference favoring men emerged in digital literacy (Cohen’s d = 0.201) but not in AI literacy (d = 0.031), a pattern we describe as a gender paradox. Third, although 85.1% of participants believed in AI’s future impact, only 22.7% rated their own AI knowledge as sufficient—a knowledge–belief gap. Self-rated AI knowledge, but not belief, differed significantly by gender (χ² = 11.05, p = .004; Cramér’s V = .120). Implications for digital and AI literacy education in higher education are discussed.

Keywords

digital literacy, artificial intelligence literacy, university students, gender differences, hierarchical regression

References

Cave, S., Coughlan, K., & Dihal, K. (2019). "Scary robots": Examining public responses to AI. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 331–337. https://doi.org/10.1145/3306618.3314232
Caz, C., Yazıcı, O. F., & Bıçer, T. (2024). Spor bilimleri fakültesi öğrencilerinde dijital iyi oluş ile yapay zekâ okuryazarlığı ilişkisi: Yapısal eşitlik modeli uygulaması [The relationship between digital well-being and artificial intelligence literacy among sport sciences faculty students: A structural equation model application]. ROL Spor Bilimleri Dergisi [Journal of ROL Sport Sciences], 5(4), 581–593. https://doi.org/10.70736/jrolss.463
Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial intelligence literacy: An adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291–306. https://doi.org/10.52911/itall.1401740
Çetindamar, D., Kitto, K., Wu, M., Zhang, Y., Abedin, B., & Knight, S. (2022). Explicating AI literacy of employees at digital workplaces. IEEE Transactions on Engineering Management, 71, 810–823. https://doi.org/10.1109/TEM.2021.3138503
Cheng, C.-C., Wang, J.-S., Zhai, X., & Yang, Y. T. C. (2025). AI literacy and gender equity in elementary education: A quasi-experimental study of a STEAM–PBL–AIoT course with questionnaire validation. International Journal of STEM Education, 12(1), Article 50. https://doi.org/10.1186/s40594-025-00574-y
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International Journal of Market Research, 50(1), 61–104. https://doi.org/10.1177/147078530805000106
DeVellis, R. F. (2017). Scale development: Theory and applications (4th ed.). SAGE Publications.
Elçiçek, M. (2024). Öğrencilerin yapay zekâ okuryazarlığı üzerine bir inceleme [An investigation on students' artificial intelligence literacy]. Bilgi ve İletişim Teknolojileri Dergisi [Journal of Information and Communication Technologies], 6(1), 24–35. https://doi.org/10.53694/bited.1460106

Eshet-Alkalai, Y. (2004). Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia, 13(1), 93–106.
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Hamutoğlu, N. B., Canan Güngören, O., Kaya Uyanık, G., & Gür Erdoğan, D. (2017). Dijital okuryazarlık ölçeği: Türkçe'ye uyarlama çalışması [Digital Literacy Scale: Adaptation study into Turkish]. Ege Eğitim Dergisi [Ege Journal of Education], 18(1), 408–429. https://doi.org/10.12984/egeefd.295306
Hargittai, E., & Shafer, S. (2006). Differences in actual and perceived online skills: The role of gender. Social Science Quarterly, 87(2), 432–448. https://doi.org/10.1111/j.1540-6237.2006.00389.x
Humlum, A., & Vestergaard, E. (2025). The unequal adoption of ChatGPT exacerbates existing inequalities among workers. Proceedings of the National Academy of Sciences, 122(1), Article e2414972121. https://doi.org/10.1073/pnas.2414972121
Işık, M., & Çamur, O. (2024). Yapay zekâ ve dijital okuryazarlık: Akademik çabada yeni dinamikler [Artificial intelligence and digital literacy: New dynamics in academic effort]. Beykoz Akademi Dergisi [Beykoz Academy Journal], 12(2), 173–197. https://doi.org/10.14514/beykozad.1508294
Karaoğlan Yılmaz, F. G., & Yılmaz, R. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçe'ye uyarlaması [Adaptation of the artificial intelligence literacy scale into Turkish]. Bilgi ve İletişim Teknolojileri Dergisi [Journal of Information and Communication Technologies], 5(2), 172–190. https://doi.org/10.53694/bited.1376831
Karasar, N. (2020). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler [Scientific research method: Concepts, principles, techniques] (35th ed.). Nobel Akademik Yayıncılık [Nobel Academic Publishing].
Kong, S.-C., & Yang, Y. (2025). Developing and validating an artificial intelligent empowerment instrument: Evaluating the impact of an artificial intelligent literacy programme for secondary school and university students. Research and Practice in Technology Enhanced Learning, 20, Article 024. https://doi.org/10.58459/rptel.2025.20024
Laupichler, M. C., Aster, A., Haverkamp, N., & Raupach, T. (2023). Development of the scale for the assessment of non-experts' AI literacy: An exploratory factor analysis. Computers in Human Behavior Reports, 12, Article 100338. https://doi.org/10.1016/j.chbr.2023.100338
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, 1–16. https://doi.org/10.1145/3313831.3376727
Mahalanobis, P. C. (1936). On the generalized distance in statistics. Proceedings of the National Institute of Sciences of India, 2(1), 49–55.
Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. https://doi.org/10.1037/a0028085
Møgelvang, A., Bjelland, C., Grassini, S., & Ludvigsen, K. (2024). Gender differences in the use of generative artificial intelligence chatbots in higher education: Characteristics and consequences. Education Sciences, 14(12), Article 1363. https://doi.org/10.3390/educsci14121363
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016
Selwyn, N., & Gallo Cordoba, B. (2022). Australian public understandings of artificial intelligence. AI & Society, 37(4), 1645–1662. https://doi.org/10.1007/s00146-021-01268-z
Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
UNESCO. (n.d.). Women's access to and participation in technological developments and the AI lifecycle. https://www.unesco.org/en/artificial-intelligence/gender-equality
Üstündağ, M. T., Güneş, E., & Bahçivan, E. (2017). Dijital okuryazarlık ölçeğinin Türkçe'ye uyarlaması ve fen bilgisi öğretmen adaylarının dijital okuryazarlık durumları [Turkish adaptation of the digital literacy scale and the digital literacy status of pre-service science teachers]. Journal of Education and Future, 12, 19–29.
van Deursen, A. J. A. M., & van Dijk, J. A. G. M. (2014). The digital divide shifts to differences in usage. New Media & Society, 16(3), 507–526. https://doi.org/10.1177/1461444813487959
Vosoughmatin, M. (2025). Investigation of digital competencies and artificial intelligence literacy of special education students. International Journal of Modern Education Studies, 9(2). https://doi.org/10.51383/ijonmes.2025.430
Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens—With new examples of knowledge, skills and attitudes (EUR 31006 EN). Publications Office of the European Union. https://doi.org/10.2760/115376
Wang, B., Rau, P.-L. P., & Yuan, T. (2022). 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

Details

Primary Language

English

Subjects

Higher Education Systems

Journal Section

Research Article

Authors

Mehmet Cabir Akkoyunlu ^*
0000-0002-9388-6554
Türkiye

Oğuz Onat
0000-0002-3191-7837
Türkiye

Yasemin Bertiz
0000-0001-7388-5901
Türkiye

Publication Date

November 1, 2026

Submission Date

May 14, 2026

Acceptance Date

July 10, 2026

Published in Issue

Year 2026 Volume: 13 Number: 6

DOI

https://doi.org/10.17275/per.26.86.13.6

IZ

https://izlik.org/JA26TC97TS

APA

Akkoyunlu, M. C., Onat, O., & Bertiz, Y. (2026). Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap. Participatory Educational Research, 13(6), 233-249. https://doi.org/10.17275/per.26.86.13.6

AMA

1.Akkoyunlu MC, Onat O, Bertiz Y. Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap. PER. 2026;13(6):233-249. doi:10.17275/per.26.86.13.6

Chicago

Akkoyunlu, Mehmet Cabir, Oğuz Onat, and Yasemin Bertiz. 2026. “Digital Literacy As a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap”. Participatory Educational Research 13 (6): 233-49. https://doi.org/10.17275/per.26.86.13.6.

EndNote

Akkoyunlu MC, Onat O, Bertiz Y (November 1, 2026) Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap. Participatory Educational Research 13 6 233–249.

IEEE

[1]M. C. Akkoyunlu, O. Onat, and Y. Bertiz, “Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap”, PER, vol. 13, no. 6, pp. 233–249, Nov. 2026, doi: 10.17275/per.26.86.13.6.

ISNAD

Akkoyunlu, Mehmet Cabir - Onat, Oğuz - Bertiz, Yasemin. “Digital Literacy As a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap”. Participatory Educational Research 13/6 (November 1, 2026): 233-249. https://doi.org/10.17275/per.26.86.13.6.

JAMA

1.Akkoyunlu MC, Onat O, Bertiz Y. Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap. PER. 2026;13:233–249.

MLA

Akkoyunlu, Mehmet Cabir, et al. “Digital Literacy As a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap”. Participatory Educational Research, vol. 13, no. 6, Nov. 2026, pp. 233-49, doi:10.17275/per.26.86.13.6.

Vancouver

1.Mehmet Cabir Akkoyunlu, Oğuz Onat, Yasemin Bertiz. Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap. PER. 2026 Nov. 1;13(6):233-49. doi:10.17275/per.26.86.13.6

27707

Creative Commons Atıf 4.0 International Lisance

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Digital Literacy as a Predictor of AI Literacy: A Gender Paradox and a Knowledge–Belief Gap

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