Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications

Okan Dede

doi:10.53444/deubefd.1897269

TR EN

Öz

Yapay Zekâ Okuryazarlığını Anlamak: Örüntüler, Değişim ve Çıkarımlar

Öz

Bu araştırmanın amacı üniversite öğrencilerinin yapay zekâ okuryazarlık düzeylerini belirlemek ve söz konusu yeterliğin bireysel ile eğitsel değişkenler bağlamında farklılaşma örüntülerini incelemektir. Türkiye yükseköğretim bağlamında yapay zekâ okuryazarlığına ilişkin ampirik kanıtların sınırlı olduğu göz önünde bulundurulduğunda bu çalışma, lisans öğrencileri arasında YZ okuryazarlığı yeterliklerinin nasıl dağıldığına dair geniş ölçekli ve çok boyutlu kanıtlar sunarak bölgesel alanyazındaki bir boşluğu doldurmaktadır. Araştırma kesitsel tarama modeli çerçevesinde desenlenmiştir. Çalışma grubunu Türkiye’nin Karadeniz Bölgesi’ndeki bir devlet üniversitesinde öğrenim gören 870 lisans öğrencisi oluşturmaktadır. Veriler farkındalık, kullanım, değerlendirme ve etik boyutlarını kapsayan Yapay Zekâ Okuryazarlık Ölçeği aracılığıyla elde edilmiştir. İstatistiksel analizlerde bağımsız örneklemler t-testi ve tek yönlü varyans analizi (ANOVA) teknikleri kullanılmıştır. Bulgular katılımcıların genel yapay zekâ okuryazarlık düzeylerinin orta düzeyin üzerinde konumlandığını, ancak dağılımın belirgin bir heterojenlik içerdiğini ortaya koymuştur. Cinsiyet ve fakülte değişkenlerine göre istatistiksel olarak anlamlı bir farklılaşma saptanmamış; bu bulgu yapay zekâ okuryazarlığının disiplinler arası niteliğine işaret etmektedir. Buna karşılık sınıf düzeyi ve günlük internet kullanım süresi değişkenleri açısından anlamlı farklılıklar belirlenmiştir. Üst sınıf öğrencilerinin alt sınıf öğrencilerine kıyasla belirgin biçimde daha yüksek okuryazarlık düzeylerine sahip olduğu ve yoğun dijital etkileşimin okuryazarlık puanlarıyla pozitif yönde ilişkili olduğu görülmüştür. Bu bulgular, sistematik YZ okuryazarlığı eğitiminin henüz kurumsal düzeyde yerleşmediği Türkiye üniversitelerinde müfredat tasarımı açısından doğrudan çıkarımlar taşımaktadır. Sonuçlar yapay zekâ okuryazarlığının görece durağan demografik değişkenlerden ziyade deneyimsel ve etkileşimsel faktörlerle ilişkili olduğunu göstermektedir. Bulgular yapay zekâ okuryazarlığının geliştirilmesinde süreklilik gösteren ve planlı eğitim stratejilerinin kritik rolüne işaret etmektedir.

Anahtar Kelimeler

Yapay zekâ, yapay zekâ okuryazarlığı, yükseköğretim

Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications

Öz

This study aims to determine university students’ levels of artificial intelligence (AI) literacy and to examine the patterns of variation of this competence across individual and educational variables. Conducted within the Turkish higher education context, where empirical evidence on AI literacy remains limited, this study addresses a gap in the regional literature by providing large-scale, multidimensional evidence on how AI literacy competencies are distributed among undergraduate students. The research was designed within the framework of a cross-sectional survey model. The study group consisted of 870 undergraduate students enrolled at a public university in the Black Sea region of Turkey. Data were obtained through the Artificial Intelligence Literacy Scale, which encompasses the dimensions of awareness, usage, evaluation, and ethics. Independent samples t-tests and one-way analysis of variance (ANOVA) were employed for statistical analyses. Critically, the findings revealed that participants’ overall AI literacy levels were positioned above the moderate level, although the distribution exhibited notable heterogeneity. No statistically significant differences were identified with respect to gender or faculty variables, suggesting the cross-disciplinary nature of AI literacy. In contrast, significant differences were observed in relation to academic level and daily internet usage. Senior students demonstrated notably higher AI literacy levels than their peers in earlier years, and intensive digital engagement was found to be positively associated with literacy scores. These findings carry direct implications for curriculum design in Turkish universities, where systematic AI literacy education has yet to be institutionally embedded. The results indicate that AI literacy is more closely related to experiential and interactional factors rather than relatively stable demographic variables, pointing to the critical role of sustained and systematically planned educational strategies in developing this competence.

Anahtar Kelimeler

Artificial intelligence, artificial intelligence literacy, higher education

Kaynakça

Adedoyin, O. B., & Soykan, E. (2023). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 31(2), 863-875. https://doi.org/10.1080/10494820.2020.1813180
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish, & W. M. Ramsey (Eds.), The cambridge handbook of artificial intelligence (pp. 316-334). Cambridge University Press. https://doi.org/10.1017/CBO9781139046855.020
Büyüköztürk, Ş. (2015). Sosyal bilimler için veri analizi el kitabı. Pegem Akademi.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
Chiu, T. K., Ahmad, Z., Ismailov, M., & Sanusi, I. T. (2024). What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open, 6, 100171. https://doi.org/10.1016/j.caeo.2024.100171
Çelebi, C., Yılmaz, F., Demir, U., & Karakuş, F. (2023). Artificial ıntelligence literacy: an adaptation study. Instructional Technology and Lifelong Learning, 4(2), 291-306. https://doi.org/10.52911/itall.1401740
Doğruel, L., Masur, P., & Joeckel, S. (2022). Development and validation of an algorithm literacy scale for internet users. Communication Methods and Measures, 16(2), 115-133. https://doi.org/10.1080/19312458.2021.1968361
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 1-47. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109
Fraenkel, R. J., & Wallen E.N. (2006). How to design and evaluate research in education. McGraw-Hill.

Gawlik-Kobylińska, M., Walkowiak, W., & Maciejewski, P. (2020). Improvement of a sustainable world through the application of innovative didactic tools in green chemistry teaching: a review. Journal of Chemical Education, 97(4), 916-924. https://doi.org/10.1021/acs.jchemed.9b01038
Gilster, P. (1997). Digital literacy. John Wiley & Sons, Inc.
Hannan, E., & Liu, S. (2023). AI: new source of competitiveness in higher education. Competitiveness Review: An International Business Journal, 33(2), 265-279. https://doi.org/10.1108/CR-03-2021-0045
Humphry, T., & Fuller, A. L. (2023). Potential chatgpt use in undergraduate chemistry laboratories. Journal of Chemical Education, 100(4), 1434-1436. https://doi.org/10.1021/acs.jchemed.3c00006
Jensen, L. X., Buhl, A., Sharma, A., & Bearman, M. (2025). Generative AI and higher education: a review of claims from the first months of chatgpt. Higher Education, 89(4),1145-1161. https://doi.org/10.1007/s10734-024-01265-3
Karasar, N. (2013). Bilimsel araştırma yöntemi. Nobel Yayıncılık.
Khlaif, Z. N., Salha, S., & Kouraichi, B. (2021). Emergency remote learning during COVID-19 crisis: students’ engagement. Education and Information Technologies, 26(6), 7033-7055. https://doi.org/10.1007/s10639-021-10566-4
Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2021). Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds. Computers and Education: Artificial Intelligence, 2, 100026. https://doi.org/10.1016/j.caeai.2021.100026
Kong, S. C., Cheung, W. M. Y., & Tsang, O. (2023). Evaluating an artificial intelligence literacy programme for empowering and developing concepts, literacy and ethical awareness in senior secondary students. Education and Information Technologies, 28(4), 4703-4724. https://doi.org/10.1007/s10639-022-11408-7
Kurzweil, R. (1992). The age of intelligent machines. MIT Press.
Lap, N. D., & Çakır, R. (2025). An analysis of university students’ artificial intelligence literacy and acceptance levels of generative ai applications across various variables. Journal of Teacher Education and Lifelong Learning, 7(2), 274-288. https://doi.org/10.51535/tell.1808375
Lawrie, G. (2023). Establishing a delicate balance in the relationship between artificial intelligence and authentic assessment in student learning. Chemistry Education Research and Practice, 24(2), 392-393. https://doi.org/10.1039/D3RP90003G
Livingstone, S. (2004). What is media literacy? InterMedia, 32(3), 18–20.
Lolinco, A. T., & Holme, T. A. (2023). Developing a curated chatbot as an exploratory communication tool for chemistry learning. Journal of Chemical Education, 100(10), 4092-4098. https://doi.org/10.1021/acs.jchemed.3c00520
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 (pp. 1-16). https://doi.org/10.1145/3313831.3376727
Mansoor, H. M., Bawazir, A., Alsabri, M. A., Alharbi, A., & Okela, A. H. (2024). Artificial intelligence literacy among university students- a comparative transnational survey. Frontiers in Communication, 9, 1478476. https://doi.org/10.3389/fcomm.2024.1478476
Müller, V. C. (2016). Risks of artificial intelligence. Chapman & Hall.
Nabiyev, V. V. (2012). Yapay zeka: İnsan-bilgisayar etkileşimi. Seçkin Yayıncılık.
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, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Oeldorf-Hirsch, A., & Neubaum, G. (2023). Attitudinal and behavioral correlates of algorithmic awareness among German and US social media users. Journal of Computer-Mediated Communication, 28(5), zmad035. https://doi.org/10.1093/jcmc/zmad035
Pence, H. E. (2019). Artificial intelligence in higher education: New wine in old wineskins? Journal of Educational Technology Systems, 48(1), 5-13. https://doi.org/doi:10.1177/0047239519865577
Popenici, S. A. D., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(22), 1-13. https://doi.org/10.1186/s41039-017-0062-8
Rahman, Md. M., & Watanobe, Y. (2023). ChatGPT for education and research: opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783
Santosa, E. B., Degeng, I., Sulton, S., & Kuswandi̇, D. (2020). The effects of mobile computer supported collaborative learning to improve problem solving and achievements. Journal for the Education of Gifted Young Scientists, 8(1), 325-342. https://doi.org/10.17478/jegys.656642
Selvaraj, A., Radhin, V., Nithin, K. A., Benson, N., & Mathew, A. J. (2021). Effect of pandemic based online education on teaching and learning system. International Journal of Educational Development, 85, 102444. https://doi.org/10.1016/j.ijedudev.2021.102444
Spector, J. M., & Ma, S. (2019). Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence. Smart Learning Environments, 6(1). 8. https://doi.org/doi:10.1186/s40561-019-0088-z
Sweeney, S. (2023). Who wrote this? Essay mills and assessment–Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. https://doi.org/10.1016/j.ijme.2023.100818
Tabachnick, B. G., & Fidell, L.S. (2013). Using multivariate statistics (6th Ed.). Pearson Publishing,
Taebnia, V., & Taqavi, M. (2020). The enhanced human vs. the virtuous human: a post-phenomenological perspective. AI & Society, 36(3), 1057-1068. https://doi.org/10.1007/s00146-020-01082-z
Turing, A. M. (1950). Computing machinery and intelligence. Mind, LIX No. 236, 433.
UNESCO (2017). Data revolution to measure equity in education for SDGs #CIES2017. UNESCO Institute for Statistics.
UNESCO (2021). AI and education: guidance for policy-makers. UNESCO.
Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227. https://doi.org/10.1111/jpim.12523
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
West, J. K., Franz, J. L., Hein, S. M., Leverentz-Culp, H. R., Mauser, J. F., Ruff, E. F., & Zemke, J. M. (2023). An analysis of ai-generated laboratory reports across the chemistry curriculum and student perceptions of chatgpt. Journal of Chemical Education, 100(11), 4351-4359. https://doi.org/10.1021/acs.jchemed.3c00581
Wiredu, J. (2023). An investigation on the characteristics, abilities, constraints, and functions of artificial intelligence (ai): The age of chatgpt as an essential. Information and Management, 108(3), 62614- 62620. https://doi.org/10.37118/ijdr.26689.05.2023
Wolff, A., Gooch, D., Cavero Montaner, J. J., Rashid, U., & Kortuem, G. (2016). Special issue on data literacy: Articles creating an understanding of data literacy for a data-driven society. Journal of Community Informatics, 12(3), 9–26. https://doi.org/10.15353/joci.v12i3.3275
Yik, B. J., Dood, A. J., Cruz-Ramírez De Arellano, D., Fields, K. B., & Raker, J. R. (2021). Development of a machine learning-based tool to evaluate correct Lewis acid–base model use in written responses to open-ended formative assessment items. Chemistry Education Research and Practice, 22(4), 866-885. https://doi.org/10.1039/D1RP00111F
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1). 39. https://doi.org/10.1186/s41239-019-0171-0
Zembylas, M. (2023). A decolonial approach to AI in higher education teaching and learning: Strategies for undoing the ethics of digital neocolonialism. Learning, Media and Technology, 48(1), 25-37. https://doi.org/10.1080/17439884.2021.2010094

Ayrıntılar

Birincil Dil

İngilizce

Konular

Eğitim Üzerine Çalışmalar (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Okan Dede ^*
0000-0002-2771-6522
Türkiye

Erken Görünüm Tarihi

29 Eylül 2026

Yayımlanma Tarihi

30 Eylül 2026

Gönderilme Tarihi

25 Şubat 2026

Kabul Tarihi

3 Ağustos 2026

Yayımlandığı Sayı

Yıl 2026 Sayı: 69

DOI

https://doi.org/10.53444/deubefd.1897269

IZ

https://izlik.org/JA65JP99AD

APA

Dede, O. (2026). Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications. Buca Eğitim Fakültesi Dergisi, 69, 123-142. https://doi.org/10.53444/deubefd.1897269

AMA

1.Dede O. Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications. Buca Eğitim Fakültesi Dergisi. 2026;(69):123-142. doi:10.53444/deubefd.1897269

Chicago

Dede, Okan. 2026. “Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications”. Buca Eğitim Fakültesi Dergisi, sy 69: 123-42. https://doi.org/10.53444/deubefd.1897269.

EndNote

Dede O (01 Eylül 2026) Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications. Buca Eğitim Fakültesi Dergisi 69 123–142.

IEEE

[1]O. Dede, “Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications”, Buca Eğitim Fakültesi Dergisi, sy 69, ss. 123–142, Eyl. 2026, doi: 10.53444/deubefd.1897269.

ISNAD

Dede, Okan. “Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications”. Buca Eğitim Fakültesi Dergisi. 69 (01 Eylül 2026): 123-142. https://doi.org/10.53444/deubefd.1897269.

JAMA

1.Dede O. Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications. Buca Eğitim Fakültesi Dergisi. 2026;:123–142.

MLA

Dede, Okan. “Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications”. Buca Eğitim Fakültesi Dergisi, sy 69, Eylül 2026, ss. 123-42, doi:10.53444/deubefd.1897269.

Vancouver

1.Okan Dede. Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications. Buca Eğitim Fakültesi Dergisi. 01 Eylül 2026;(69):123-42. doi:10.53444/deubefd.1897269

Öz

Öz

Yapay Zekâ Okuryazarlığını Anlamak: Örüntüler, Değişim ve Çıkarımlar

Öz

Anahtar Kelimeler

Understanding Artificial Intelligence Literacy in Higher Education: Patterns, Variation, and Implications

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster