How to Cite
Abstract
AI literacy has become an essential competence in higher education, encompassing distinct technical, practical, critical, and ethical dimensions. This study aimed to examine whether students’ AI literacy, as well as its dimensions, namely AI technical understanding, AI practical application, AI critical appraisal, and AI ethics, differed according to gender, age, and educational level. An online survey was conducted across five Austrian higher education institutions to collect data from 245 students. The results showed a moderate overall level of AI literacy. AI critical appraisal showed the highest mean score, whereas AI technical understanding was markedly lower than the practical, critical, and ethical dimensions. All four dimensions were positively and significantly correlated, with the strongest association between AI critical appraisal and AI practical application. Male students scored significantly higher on overall AI literacy and on AI technical understanding, AI critical appraisal, and AI practical application than female students, while no gender difference was found for AI ethics. No statistically significant differences emerged across age groups. Educational-level differences were identified for overall AI literacy, AI technical understanding, and AI critical appraisal, with master’s-level students demonstrating the highest scores; no differences were found for AI practical application or AI ethics. Theoretically, the findings support a multidimensional conceptualization of AI literacy, showing that demographic differences are not homogeneous across individual dimensions and may be masked when AI literacy is treated only as an overall construct. Practically, they emphasize the necessity of dimension-specific, inclusive AI literacy initiatives, which greatly enhance technical understanding and critical appraisal and embed ethical competence across student groups.
References
Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173
Bećirović, S. (2023a). Examining learning management system success: A multiperspective framework. Education and Information Technologies, 1–25. https://doi.org/10.1007/s10639-023-12308-0
Bećirović, S. (2023b). Fostering Digital Competence in Teachers: A Review of Existing Frameworks. In S. Bećirović (Ed.), Digital Pedagogy: The Use of Digital Technologies in Contemporary Education (pp. 51–67). Springer Nature. https://doi.org/10.1007/978-981-99-0444-0_5
Bećirović, S., Augustín, M., Mattoš, B., & Dančo, J. (2026). Uncovering structural relationships between AI literacy, AI ethics, AI problem-solving, and self-regulated learning. Discover Computing, 29(1), 1–32. https://doi.org/10.1007/s10791-026-10042-y
Bećirović, S., Dervic, M., & Mattoš, B. (2025). Exploring the Factors Affecting Students’ Internet Habits, Self-Efficacy in E-Learning, and Academic Achievement: Structural Equation Modeling Approach. SAGE Open, 15(2), 1–21. https://doi.org/10.1177/21582440251339286
Bećirović, S., Karas, M., Dančo, J., & Augustín, M. (2026). Challenges, Barriers, and Pathways to Effective Artificial Intelligence Integration in University Education: A Qualitative Case Study. European Journal of Contemporary Education, 15(2), 182–201. https://doi.org/10.13187/ejced.2026.2.182
Bećirović, S., & Mattoš, B. (2024). Artificial Intelligence in the Transformation of Higher Education: Threats, Promises and Implementation Strategies. In L. Miltiadis D., A. C. Serban, E. Alkhalidi, M. Sawsan, & T. Aldosemani (Eds.), Digital Transformation in Higher Education, Part A: Best Practices and Challenges (pp. 23–43). Emerald Publishing.
Bećirović, S., Polz, E., & Tinkel, I. (2025a). A multidimensional study of AI adoption among University students in teacher education programs. Smart Learning Environments, 12(1), 1–26. https://doi.org/10.1186/s40561-025-00422-0
Bećirović, S., Polz, E., & Tinkel, I. (2025b). Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance. Smart Learning Environments, 12(1), 29. https://doi.org/10.1186/s40561-025-00384-3
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(4), 2616–2666. https://doi.org/10.1007/s40593-025-00466-w
Butt, F. S., Malik, A., & Mahmood, K. (2026). Assessing AI literacy of university students in Pakistan: A cross-sectional survey. Digital Library Perspectives, 42(1), 141–158. https://doi.org/10.1108/DLP-06-2025-0094
Cachero, C., Tomás, D., & Pujol, F. A. (2025). Gender Bias in Self-Perception of AI Knowledge, Impact, and Support among Higher Education Students: An Observational Study. ACM Transactions on Computing Education, 25(2), 1–26. https://doi.org/10.1145/3721295
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
Dautbašić, A., & Bećirović, S. (2022). Teacher and Student Experiences in Online Classes During COVID-19 Pandemic in Serbia, Bosnia and Herzegovina and Croatia. MAP Social Sciences, 2(1), Article 1. https://doi.org/10.53880/2744-2454.2022.2.1.9
Dervić, M., Bećirović, S., & Polz, E. (2025). Revolutionizing Pedagogy: The Transformative Influence of Artificial Intelligence on Educators’ Practices. In M. A. Adarkwah, S. Amponsah, R. Huang, & M. Thomas (Eds.), Artificial Intelligence and Human Agency in Education: Volume One: The Nexus Between AI and Human Agency in Educational Contexts (Vol. 1, pp. 193–212). Springer Nature. https://doi.org/10.1007/978-981-96-7937-9_9
Dong, Y., Xu, W., Huang, J., & Yann, K. (2025). Validating and refining a multi-dimensional scale for measuring AI literacy in education using the Rasch Model. Humanities and Social Sciences Communications, 12(1), 1317. https://doi.org/10.1057/s41599-025-05670-6
Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165. https://doi.org/10.1016/j.caeai.2023.100165
Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2022). Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building. Computers in Human Behavior Reports, 7, 100223. https://doi.org/10.1016/j.chbr.2022.100223
Kong, S.-C., Man-Yin Cheung, W., & 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
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, 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
M. Stracke, C., Griffiths, D., Pappa, D., Bećirović, S., Polz, E., Perla, L., Di Grassi, A., Massaro, S., Prifti Skenduli, M., Burgos, D., Punzo, V., Amram, D., Ziouvelou, X., Katsamori, D., Gabriel, S., Nahar, N., Schleiss, J., & Hollins, P. (2025). Analysis of Artificial Intelligence Policies for Higher Education in Europe. International Journal of Interactive Multimedia and Artificial Intelligence, 9(2), 124–137. https://doi.org/10.9781/ijimai.2025.02.011
M. Stracke, C., Nahar, N., Punzo, V., Massaro, S., Pappa, D., Di Grassi, A., Bećirović, S., Hollins, P., Ziouvelou, X., Prifti Skenduli, M., & Burgos, D. (2025). Artificial Intelligence Policies for Higher Education: Manifesto for Critical Considerations and a Roadmap. MAP Education and Humanities, 61–73. https://doi.org/10.53880/2744-2373.2025.6.61
Mansoor, H. M. H., 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
Medina-Gual, L., Medina-Velázquez, L., & Parejo, J.-L. (2025). A tridimensional model of AI literacy: An empirical analysis of student performance and demographic patterns in higher education. Australasian Journal of Educational Technology, 41(5), 37–55. https://doi.org/10.14742/ajet.10596
Memarian, B., & Doleck, T. (2023). Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review. Computers and Education: Artificial Intelligence, 5, 100152. https://doi.org/10.1016/j.caeai.2023.100152
Ng, D. T. K., Leung, J. K. L., Chu, K. W. S., & 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
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, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., Wu, W., Leung, J. K. L., Chiu, T. K. F., & Chu, S. K. W. (2024). Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach. British Journal of Educational Technology, 55(3), 1082–1104. https://doi.org/10.1111/bjet.13411
O’Dea, X., Tsz Kit Ng, D., O’Dea, M., & Shkuratskyy, V. (2026). Factors affecting university students’ generative AI literacy: Evidence and evaluation in the UK and Hong Kong contexts. Policy Futures in Education, 24(1), 13–34. https://doi.org/10.1177/14782103241287401
Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education. Computers and Education Open, 6, 100159. https://doi.org/10.1016/j.caeo.2024.100159
Tzirides, A. O. (Olnancy), Zapata, G., Kastania, N. P., Saini, A. K., Castro, V., Ismael, S. A., You, Y., Santos, T. A. D., Searsmith, D., O’Brien, C., Cope, B., & Kalantzis, M. (2024). Combining human and artificial intelligence for enhanced AI literacy in higher education. Computers and Education Open, 6, 100184. https://doi.org/10.1016/j.caeo.2024.100184
VanderStoep, S. W., & Johnston, D. D. (2009). Research methods for everyday life: Blending qualitative and quantitative approaches (1st ed). Jossey-Bass. http://catdir.loc.gov/catdir/toc/ecip0826/2008037380.html
Verduga-Alcívar, D. A., Román-Cao, E., & Sablón-Cossío, N. (2026). An assessment of artificial intelligence literacy in Ecuadorian university students. Social Sciences & Humanities Open, 13, 102465. https://doi.org/10.1016/j.ssaho.2026.102465
Wang, Z., Chai, C.-S., Li, J., & Lee, V. W. Y. (2025). Assessment of AI ethical reflection: The development and validation of the AI ethical reflection scale (AIERS) for university students. International Journal of Educational Technology in Higher Education, 22(1), 19. https://doi.org/10.1186/s41239-025-00519-z
Weng, X., Ye, H., Dai, Y., & Ng, O. (2024). Integrating Artificial Intelligence and Computational Thinking in Educational Contexts: A Systematic Review of Instructional Design and Student Learning Outcomes. Journal of Educational Computing Research, 07356331241248686. https://doi.org/10.1177/07356331241248686
Yaman, A., & Bećirović, S. (2016). Learning English and Media Literacy. 2(6), 4.
Yang, J., Xie, W., & Ni, J. (2025). A framework for AI ethics literacy: Development, validation, and its role in fostering students’ self-rated learning competence. Scientific Reports, 15(1), 38030. https://doi.org/10.1038/s41598-025-21977-5
Żywiołek, J., Ejdys, J., Wolniak, R., & Bećirović, S. (2026). Urban Transformation through Artificial Intelligence: Management Innovations for Sustainable Development. Routledge. https://doi.org/10.4324/9781003649809