An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables

Nazmiye Didem Lap; Recep Çakır

doi:10.51535/tell.1808375

An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables

Abstract

This study investigates the relationship between university students’ levels of Artificial Intelligence Literacy (AIL) and their acceptance of Generative Artificial Intelligence (GAI) applications within the framework of the Technology Acceptance Model (TAM). The research was conducted using a quantitative correlational survey design with a total of 797 students enrolled in associate, undergraduate, and graduate programs at Amasya University. Data was collected through the Artificial Intelligence Literacy Scale and the Generative Artificial Intelligence Acceptance Scale and analyzed using descriptive statistics, correlation and ANOVA analyses. The findings indicated that students’ overall AIL levels were above average with the highest scores observed in the evaluation and ethics dimensions. The level of GAI acceptance was found to be moderate with performance expectancy and effort expectancy dimensions scoring higher than social influence. Small, but significant differences were observed across gender, level of education, internet usage, and school type: female students scored higher on ethical dimensions, while male students showed higher scores on effort expectancy and social influence. Graduate students achieved higher means in their performance expectancy. Correlation analysis revealed a moderate positive relationship between AIL and GAI acceptance. Regression results indicated that AIL explained 34% of the variance in acceptance. The evaluation dimension emerged as the strongest predictor, while awareness and use made limited yet significant contributions. The ethics dimension was not found to be significant. These results highlight the necessity of integrating critical evaluation and ethical awareness components into higher education curricula to foster responsible and informed use of AI technologies.

Keywords

Artificial Intelligence Literacy, Technology Acceptance Model, Generative Artificial Intelligence Acceptance, Higher Education

References

Acosta-Enriquez, B. G., Farroñan, E. V. R., Zapata, L. I. V., Garcia, F. S. M., Rabanal-León, H. C., Angaspilco, J. E. M., & Bocanegra, J. C. S. (2024). Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory. Heliyon, 10(19).
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, 100173.
Arslankara, V. B., & Usta, E. (2024). Generative artificial intelligence as a lifelong learning self efficacy: Usage and competence scale. Journal of Teacher Education and Lifelong Learning, 6(2), 288-302. https://doi.org/10.51535/tell.1489304
Bećirović, S., Polz, E., & Tinkel, I. (2025). Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance. Smart Learning Environments, 12(1), 29.
Cengiz, S., & Peker, A. (2025). Generative artificial intelligence acceptance and artificial intelligence anxiety among university students: The sequential mediating role of attitudes toward artificial intelligence and literacy. Current Psychology, 44(9), 7991-8000.
Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002.
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., ... & Soomro, R. B. (2024). Extended TAM-based acceptance of AI-powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8).
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Dede, C. (2010). Comparing frameworks for 21st century skills. In J. Bellanca & R. Brandt (Eds.), 21st century skills: Rethinking how students learn (pp. 51–76). Solution Tree Press.
De Leon, L. (2023). Redefining the paradigm of engagement for a digital age. International Journal of Technology, Knowledge and Society, 19(1), 1

El Hosayny, O., Razkane, H., & Yeou, M. (2025). Extended TAM-based acceptance of AI for supporting critical thinking in academic research: An examination of perceived benefits and challenges. Discover Artificial Intelligence, 5(1), 167.
Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). Sage Publications.
Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. Machine learning and the city: Applications in architecture and urban design, 535-545.
Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th ed.). McGraw-Hill.
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84.
Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497. https://doi.org/10.1007/s40593-014-0024-x
Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367. https://doi.org/10.1080/15391523.2011.10782576
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20(1), 2.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Karasar, N. (2009). Scientific research method: Concepts, principles, and techniques. Ankara: Nobel Publishing.
Kay, R. H. (2009). Examining gender differences in attitudes toward interactive classroom communication systems (ICCS). Computers & Education, 52(4), 730–740.
Kivunja, C. (2014). Innovative pedagogies in higher education to become effective teachers of 21st century skills: Unpacking the learning and innovation skills domain of the new learning paradigm. International Journal of Higher Education, 3(4), 37-48.
Kleine, A. K., Schaffernak, I., & Lermer, E. (2025). Exploring predictors of AI chatbot usage intensity among students: Within- and between-person relationships based on the technology acceptance model. Computers in Human Behavior: Artificial Humans, 3, 100113.
Kong, S. C., & Zhu, J. (2025). Developing and validating an artificial intelligence ethical awareness scale for secondary and university students: Cultivating ethical awareness through problem-solving with artificial intelligence tools. Computers and Education: Artificial Intelligence, 100447.
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
Luckin, R. (2018). Machine learning and human intelligence: The future of education for the 21st century. UCL Institute of Education Press.
Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023). ChatGPT and a new academic reality: Artificial intelligence‐written research papers and the ethics of large language models in scholarly publishing. Journal of the Association for Information Science and Technology, 74(5), 570–581.
Mustofa, R. H., Kuncoro, T. G., Atmono, D., & Hermawan, H. D. (2025). Extending the technology acceptance model: The role of subjective norms, ethics, and trust in AI tool adoption among students. Computers and Education: Artificial Intelligence, 8, 100379.
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.
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), 46.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223–245.
Tang, X., Yuan, Z., Kuang, H., & Qu, H. (2024). Using the UTAUT-TPACK model to explain digital teaching behaviour of elementary school mathematics teachers. Asia Pacific Journal of Education, 1-23.
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432-2440. https://doi.org/10.1016/j.compedu.2011.06.008
Teo, T. (2019). Students and teachers' intention to use technology: Assessing their measurement equivalence and structural invariance. Journal of Educational Computing Research, 57(1), 201-225.
Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. John Wiley & Sons.
Tondeur, J., Scherer, R., Siddiq, F., & Baran, E. (2020). Enhancing pre-service teachers’ technological pedagogical content knowledge (TPACK): A mixed-method study. Educational Technology Research and Development, 68(1), 319-343.
Touretzky, D. S., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K–12: What should every child know about AI? In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 9795–9799). https://doi.org/10.1609/aaai.v33i01.33019795
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M. A., Lacroix, T., ... & Lample, G. (2023). LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971.
Uğraş, H., Doğan, M., & Uğraş, M. (2024). Adaptation of the artificial intelligence literacy scale into Turkish: A sample of pre-service teachers. e-Kafkas Journal of Educational Research, 11, 688-701.
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO Publishing.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Voogt, J., & Roblin, N. P. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 44(3), 299-321.
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.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Xia, T., Pan, X., Cao, M., & Guo, J. (2025). An investigation of college students’ acceptance of AI-assisted reading tools: An expansion of the TAM and SDT. Education and Information Technologies, 1-28.
Yıldırım, A., & Şimşek, H. (2018). Qualitative research methods in social sciences (11th ed.). Ankara: Seçkin Publishing.
Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M. (2024). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human-Computer Interaction, 40(24), 8703-8715.
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), 1-27. https://doi.org/10.1186/s41239-019-0171-0

Details

Primary Language

English

Subjects

Higher Education Studies (Other)

Journal Section

Research Article

Authors

Nazmiye Didem Lap This is me
Türkiye

Recep Çakır ^*
0000-0002-2641-5007
Türkiye

Publication Date

December 31, 2025

Submission Date

October 21, 2025

Acceptance Date

December 1, 2025

Published in Issue

Year 2025 Volume: 7 Number: 2

DOI

https://doi.org/10.51535/tell.1808375

IZ

https://izlik.org/JA72JZ58YH

APA

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

AMA

1.Lap ND, Çakır R. An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables. TELL. 2025;7(2):274-288. doi:10.51535/tell.1808375

Chicago

Lap, Nazmiye Didem, and Recep Çakır. 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): 274-88. https://doi.org/10.51535/tell.1808375.

EndNote

Lap ND, Çakır R (December 1, 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.

IEEE

[1]N. D. Lap and R. Çakır, “An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables”, TELL, vol. 7, no. 2, pp. 274–288, Dec. 2025, doi: 10.51535/tell.1808375.

ISNAD

Lap, Nazmiye Didem - Çakır, Recep. “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 (December 1, 2025): 274-288. https://doi.org/10.51535/tell.1808375.

JAMA

1.Lap ND, Çakır R. An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables. TELL. 2025;7:274–288.

MLA

Lap, Nazmiye Didem, and Recep Çakır. “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, vol. 7, no. 2, Dec. 2025, pp. 274-88, doi:10.51535/tell.1808375.

Vancouver

1.Nazmiye Didem Lap, Recep Çakır. An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables. TELL. 2025 Dec. 1;7(2):274-88. doi:10.51535/tell.1808375

26172 20107

An Analysis of University Students’ Artificial Intelligence Literacy and Acceptance Levels of Generative AI Applications Across Various Variables

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite