Carlo Giovannella, Alaa Alkhafaji, Mihai Dascalu, Elvira Popescu
pp. 24 – 57, download
(https://doi.org/10.55612/s-5002-067-002)
Abstract
This study presents findings on university students’ perceptions and usage habits regarding generative artificial intelligence (GenAI), based on questionnaire responses from students of various academic levels and disciplines across multiple countries (Romania, Iraq, Italy, Argentina, and the Philippines). The results indicate a near-universal adoption of GenAI, with usage largely driven by personal initiative. Most students acquire their knowledge of GenAI applications and form their perceptions independently, relying primarily on online resources rather than expert instruction. While awareness and understanding of GenAI tend to increase with academic progression, they are also influenced by broader cultural contexts; in some cases, gender-related differences are observed. Higher levels of awareness are associated with increased concern about the challenges posed by GenAI, as well as a deeper appreciation of its potential benefits. Students primarily use GenAI to obtain immediate, practical educational advantages. However, there is relatively limited engagement with the broader societal implications—positive or negative—of its use. The primary concern among participants is the impact of GenAI on future employment opportunities. A causal network analysis of the questionnaire data identifies two key outcomes at the end of the causal chain: the level of trust in GenAI-generated results and the degree of personalization in learning processes. Use of GenAI outside academic contexts remains limited, suggesting the emergence of a gap in AI literacy and application in everyday life. To address this issue, it is essential to implement training programs for educators, ensuring that students’ acquisition of AI literacy is supported by expert guidance—ideally beginning as early as primary or secondary education.
Keywords: AI usage, Perception on AI, AI in HE, AI pros and cons, causal network, AI and quasi-global patterns
References
1. https://www.bestcolleges.com/research/most-college-students-have-used-ai-survey/
2. https://www.compilatio.net/en/blog/press-release-ai-survey-2023
3. DEC: Digital Education Council Global AI Student Survey (2024) https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024
4. Freeman J.: Student Generative AI Survey 2025, Hepi Policy Notes 61 (2025) https://www.hepi.ac.uk/wp-content/uploads/2025/02/HEPI-Kortext-Student-Generative-AI-Survey-2025.pdf
5. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS Q. 13, pp. 319–340 (1989)
6. Marangunić, N. & Granić, A., Technology acceptance model: A literature review from 1986 to 2013. Univers. Access Inf. Soc. 14, pp. 81–95 (2015) https:// doi. org/ 10. 1007/ s10209- 014- 0348-1
7. Abdaljaleel M. et al., A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT, NaturePortfolio Scientific Reports 14:1983 (2024) https://doi.org/10.1038/s41598-024-52549-8
8. Venkatesh, V. et al. User acceptance of information technology: toward a unified view. MIS Quarterly 27 (3), 425–478 (2003).
9. Venkatesh, V., Thong, J. Y. & Xu, X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, : pp. 157–178. (2012).
10. Czikszentmihalyi, M. Flow: The Psychology of Optimal Experience, Harper & Row (1990)
11. Parveen K. et al.: Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling, NaturePortfolio Scientific Reports 14:23469, (2024) https://doi.org/10.1038/s41598-024-74406-4
12. Ansong, E., Lovia Boateng, S. & Boateng, R. Determinants of E-learning adoption in universities: Evidence from a developing country. J. Educ. Technol. Syst. 46, pp. 30–60 (2017) https:// doi. org/ 10. 1177/ 00472 39516 671520
13. Salimon, M. G. et al. Malaysian SMEs m-commerce adoption: TAM 3, UTAUT 2 and TOE approach. J. Sci. Technol. Policy Manag (2021) https:// doi. org/ 10. 1108/ JSTPM- 06- 2019- 0060
14. Tornatzky, L. G., Fleischer, M. & Chakrabarti, A. K. Processes of Technological Innovation, Lexington books (1990).
15. Jo H. and Bang Y., Analyzing ChatGPT adoption drivers with the TOEK framework, NaturePortfolio Scientific Reports 13:22606 (2023)
https://doi.org/10.1038/s41598-023-49710-0
16. Alshammari H., Babu E., The mediating role of satisfaction in the relationship between perceived usefulness, perceived ease of use and students’ behavioural intention to use ChatGPT, NaturePortfolio Scientific Reports 15:7169, (2025)
https://doi.org/10.1038/s41598-025-91634-4
17. Shoufan, A.: Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey. IEEE Access 11, 38805-38818 (2023).
https://doi.org/10.1109/ACCESS.2023.3268224
18. Johnston H., Wells R. F., Shanks E.M., Boey T., Parsons B.N., Student perspectives on the use of generative artificial intelligence technologies in higher education, International Journal for Educational Integrity, 20:2 (2024) https://doi.org/10.1007/s40979-024-00149-4
19. Dobre, S.C., Popescu, E.: Exploring Students’ Perception and Experience with ChatGPT and Critical Thinking in a Higher Education Context. In: 2024 21st International Conference on Information Technology Based Higher Education and Training (ITHET) Proceedings, IEEE (2024). https://doi.org/10.1109/ITHET61869.2024.10837650
20. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., et al. (2025) Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLOS ONE 20(2): e0315011 (2025). https://doi.org/10.1371/journal.pone.0315011
21. Stöhr C., Ou A. W., Malmström H.: Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study, Computers and Education Artificial Intelligence 7:100259,ì (2024) https://doi.org/10.1016/j.caeai.2024.100259
22.Santomartino S. M., & Yi P. H.: Systematic review of radiologist and medical student attitudes on the role and impact of AI in radiology. Academic Radiology, 29 (11), 1748–1756 (2022) https://doi.org/10.1016/j.acra.2021.12.032
23. Laínez Quinde G.A., Tumbaco Muñoz M.Y., Ricardo Suárez J.M, Peñafiel Villarreal R.E.: Perception of university students on the use of artificial intelligence (AI) tools for the development of autonomous learning, Revista de Gestão Social e Ambiental 18(2):e06170 (2024) https://doi.org/10.24857/rgsa.v18n2-136
24. Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1), 2287917 (2023)
https://doi.org/10.1080/2331186X.2023.2287917
25. Hayduk, L., Cummings, G., Stratkotter, R., Nimmo, M., Grygoryev, K., Dosman, D., & Boadu, K. (2003). Pearl’s D-separation: One more step into causal thinking. Structural Equation Modeling, 10(2), 289-311.
26. M. Kalisch, M. Maechler, D. Colombo, M.H. Maathuis and P. Buehlmann (2012).
Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software 47(11) 1–26, http://www.jstatsoft.org/v47/i11/.
27. Vuorikari R., Kluzer S., Punie Y., 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, Luxembourg (2022) https://doi.org/10.2760/490274, JRC128415
https://publications.jrc.ec.europa.eu/repository/bitstream/JRC128415/JRC128415_01.pdf
28. Giovannella C., Passarelli M., Alkhafaji A., Pérez Negrón A.: A comparative study on the effects of the COVID-19 pandemic on three different national university learning ecosystems as bases to derive a Model for the Attitude to get Engaged in Technological Innovation (MAETI), Interaction Design & Architecture(s) – IxD&A Journal, N.47, pp. 167–190 (2020) https://doi.org/10.55612/s-5002-047-008
29. Giovannella C., Cianfriglia L., Giannelli A.: AIs @ School: the perception of the actors of the learning processes., Interaction Design & Architecture(s) – IxD&A Journal, N.62, pp. 123–140 (2024) https://doi.org/10.55612/s-5002-062-008
30. Giovannella C.: “Learning by being”: integrated thinking and competencies to mark the difference from AIs, Interaction Design & Architecture(s) – IxD&A Journal, N.57, pp. 8–26 (2023) https://doi.org/10.55612/s-5002-057-001
31. DEC: AI Literacy Framework (2025)
https://www.digitaleducationcouncil.com/post/digital-education-council-ai-literacy-framework
32. de Kerckhove D.: Brainframes: Technology, Mind and Business, Bosch & Keuning, (1991)
33. Bozkurt A. et al.: The Manifesto for Teaching and Learning in a Time of Generative AI: A Critical Collective Stance to Better Navigate the Future. Open Praxis, 16(4), pp. 487–513 (2024) https://search.informit.org/doi/pdf/10.3316/informit.T2025011300014191575969861 https://doi.org/10.55982/openpraxis.16.4.777
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