This research aims to uncover the factors that might be associated with higher education students’ Artificial Intelligence (AI) literacy. Understanding these factors is crucial, as it can lead to numerous benefits, such as preventing skill gaps and ensuring workforce adaptability. The study used quantitative methodology. One hundred ninety students responded to an online quantitative questionnaire. Measures include a demographic questionnaire, as well as established validated and reliable questionnaires including AI literacy scale, the personal trait of openness to experience, the cognitive appraisals of threat and challenge, and four sub-scales of AI Device Usage Acceptance (AIDUA): social-influence, hedonic-motivation, willingness to accept AI usage, and positive emotions toward AI. Students’ AI literacy correlated positively with their openness to experience, challenge, social-influence, hedonic-motivation, willingness to use, emotions toward AI, and Generative AI (GenAI) usage. The study also provided insights into gender and age differences. As AI literacy is a crucial factor in our professional and daily life, the current findings suggest opportunities for enhancing it through tailored educational programs that foster relevant personal traits, attitudes toward AI, and curriculum development in higher education. This study is significant as it explores the relationship between students’ AI literacy and various variables that have not yet been examined thoroughly, in the context of the rapidly evolving use of GenAI chatbots.
AharonyN (2009) Web 2.0 use by librarians. Library & Information Science Research31(1): 29–37.
2.
AharonyN (2015) An exploratory study on factors affecting the adoption of cloud computing by information professionals. Electronic Library33(2): 308–323.
3.
AharonyNBar-IlanJ (2016) Students’ perceptions on MOOCs: An exploratory study. Interdisciplinary Journal of e-Skills and Life Long Learning12: 145–162.
4.
AharonyNGazitT (2020) Students’ information literacy self-efficacy: An exploratory study. Journal of Librarianship and Information Science52(1): 224–236.
5.
AharonyNGurH (2019) The relationships between personality, perceptual, cognitive and technological variables and students’ level of information literacy. Journal of Librarianship and Information Science51(2): 527–544.
6.
AharonyNShonfeldM (2015) ICT use: Educational technology and library and information science students perspectives – An exploratory study article. Interdisciplinary Journal of e-Skills and Life Long Learning11: 191–207.
7.
AhmedSRasheedT (2020) Relationship between personality traits and digital literacy skills: A study of university librarians. Digital Library Perspectives36(2): 191–206.
8.
AkbarMN (2025) Use of artificial intelligence tools by doctoral students: A mixed-methods explanatory-sequential investigation. Journal of Further and Higher Education49: 995–1013.
9.
AndrewsJEWardHYoonJ (2021) UTAUT as a model for understanding intention to adopt AI and related technologies among librarians. Journal of Academic Librarianship47(6): 102437.
10.
BrownRSillenceEBranley-BellD (2025) AcademAI: Investigating AI usage, attitudes and literacy in higher education and research. Journal of Educational Technology Systems54: 6–33.
11.
Cabrera-SánchezJPVillarejo-RamosÁFLiébana-CabanillasF, et al. (2021) Identifying relevant segments of AI applications adopters – Expanding the UTAUT2’s variables. Telematics and Informatics58: 101529.
12.
ChiuTKFAhmadZIsmailovM, et al. (2024) What are artificial intelligence literacy and competency? A comprehensive framework to support them. Computers and Education Open6: 100171.
13.
CreswellJWCreswellJD (2018) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 5th edn.Sage Publications.
14.
DarlingtonRBHayesAF (2017) Regression Analysis and Linear Models: Concepts, Applications, and Implementation. Guilford Press.
15.
DavisFD (1985) A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral Dissertation, Massachusetts Institute of Technology.
16.
DeshenMAharonyN (2026) Librarians’ and information science professionals’ artificial intelligence literacy: An exploratory study. Journal of Education for Library and Information Science67(2).
DingNChenMHuL (2025) Examining the impact of big five personality traits on generation Z designers’ subscription to paid AI drawing tools using SEM and FsQCA. Scientific Reports15(1): 17587–17619.
19.
DuHSunYJiangH, et al. (2024) Exploring the effects of AI literacy in teacher learning: An empirical study. Humanities and social sciences communications11(1): 1–10.
20.
DurodoluO (2016) Technology acceptance model as a predictor of using information system to acquire information literacy skills. Library Philosophy and Practice (e-journal). 1450 Available at: http://digitalcommons.unl.edu/libphilprac/1450 (accessed 20 December 2025).
21.
GammohLA (2024) ChatGPT in academia: Exploring university students’ risks, misuses and challenges in Jordan. Journal of Further and Higher Education48(6): 608–624.
22.
GokceATDeveci TopalAKolburan GeçerA, et al. (2024) Investigating the level of artificial intelligence literacy of university students using decision trees. Education and Information Technologies30(5): 6765–6784.
23.
GreenSB (1991) How many subjects does it take to do a regression analysis. Multivariate Behavioral Research26(3): 499–510.
24.
GuadagnoREOkdieBMEnoCA (2008) Who blogs? Personality predictors of blogging. Computers in Human Behavior24(5): 1993–2004.
25.
GursoyDChiOHLuL, et al. (2019) Consumers’ acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management49: 157–169.
26.
HairJFBlackWCBabinBJ, et al. (2014) Multivariate Data Analysis, 7th edn.Pearson Education Limited.
27.
HayesAF (ed.) (2022) Introduction to Mediation, Moderation and Conditional Process Analysis: A Regression-Based Approach, 3rd edn.Guilford Press.
28.
JangM (2024) AI literacy and intention to use text-based GenAI for learning: The case of business students in Korea. Informatics11: 54.
29.
JangMAavakareMKimS, et al. (2020) The effects of digital literacy and information literacy on the intention to use digital technologies for learning – A comparative study in Korea and Finland. In: ITS online event, 14–17June. Calgary: International Telecommunications Society (ITS), Econstor.
30.
JohnOPSrivastavaS (1999) The Big Five trait taxonomy: History, measurement and theoretical perspectives. In: JohnOPPervinLA (eds) Handbook of Personality: Theory and Research. Guilford, pp.102–138.
31.
JohnsonPONeymanJ (1936) Test of certain linear hypotheses and the application to some statistical problems. Statistical Research Memoirs1: 57–93.
32.
KimJKlopferMGrohsJR, et al. (2025) Examining faculty and student perceptions of generative AI in university courses. Innovative Higher Education50: 1281–1313.
33.
KlineRB (2016) Principles and Practice of Structural Equation Modeling, 4th edn.Guilford Press.
34.
LazarusRS (1991) Progress on a cognitive-motivational-relational theory of emotion. American Psychologist46(8): 819–834.
35.
LazarusRSFolkmanS (1986) Stress, Appraisal and Coping. Springer Publishing Company.
36.
LongDMagerkoB (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. Association for Computing Machinery.
37.
MakridakisS (2017) The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures90: 46–60.
38.
MansoorHMHBawazirAAlsabriMA, et al. (2024) Artificial intelligence literacy among university students—A comparative transnational survey. Frontiers in Communication9: 1478476.
39.
MartinAJGinnsPBurnsEC, et al. (2021) Assessing instructional cognitive load in the context of students’ psychological challenge and threat orientations: A multi-level latent profile analysis of students and classrooms. Frontiers in Psychology12: 656994.
40.
MaXHuoY (2023) Are users willing to embrace chatgpt? Exploring the factors on the acceptance of chatbots from the perspective of AIDUA framework. Technology and Society75: 102362.
41.
McCraeRRCostaPT (1988) Do parental influences matter? A reply to Halverson. Journal of Personality56(2): 445–449.
42.
McElroyJCHendricksonARTownsendAM, et al. (2007) Dispositional factors in internet use: Personality versus cognitive style. MIS Quarterly31(4): 809–820.
NgDTKLeungJKLChuSKW, et al. (2021) Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence2: 100041.
45.
NierenbergEDahlTI (2023) Is information literacy ability, and metacognition of that ability, related to interest, gender, or education level? A cross-sectional study of higher education students. Journal of Librarianship and Information Science55(1): 57–69.
46.
PintoMSalesDFernández-PascualR (2019) Gender perspective on information literacy: An interdisciplinary and multidimensional analysis within higher education settings. Library & Information Science Research41(4): 100979.
47.
PodsakoffPMMacKenzieSBLeeJY, et al. (2003) Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology88(5): 879–903.
StrzeleckiA (2024) Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education49(2): 223–245.
50.
ValorCAntonettiPCrisafulliB (2022) Emotions and consumers’ adoption of innovations: An integrative review and research agenda. Technological Forecasting and Social Change179: 121609.
51.
VenkateshVDavisFD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science46(2): 186–204.
52.
VenkateshVThongJYLXuX (2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of Technology. MIS Quarterly36(1): 157–178.
53.
VrainEWilsonCKerrL, et al. (2022) Social influence in the adoption of digital consumer innovations for climate change. Energy Policy162: 112800.
54.
WangBRauPLPYuanT (2023) Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology42(9): 1324–1337.
55.
WangKCuiWYuanX (2025) Artificial intelligence in higher education: The impact of need satisfaction on artificial intelligence literacy mediated by self-regulated learning strategies. Behavioral Sciences15(2): 165.
56.
WhitcombKMKalenderZYNokes-MalachTJ, et al. (2020) Comparison of self-efficacy and performance of engineering undergraduate women and men. International Journal of Engineering Education36(6): 1996–2014.
XiaoJAlibakhshiGZamanpourA, et al. (2024) How AI literacy affects students’ educational attainment in online learning: Testing a structural equation model in higher education context. International Review of Research in Open and Distributed Learning25(3): 179–198.
59.
XueLCuiPBDiZ (2023) Identifying the structure patterns to govern the performance of localization in regulating innovation diffusion. New Journal of Physics25(5): 053009.
60.
YangKCShihPH (2020) Cognitive age in technology acceptance: At what age are people ready to adopt and continuously use fashionable products?Telematics and Informatics51: 101400.
61.
YuLLiYFanF (2023) Employees’ appraisals and trust of artificial intelligences’ transparency and opacity. Behavioral Sciences13(4): 344.
62.
YuTKLinMLLiaoYK (2017) Understanding factors influencing information communication technology adoption behavior: The moderators of information literacy and digital skills. Computers in Human Behavior71: 196–208.
63.
Zawacki-RichterOMarínVIBondM, et al. (2019) Systematic review of research on artificial intelligence applications in higher education – where are the educators?International Journal of Educational Technology in Higher Education16(1): 1–27.
64.
ZhaoLWuXLuoH (2022) Developing AI literacy for primary and middle school teachers in China: Based on a structural equation modeling analysis. Sustainability14(21): 14549.
65.
ZilkaGCRahimiIDCohenR (2019) Sense of challenge, threat, self-efficacy and motivation of students learning in virtual and blended courses. American Journal of Distance Education33(1): 2–15.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
