Department of Property Protection and Security, Amasya University, Amasya, 05100, Türkiye
Abstract
This study examined the relationships among artificial intelligence (AI) literacy, AI acceptance, and attitudes toward AI among students enrolled in occupational health and safety (OHS) programs, as well as whether these variables differed according to various demographic characteristics. A multi-centre, cross-sectional relational survey design was adopted, and data were collected online from OHS students enrolled in associate, undergraduate, and graduate programmes at three state universities in Türkiye. Of the 253 responses initially obtained, 29 were removed after data cleaning based on a control item and straight-lining criteria, leaving a final analytic sample of N=224. The AI Literacy Scale, the short form of the AI Acceptance Scale, and the General Attitudes towards Artificial Intelligence Scale were administered. Descriptive statistics, independent-samples t tests, the Kruskal-Wallis H test, and Pearson correlation analysis were used to analyse the data. The findings indicated that scale scores did not differ significantly by gender, AI training, employment status, age, or grade point average. In contrast, scores differed significantly by frequency of AI tool use for positive acceptance (H=50.28, p<0.001, ε²=0.21) and for overall attitude (H=29.74, p<0.001, ε²=0.12); students who reported never using AI scored lower than those who did. AI literacy was positively and moderately-to-strongly associated with positive acceptance (r=0.59) and positive attitude (r=0.63). The findings suggest that the level of interaction with AI covaries with positive acceptance and attitudes. Because the design is cross-sectional, these associations cannot be interpreted causally, and the convenience sample drawn from three universities limits generalisation to OHS students more broadly.
Keywords
AI literacy,AI acceptance,AI attitude,occupational health and safety,university students
How to Cite
Yurtcu, A. (2026). Examining the relationships among artificial intelligence literacy, acceptance, and attitudes of occupational health and safety students. International Journal of Eurasia Social Sciences, 17(65), 1250–1270. https://doi.org/10.70736/ijoess.2512
📄Acosta-Enriquez, B. G., Arbulú Pérez Vargas, C. G., Huamaní Jordan, O., Arbulú Ballesteros, M. A., & Paredes Morales, A. L. (2024). Exploring attitudes toward ChatGPT among college students: An empirical analysis of cognitive, affective, and behavioral components using path analysis. Computers and Education: Artificial Intelligence, 7, 100320. [Crossref]
📄Al-Nasa’h, M., Al-Tarawneh, L., & Alhwayan, O. (2026). Artificial intelligence anxiety, digital well-being, and future career concerns among engineering and information technology students in Jordan. Frontiers in Artificial Intelligence, 9, Article 1598741. [Crossref]
📄Avrupa Komisyonu, Communications Networks, Content & Technology Genel Müdürlüğü. (2017). Attitudes towards the impact of digitisation and automation on daily life: Report. Avrupa Komisyonu. [Crossref]
📄Batuk, B., Aktu, Y., & Türk, N. (2025). Yapay zekâ kabul ölçeği kısa formu’nun psikometrik özelliklerinin incelenmesi. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Özel Sayı), 438–451. [Crossref]
📄Becker, D. (2018). Possibilities to improve online mental health treatment: Recommendations for future research and developments. In Advances in Information and Communication Networks (Vol. 886, pp. 91–112). Springer International Publishing.
📄Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial intelligence in FinTech: Understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430. [Crossref]
📄Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy. Computers and Education: Artificial Intelligence, 8, 100340. [Crossref]
📄Büyüköztürk, Ş., Kılıç Çakmak, E., Akgün, Ö. E., Karadeniz, Ş., & Demirel, F. (2020). Bilimsel araştırma yöntemleri (29. baskı). Pegem Akademi.
📄Cacioppo, J. T., Gardner, W. L., & Berntson, G. G. (1997). Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space. Personality and Social Psychology Review, 1(1), 3–25. [Crossref]
📄Casal-Otero, L., Catala, A., Fernández-Morante, C., Taboada, M., Cebreiro, B., & Barro, S. (2023). AI literacy in K-12: A systematic literature review. International Journal of STEM Education, 10, Article 29. [Crossref]
📄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. [Crossref]
📄Chocarro, R., Cortiñas, M., & Marcos-Matás, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 1–19. [Crossref]
📄Cho, K. A., & Seo, Y. H. (2024). Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: A descriptive study. BMC Nursing, 23, Article 212. [Crossref]
📄Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
📄Cordenonzi, W. H., Del Pino, J. C., Oliveira, E. C., & Guimaraes Strohschoen, A. A. (2020). Literacy: An evolution of the concept: Literacy and code literacy. Texto Livre: Linguagem e Tecnologia, 13(1), 137–155. [Crossref]
📄Cumhurbaşkanlığı Dijital Dönüşüm Ofisi [CBDDO]. (2024). Ulusal yapay zekâ stratejisi 2024–2025 eylem planı. T.C. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi. https://cbddo.gov.tr
📄Dahri, N. A., Yahaya, N., & Al-Rahmi, W. M. (2024). Exploring the influence of ChatGPT on student academic success and career readiness. Education and Information Technologies, 1–45. [Crossref]
📄Darko, A., Chan, A. P., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081. [Crossref]
📄Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Crossref]
📄De Winter, J., Dodou, D., & Eisma, Y. B. (2024). Personality and acceptance as predictors of ChatGPT use. Discover Psychology, 4, Article 57. [Crossref]
📄Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … et al. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. [Crossref]
📄Erebak, S., & Turgut, T. (2021). Anxiety about the speed of technological development: Effects on job insecurity, time estimation, and automation level preference. The Journal of High Technology Management Research, 32(2), 100419. [Crossref]
📄Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. [Crossref]
📄George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A simple guide and reference (15th ed.). Routledge.
📄Gillespie, N., Lockey, S., & Curtis, C. (2021). Trust in artificial intelligence: A five country study. The University of Queensland and KPMG Australia. [Crossref]
📄Gnambs, T., & Appel, M. (2019). Are robots becoming unpopular? Changes in attitudes towards autonomous robotic systems in Europe. Computers in Human Behavior, 93, 53–61. [Crossref]
📄Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. [Crossref]
📄Harshvardhan, G. M., Gourisaria, M. K., Pandey, M., & Rautaray, S. S. (2020). A comprehensive survey and analysis of generative models in machine learning. Computer Science Review, 38, 100285. [Crossref]
📄Hopcan, S., Türkmen, G., & Polat, E. (2023). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 29, 7281–7301. [Crossref]
📄Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. [Crossref]
📄International Labour Organization [ILO]. (2023). A call for safer and healthier working environments. International Labour Organization. https://www.ilo.org
📄Kaarlela, T., Pieskä, S., & Pitkäaho, T. (2020). Digital twin and virtual reality for safety training. In 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom) (pp. 115–120). IEEE. [Crossref]
📄Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. [Crossref]
📄Karasar, N. (2016). Bilimsel araştırma yöntemi: Kavramlar, ilkeler, teknikler. Nobel Akademik Yayıncılık.
📄Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. [Crossref]
📄Kaye, S. A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis & Prevention, 137, 105441. [Crossref]
📄Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. [Crossref]
📄Kılıç, Y., Ata, H. A., & Seyrek, İ. H. (2015). Finansal okuryazarlık: Üniversite öğrencilerine yönelik bir araştırma. Muhasebe ve Finansman Dergisi, 66, 129–150. [Crossref]
📄Kim, K., Cho, Y. K., & Zhang, S. (2016). Integrating work sequences and temporary structures into safety planning: Automated scaffolding-related safety hazard identification and prevention in BIM. Automation in Construction, 70, 128–142. [Crossref]
📄Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
📄Laupichler, M. C., Aster, A., Meyerheim, M., Raupach, T., & Mergen, M. (2024). Medical students’ AI literacy and attitudes towards AI: A cross-sectional two-center study using pre-validated assessment instruments. BMC Medical Education, 24, Article 401. [Crossref]
📄Li, J., & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410. [Crossref]
📄Li, X., Yi, W., Chi, H. L., Wang, X., & Chan, A. P. (2018). A critical review of virtual and augmented reality (VR/AR) applications in construction safety. Automation in Construction, 86, 150–162. [Crossref]
📄Lieto, A., Bhatt, M., Oltramari, A., & Vernon, D. (2018). The role of cognitive architectures in general artificial intelligence. Cognitive Systems Research, 48, 1–3. [Crossref]
📄Lintner, T. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, Article 50. [Crossref]
📄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). Association for Computing Machinery. [Crossref]
📄Lund, B. D., Mannuru, N. R., & Agbaji, D. (2024). AI anxiety and fear: A look at perspectives of information science students and professionals towards artificial intelligence. Journal of Information Science. [Crossref]
📄Martin, B. A., Jin, H. S., Wang, D., Nguyen, H., Zhan, K., & Wang, Y. X. (2020). The influence of consumer anthropomorphism on attitudes towards artificial intelligence trip advisors. Journal of Hospitality and Tourism Management, 44, 108–111. [Crossref]
📄Meade, A. W., & Craig, S. B. (2012). Identifying careless responses in survey data. Psychological Methods, 17(3), 437–455. [Crossref]
📄Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: An application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816–1844. [Crossref]
📄Montag, C., Kraus, J., Baumann, M., & Rozgonjuk, D. (2023). The propensity to trust in (automated) technology mediates the links between technology self-efficacy and fear and acceptance of artificial intelligence. Computers in Human Behavior Reports, 11, 100315. [Crossref]
📄Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., … et al. (2021). Artificial intelligence for good health: A scoping review of the ethics literature. BMC Medical Ethics, 22, Article 1–17. [Crossref]
📄Neudert, L. M., Knuutila, A., & Howard, P. N. (2020). Global attitudes towards AI, machine learning & automated decision making. Oxford Commission on AI & Good Governance, Oxford Internet Institute.
📄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. [Crossref]
📄Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
📄Park, H., Kim, H. S., & Park, H. W. (2021). A scientometric study of digital literacy, ICT literacy, information literacy, and media literacy. Journal of Data and Information Science, 6(2), 116–138. [Crossref]
📄Park, I., Kim, D., Moon, J., Kim, S., Kang, Y., & Bae, S. (2022). Searching for new technology acceptance model under social context: Analyzing the determinants of acceptance of intelligent information technology in digital transformation and implications for the requisites of digital sustainability. Sustainability, 14(1), 579. [Crossref]
📄Park, J., & Woo, S. E. (2022). Who likes artificial intelligence? Personality predictors of attitudes toward artificial intelligence. The Journal of Psychology, 156(1), 68–94. [Crossref]
📄Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., ... et al. (2019). Medical students’ attitude towards artificial intelligence: A multicentre survey. European Radiology, 29(4), 1640–1646. [Crossref]
📄Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. [Crossref]
📄Polatgil, M., & Güler, A. (2023). Yapay zekâ okuryazarlığı ölçeğinin Türkçe’ye uyarlanması. Sosyal Bilimlerde Nicel Araştırmalar Dergisi, 3(2), 99–114.
📄Reinhart, R. J. (2018, March 6). Most Americans already using artificial intelligence products. Gallup. https://news.gallup.com
📄Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. [Crossref]
📄Schepman, A., & Rodway, P. (2022). The general attitudes towards artificial intelligence scale (GAAIS): Confirmatory validation and associations with personality, corporate distrust, and general trust. International Journal of Human–Computer Interaction, 39(13), 2724–2741. [Crossref]
📄Schwab, K. (2017). The fourth industrial revolution. World Economic Forum.
📄Shah, I. A., & Mishra, S. (2024). Artificial intelligence in advancing occupational health and safety: An encapsulation of developments. Journal of Occupational Health, 66(1), uiad017. [Crossref]
📄Shehadeh, A., & Alshboul, O. (2025). Enhancing occupational safety in construction: Predictive analytics using advanced ensemble machine learning algorithms. Engineering Applications of Artificial Intelligence, 159, 111761. [Crossref]
📄Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., … et al. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. Künstliche Intelligenz, 35(1), 109–118. [Crossref]
📄Sindermann, C., Yang, H., Elhai, J. D., Yang, S., Quan, L., Mei, L., … et al. (2022). Acceptance and fear of artificial intelligence: Associations with personality in a German and a Chinese sample. Discover Psychology, 2, 1–12. [Crossref]
📄Sosyal Güvenlik Kurumu [SGK]. (2024). 2023 yılı SGK istatistik yıllığı. T.C. Çalışma ve Sosyal Güvenlik Bakanlığı. https://www.sgk.gov.tr
📄Stein, J. P., Messingschlager, T., Gnambs, T., Hutmacher, F., & Appel, M. (2024). Attitudes towards AI: Measurement and associations with personality. Scientific Reports, 14, Article 2909. [Crossref]
📄Stephanidis, C., Salvendy, G., Antona, M., Chen, J. Y. C., Dong, J., Duffy, V. G., … et al. (2019). Seven HCI grand challenges. International Journal of Human–Computer Interaction, 35(14), 1229–1269. [Crossref]
📄Stöhr, C., Ou, A. W., & Malmström, H. (2024). 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. [Crossref]
📄Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
📄Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339–343. [Crossref]
📄Tang, K. H. D. (2024). Artificial intelligence in occupational health and safety risk management of construction, mining, and oil and gas sectors: Advances and prospects. Journal of Engineering Research and Reports, 26(6), 241–253. [Crossref]
📄Tegmark, M. (2018). Life 3.0: Being human in the age of artificial intelligence. Vintage.
📄Tekin, Ö. G. (2024). Factors affecting teachers’ acceptance of artificial intelligence technologies: Analyzing teacher perspectives with structural equation modeling. Instructional Technology and Lifelong Learning, 5(2), 399–420. [Crossref]
📄Terzi, R. (2020). An adaptation of artificial intelligence anxiety scale into Turkish: Reliability and validity study. International Online Journal of Education and Teaching, 7(4), 1501–1515.
📄Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and Software Technology, 52(5), 463–479. [Crossref]
📄Vatan, A., & Doğan, S. (2021). What do hotel employees think about service robots? A qualitative study in Turkey. Tourism Management Perspectives, 37, 100775. [Crossref]
📄Velardo, S. (2015). The nuances of health literacy, nutrition literacy, and food literacy. Journal of Nutrition Education and Behavior, 47(4), 385–389. [Crossref]
📄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. [Crossref]
📄Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. [Crossref]
📄Vu, H. T., & Lim, J. (2022). Effects of country and individual factors on public acceptance of artificial intelligence and robotics technologies: A multilevel SEM analysis of 28-country survey data. Behaviour & Information Technology, 41(7), 1515–1528. [Crossref]
📄Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. [Crossref]
📄Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619–634. [Crossref]
📄Yılmaz, C. (2026). Examination of intergenerational artificial intelligence attitudes of employees in hospitality enterprises: A mixed methods study. International Journal of Eurasia Social Sciences, 17(64), 615–631. [Crossref]
📄Yılmaz, F. G. K., Yılmaz, R., & Ceylan, M. (2024). Generative artificial intelligence acceptance scale: A validity and reliability study. International Journal of Human–Computer Interaction, 40(24), 8703–8715. [Crossref]
📄Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 9(2, Pt. 2), 1–27. [Crossref]
📄Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. SSRN. [Crossref]
📄Zhao, C., Liu, T., & Gao, B. (2026). Latent profiles of college students’ AI literacy and their association with AI acceptance: The sequential indirect role of perceived affordances and growth mindset. Frontiers in Psychology, 17, Article 1926324. [Crossref]