öhüi̇i̇bfd Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2564-6931 Niğde Ömer Halisdemir Üniversitesi 10.25287/ohuiibf.1721842 Management Information Systems Artificial Intelligence (Other) Demography (Other) Human Resources and Industrial Relations (Other) Business Administration Yönetim Bilişim Sistemleri Yapay Zeka (Diğer) Demografi (Diğer) İnsan Kaynakları ve Endüstriyel İlişkiler (Diğer) İşletme Artificial Intelligence Anxiety Among Potential Employees: An Evaluation in Terms of Sector Preference, Digital Competence Level and Demographic Characteristics Potansiyel Çalışanlarda Yapay Zekâ Kaygısı: Sektör Tercihi, Dijital Yetkinlik Düzeyi ve Demografik Özellikler Açısından Bir Değerlendirme https://orcid.org/0000-0002-9287-4754 Kılıç Selçuk AKSARAY ÜNİVERSİTESİ 04 05 2026 19 1 27 06 17 2025 03 27 2026 Copyright © 2008, Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 2008 Ömer Halisdemir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi

This study aims to examine the levels of artificial intelligence (AI) anxiety among prospective employees (university students) in relation to sectoral preferences, digital competency levels, and demographic characteristics. AI technologies are perceived in diverse ways by individuals, either as a professional risk or as a career opportunity. This study evaluates how such perceptions are associated with individual and occupational variables through a quantitative research model. Data collected from 1,382 students at a public university in Turkey revealed that AI anxiety levels were higher among those who preferred to work in the public sector, had lower digital competency, and lacked work experience. Additionally, female participants and second-year students reported higher levels of AI anxiety. In contrast, participants with entrepreneurial career aspirations demonstrated significantly lower levels of AI anxiety. This finding suggests that entrepreneurial individuals tend to perceive AI more as a source of innovation and opportunity. Overall, the findings indicate that enhancing digital competencies may foster more positive attitudes toward AI and offer valuable insights for educational and career planning policies.

Bu çalışma, yapay zekâ teknolojilerinin iş gücü üzerindeki etkilerine ilişkin potansiyel çalışanların (üniversite öğrencileri) yapay zekâya yönelik kaygı düzeylerini; sektör tercihi, dijital yetkinlik düzeyi ve demografik özellikler bağlamında incelemeyi amaçlamaktadır. Yapay zekâ teknolojileri, bireyler tarafından mesleki risk veya kariyer fırsatı olarak farklı şekillerde algılanabilmektedir. Bu farklı algıların hangi bireysel ve mesleki değişkenlerle ilişkili olduğu nicel bir araştırma modeliyle değerlendirilmiştir. Türkiye’deki bir devlet üniversitesinin 1382 öğrencisinden elde edilen veriler yapay zekâ kaygısının özellikle kamu sektörü yönelimi olan, dijital yetkinlik düzeyi düşük ve iş deneyimi bulunmayan bireylerde daha yüksek olduğunu göstermiştir. Ayrıca kadın katılımcılar ile ikinci sınıf öğrencilerinin kaygı düzeylerinin daha yüksek olduğu belirlenmiştir. Buna karşılık, girişimcilik yöneliminde bulunan katılımcıların yapay zekâ kaygısı düzeylerinin daha düşük olduğu saptanmıştır. Bu sonuç, girişimci adaylarının yapay zekâyı daha çok bir yenilik ve fırsat kaynağı olarak değerlendirdiklerini düşündürmektedir. Bulgular dijital yetkinliğin geliştirilmesinin yapay zekâya yönelik olumlu tutumları destekleyebileceğini ortaya koymakta, eğitim ve kariyer planlaması açısından önemli öneriler sunmaktadır.

 Yapay Zekâ Kaygısı Dijital Yetkinlik Sektör Tercihi Potansiyel Çalışanlar İş Tecrübesi Artificial Intelligence Anxiety Digital Competence Sector Preference Potential Employees Work Experience Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets. Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716 Ahn, M. J., & Chen, Y.-C. (2022). Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government. Government Information Quarterly, 39(2). https://doi.org/10.1016/j.giq.2021.101664 Akçakanat, Ö. (2024). The impact of artificial intelligence anxiety on technology-induced unemployment concerns: A study on accounting professionals. Afyon Kocatepe University Journal of Economics and Administrative Sciences, 26(Özel Sayı), 53–67. https://doi.org/10.33707/akuiibfd.1458358 Akkaya, B., Özkan, A., & Özkan, H. (2021). Yapay zeka kaygı (YZK) ölçeği: Türkçeye uyarlama, geçerlik ve güvenirlik çalışması. Alanya Akademik Bakış, 5(2), 1125–1146. https://doi.org/10.29023/alanyaakademik.833668 Asad, M. M., & Ajaz, A. (2024). Impact of ChatGPT and generative AI on lifelong learning and upskilling learners in higher education: Unveiling the challenges and opportunities globally. The International Journal of Information and Learning Technology, 41(5), 507–523. https://doi.org/10.1108/IJILT-06-2024-0103 Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace automation. Journal of economic perspectives, 29(3), 3–30. https://doi.org/10.1257/jep.29.3.3 Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company. Belber, B. G., & Özmen, M. H. (2024). Hizmet sektörü çalışanlarının yapay zekâ ile ilgili gelecek kaygıları. Elektronik Sosyal Bilimler Dergisi, 23(91), 1085–1101. https://doi.org/10.17755/esosder.1437531 Bessen, J. (2020). Automation and jobs: When technology boosts employment. Economic Policy, 34(100), 589–626. https://doi.org/10.1093/epolic/eiaa001 Bharadiya, J. P., Thomas, R. K., & Ahmed, F. (2023). Rise of artificial intelligence in business and industry. Journal of Engineering Research and Reports, 25(3), 85–103. https://doi.org/10.9734/jerr/2023/v25i3893 Bıyıklı, F., Bozdoğan, T., & Çetin, Ö. O. (2024). Kişilik özelliklerinin yapay zekâ tutumuna etkisinde statü kaygısının ılımlaştırıcı rolü: Muhasebe meslek mensupları üzerine ampirik bir çalışma. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(Özel Sayı), 117–132. https://doi.org/10.33707/akuiibfd.1462678 Bozkurt, A. (2023). ChatGPT, generative artificial intelligence and algorithmic paradigm shift. Journal of Critical Review of Educational Sciences, 4(1), 63–72. https://doi.org/10.59320/alanyazin.1283282 Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: W. W. Norton & Company. Büyüköztürk, Ş. (2024). Sosyal bilimler için veri analizi el kitabı: İstatistik, araştırma deseni, SPSS uygulamaları ve yorum (31 b.). Ankara: Pegem Akademi Yayıncılık. Cantas, Ç., Soyer, C., & Batur, Ö. (2024). Examination of undergraduate students’ artificial intelligence anxiety, multidimensional 21st century skills, and lifelong learning levels in terms of various variables. Turkish Online Journal of Educational Technology-TOJET, 23(3), 29–53. https://files.eric.ed.gov/fulltext/EJ1434130.pdf 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 Chen, C., Hu, W., & Wei, X. (2025). From anxiety to action: exploring the impact of artificial intelligence anxiety and artificial intelligence self-efficacy on motivated learning of undergraduate students. Interactive Learning Environments, 33(4), 3162–3177. https://doi.org/10.1080/10494820.2024.2440877 Chiu, Y.-T., Zhu, Y.-Q., & Corbett, J. (2021). In the hearts and minds of employees: A model of pre-adoptive appraisal toward artificial intelligence in organizations. International Journal of Information Management, 60, 102379. https://doi.org/10.1016/j.ijinfomgt.2021.102379 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2 b.). Hillsdale, New Jersey: Lawrence Earlbaum Associates. Çetiner, N., & Çetinkaya, F. Ö. (2024). Çalışanların yapay zekâ kaygısı ile motivasyon düzeyleri arasındaki ilişki: Turizm çalışanları üzerine bir araştırma. Alanya Akademik Bakış, 8(1), 159–173. https://doi.org/10.29023/alanyaakademik.1297394 Diamantopoulos, A., Sarstedt, M., Fuchs, C., Wilczynski, P., & Kaiser, S. (2012). Guidelines for choosing between multi-item and single-item scales for construct measurement: A predictive validity perspective. Journal of the Academy of Marketing Science, 40(3), 434–449. https://doi.org/10.1007/s11747-011-0300-3 Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709–1734. https://doi.org/10.1007/s10796-021-10186-w Eyüp, B., & Kayhan, S. (2023). Pre-service turkish language teachers’ anxiety and attitudes toward artificial intelligence. International Journal of Education and Literacy Studies, 11(4), 43–56. https://doi.org/10.7575/aiac.ijels.v.11n.4p.43 Ferrari, A. (2012). Digital competence in practice: An analysis of frameworks [EUR 25351 EN]. Seville, Spain: Joint Research Centre of the European Commission Publications Office. https://doi.org/10.2791/82116 Field, A. (2024). Discovering statistics using IBM SPSS statistics (6 b.). London: SAGE. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 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. https://doi.org/10.1016/j.techfore.2016.08.019 Georgieff, A., & Hyee, R. (2021). Artificial intelligence and employment: New cross-country evidence. İçinde OECD social, employment and migration working papers no. 265. Paris: OECD Publishing. https://doi.org/10.1787/c2c1d276-en Gogol, K., Brunner, M., Goetz, T., Martin, R., Ugen, S., Keller, U., Fischbach, A., & Preckel, F. (2014). “My questionnaire is too long!” The assessments of motivational-affective constructs with three-item and single-item measures. Contemporary Educational Psychology, 39(3), 188–205. https://doi.org/10.1016/j.cedpsych.2014.04.002 Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669 Göldağ, B. (2021). Investigation of the relationship between digital literacy levels and digital data security awareness levels of university students. E-International Journal of Educational Research, 12(3), 82–100. https://doi.org/10.19160/e-ijer.950635 Gryncewicz, W., Zygała, R., & Pilch, A. (2023). AI in HRM: Case study analysis. Preliminary research. Procedia Computer Science, 225, 2351–2360. https://doi.org/10.1016/j.procs.2023.10.226 Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925 Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8 b.). China: Cengage Learning EMEA. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3 b.). Sage. Hasırcı, I., & Örücü, E. (2024). Öz yeterlilik algısı ve örgütsel hazırlığın yapay zeka kaygısı üzerindeki etkileri: Bir araştırma. Sinop Üniversitesi Sosyal Bilimler Dergisi, 8(2), 732–760. https://doi.org/10.30561/sinopusd.1384443 Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 Hopcan, S., Türkmen, G., & Polat, E. (2024). Exploring the artificial intelligence anxiety and machine learning attitudes of teacher candidates. Education and Information Technologies, 29(6), 7281–7301. https://doi.org/10.1007/s10639-023-12086-9 Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118 Huttayavilaiphan, R. (2025). Digital competence and anxiety in Thai university English education: Impacts on teachers and students. World Journal of English Language, 15(1), 225–251. https://doi.org/10.5430/wjel.v15n1p225 Ilomäki, L., Paavola, S., Lakkala, M., & Kantosalo, A. (2016). Digital competence – an emergent boundary concept for policy and educational research. Education and Information Technologies, 21(3), 655–679. https://doi.org/10.1007/s10639-014-9346-4 Johnson, D. G., & Verdicchio, M. (2017a). AI anxiety. Journal of the Association for Information Science and Technology, 68(9), 2267–2270. https://doi.org/10.1002/asi.23867 Johnson, D. G., & Verdicchio, M. (2017b). Reframing AI discourse. Minds and Machines, 27(4), 575–590. https://doi.org/10.1007/s11023-017-9417-6 Kang, J., Shin, H., & Kang, C. (2024). Hospitality labor leakage and dynamic turnover behaviors in the age of artificial intelligence and robotics. Journal of Hospitality and Tourism Technology, 15(5), 916–933. https://doi.org/10.1108/JHTT-12-2023-0411 Kappeler, K. (2024). A longitudinal perspective on digital skills for everyday life: Measurement and empirical evidence. Media and Communication, 12(Article 8159). https://doi.org/10.17645/mac.8159 Karaarslan, M. H., & Altuntaş, B. (2016). Kariyer tercihini etkileyen faktör düzeylerinin öneminin konjoint analizi ile belirlenmesi. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 5(7), 1972–1988. https://doi.org/10.15869/itobiad.259499 Kaya, F., Fatih, A., Astrid, S., Paul, R., Okan, Y., & and 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. https://doi.org/10.1080/10447318.2022.2151730 Kaya, F., Yetişensoy, O., Aydın, F., & Demir Kaya, M. (2024). Yapay zekâ korkusu ölçeğinin Türkçe’ye uyarlanması. Ordu Üniversitesi Sosyal Bilimler Araştırmaları Dergisi, 14(2), 554–567. https://doi.org/10.48146/odusobiad.1264103 Kaya, M. (2023). Havacılık yönetimi öğrencilerinin yapay zekâ kaygısı ve teknoloji kaynaklı işsizlik endişesi [Yayınlanmamış Yüksek Lisans Tezi, Eskişehir Teknik Üniversitesi]. Ulusal Tez Merkezi, Tez No: 809085. Eskişehir. Kerr, S. P., Kerr, W. R., & Dalton, M. (2019). Risk attitudes and personality traits of entrepreneurs and venture team members. Proceedings of the National Academy of Sciences, 116(36), 17712–17716. https://doi.org/10.1073/pnas.1908375116 Kim, S. Y., & Fernandez, S. (2017). Employee empowerment and turnover intention in the U.S. federal bureaucracy. The American Review of Public Administration, 47(1), 4–22. https://doi.org/10.1177/0275074015583712 Kline, R. B. (2023). Principles and practice of structural equation modeling series: Methodology in the social sciences (4 b.). New York: The Guilford Press. Koçer, S. (2024). Yapay zeka ve doğrulama. REFLEKTİF Sosyal Bilimler Dergisi, 5(1), 103–114. https://doi.org/10.47613/reflektif.2024.147 Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122. https://doi.org/10.1006/jvbe.1994.1027 Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory (4 b.). San Francisco, CA: Jossey-Bass. Li, J., Bonn, M. A., & Ye, B. H. (2019). Hotel employee’s artificial intelligence and robotics awareness and its impact on turnover intention: The moderating roles of perceived organizational support and competitive psychological climate. Tourism Management, 73, 172–181. https://doi.org/10.1016/j.tourman.2019.02.006 Li, L., Bibi, M., Hussain, Y., & Xiao, D. (2025). Examining sustainable hospitality practices and employee turnover in Pakistan: The interplay of robotics awareness, mutual trust, and technical skills development in the age of artificial intelligence. Journal of Environmental Management, 373, 123922. https://doi.org/10.1016/j.jenvman.2024.123922 LimeSurvey. (2026). LimeSurvey: An open source survey tool. LimeSurvey GmbH. Erişim Adresi https://www.limesurvey.org Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. https://doi.org/10.1016/j.jbusres.2020.11.001 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, 01655515241282001. https://doi.org/10.1177/01655515241282001 Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, L., Batra, P., Ko, R., & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#/ Minbaeva, D. (2021). Disrupted HR? Human Resource Management Review, 31(4). https://doi.org/10.1016/j.hrmr.2020.100820 Mutascu, M. (2021). Artificial intelligence and unemployment: New insights. Economic Analysis and Policy, 69, 653–667. https://doi.org/10.1016/j.eap.2021.01.012 Mutlu, T. (2023). Yapay zeka ve girişimcilik. İçinde G. Durmuş & M. Seyhan (Ed.), Yeni dünyada girişimcilik (ss. 1–18). Ankara: Gazi Kitabevi. Nahar, A., Sharma, P., & Thakur, M. (2022). Preference for entrepreneurship as career: Exploring some aspects. Journal of Positive School Psychology, 6(6), 3192–3199. https://journalppw.com/index.php/jpsp/article/view/3819 Neumann, O., Katharina, G., & Steiner, R. (2024). Exploring artificial intelligence adoption in public organizations: a comparative case study. Public Management Review, 26(1), 114–141. https://doi.org/10.1080/14719037.2022.2048685 Ng, W. (2012). Can we teach digital natives digital literacy? Computers & Education, 59(3), 1065–1078. https://doi.org/10.1016/j.compedu.2012.04.016 Nguyen, Q. P., & Vo, D. H. (2022). Artificial intelligence and unemployment: An international evidence. Structural Change and Economic Dynamics, 63, 40–55. https://doi.org/10.1016/j.strueco.2022.09.003 Özbek, A. (2024). A study on artificial intelligence concerns of professional accountants and perceptions of future employability. Alanya Academic Review, 8(1), 254–267. https://doi.org/10.29023/alanyaakademik.1329511 Özkoç, H., Marangoz, M., & Aydın, A. E. (2018). Öğrencilerin kariyer alternatiflerini etkileyen faktörler ve girişimcilik eğitimi. Uluslararası İktisadi ve İdari Bilimler Dergisi, 4(1), 98–119. https://doi.org/10.29131/uiibd.413610 Pan, Y., & Froese, F. J. (2023). An interdisciplinary review of AI and HRM: Challenges and future directions. Human Resource Management Review, 33(1). https://doi.org/10.1016/j.hrmr.2022.100924 Paredes-Aguirre, M., Hernandez-Pozas, O., Ayala, Y., & Campoverde Aguirre, R. (2025). The stress-busting power of digital self-efficacy: Does training format matter for workers? Current Research in Behavioral Sciences, 8, 100161. https://doi.org/10.1016/j.crbeha.2024.100161 Patil, D. (2024). Impact of artificial intelligence on employment and workforce development: Risks, opportunities, and socioeconomic implications. Opportunities, And Socioeconomic Implications(November 12). https://doi.org/10.2139/ssrn.5057396 Pérez-Navío, E., Ocaña-Moral, M. T., & Martínez-Serrano, M. d. C. (2021). University graduate students and digital competence: Are future secondary school teachers digitally competent? Sustainability, 13(15), 8519. https://doi.org/10.3390/su13158519 Priyadarshi, P., & Premchandran, R. (2022). Insecurity and turnover as robots take charge: impact of neuroticism and change-related uncertainty. Personnel Review, 51(1), 21–39. https://doi.org/10.1108/PR-06-2019-0310 Ratten, V. (2020). Coronavirus (covid-19) and entrepreneurship: Changing life and work landscape. Journal of Small Business & Entrepreneurship, 32(5), 503–516. https://doi.org/10.1080/08276331.2020.1790167 Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4. Bönningstedt: SmartPLS. Retrieved from https://www.smartpls.com Ropohl, G. (1999). Philosophy of socio-technical systems. Techné: Research in Philosophy and Technology, 4(3), 186–194. https://doi.org/10.5840/techne19994311 Russo, C., Romano, L., Clemente, D., Iacovone, L., Gladwin, T. E., & Panno, A. (2025). Gender differences in artificial intelligence: the role of artificial intelligence anxiety. Frontiers in Psychology, 16, 1559457. https://doi.org/10.3389/fpsyg.2025.1559457 Sanguinetti, P., & Palomo, B. (2024). An alien in the newsroom: AI anxiety in European and American newspapers. Social Sciences, 13(11), 608. https://doi.org/10.3390/socsci13110608 Schein, E. H. (1992). How can organizations learn faster? The challenge of entering the green room. MIT Sloan Management Review, 34(2), 85–92. https://core.ac.uk/download/pdf/4380058.pdf Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014 Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23–74. https://doi.org/10.23668/psycharchives.12784 Shandilya, E., & Fan, M. (2022). Understanding older adults’ perceptions and challenges in using AI-enabled everyday technologies. İçinde The Tenth International Symposium of Chinese CHI (Chinese CHI 2022) (ss. 105–116). Guangzhou and Online, China: ACM. https://doi.org/10.1145/3565698.3565774 Şeker, C., Örücü, E., & Ercan Önbıçak, A. (2024). Örgütlerde inovasyon, örgütsel öğrenme ilişkisinde yapay zekâ kaygısının rolü. İşletme Araştırmaları Dergisi, 16(1), 328–345. https://doi.org/10.20491/isarder.2024.1793 Takıl, N., Erden, N. K., & Sarı, A. B. (2022). Farklı meslek grubu adaylarının yapay zekâ teknolojisine yönelik kaygı seviyesinin incelenmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 25(48), 343–353. https://doi.org/10.31795/baunsobed.1165386 Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Policies for the future (Y. Punie, R. Vuorikari, & M. Cabrera, Ed.) [EUR 29442 EN]. Luxembourg: Publications Office of the European Union. https://doi.org/10.2760/12297 Uçar, M., Çapuk, H., & Yiğit, M. F. (2024). The relationship between artificial intelligence anxiety and unemployment anxiety among university students. WORK, 80(2), 701–710. https://doi.org/10.1177/10519815241290648 Ulukapı Yılmaz, H., & Yılmaz, A. (2024). Dijital çağın potansiyel çalışanlarının yapay zekâ kaygılarının belirlenmesi. Business and Economics Research Journal, 15(2), 171–188. https://doi.org/10.20409/berj.2024.440 Van Laar, E., Van Deursen, A. J. A. M., Van Dijk, J. A. G. M., & De Haan, J. (2017). The relation between 21st-century skills and digital skills: A systematic literature review. Computers in Human Behavior, 72, 577–588. https://doi.org/10.1016/j.chb.2017.03.010 Van Laar, E., Van Deursen, A. J. A. M., Van Dijk, J. A. G. M., & De Haan, J. (2019). Determinants of 21st-century digital skills: A large-scale survey among working professionals. Computers in Human Behavior, 100, 93–104. https://doi.org/10.1016/j.chb.2019.06.017 Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24(1), 115–139. https://doi.org/10.2307/3250981 Venkatesh, V., Thong, J. Y. L., & 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. https://doi.org/10.2307/41410412 Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens [EUR 31006 EN]. Publications Office of the European Union. https://doi.org/10.2760/115376 Wang, Y.-M., Chung-Lun, W., Hsin-Hui, L., Sheng-Ching, W., & and Wang, Y.-S. (2024). What drives students’ AI learning behavior: A perspective of AI anxiety. Interactive Learning Environments, 32(6), 2584–2600. https://doi.org/10.1080/10494820.2022.2153147 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. https://doi.org/10.1080/10494820.2019.1674887 Wirtz, B. W., C., W. J., & and Geyer, C. (2019). Artificial intelligence and the public sector: Applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103 Yaşar, H., & Karagucuk, V. (2025). The effect of artificial intelligence anxiety on career decidedness among students in English-related departments at universities. Discover Artificial Intelligence, 5(1), 48. https://doi.org/10.1007/s44163-025-00284-y Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Oxford, UK: Center for the Governance of AI, Future of Humanity Institute, University of Oxford. https://doi.org/10.2139/ssrn.3312874 Zhao, H., & Seibert, S. E. (2006). The big five personality dimensions and entrepreneurial status: A meta-analytical review. Journal of Applied Psychology, 91(2), 259. https://doi.org/10.1037/0021-9010.91.2.259