Artificial Intelligence Anxiety and Perceived Future Employability Among Aviation Students

Year 2025, Volume: 12 Issue: 2, 118 - 132

Arif Tuncal , Seda Çeken

https://doi.org/10.18394/iid.1594301

Abstract

The aim of the study was to examine the effect of artificial intelligence anxiety and its sub-dimensions on perceived future employability among aviation students. Data were collected through an online survey with 909 participants. The artificial intelligence anxiety scale and the perceived future employability scale were employed as data collection instruments. The multiple linear regression analysis revealed the limited explanatory power of the artificial intelligence anxiety sub-dimensions on perceived future employability (R² = .024, adj. R² = .020). Among the sub-dimensions, artificial intelligence socio-technical blindness positively influenced PFE (β = .130, p = .009), while artificial intelligence learning had a negative impact (β = -.142, p = .001). No significant effects were found for artificial intelligence job replacement and artificial intelligence configuration. Difference tests showed significant variances among the demographic groups. The results demonstrated that the impact of artificial intelligence anxiety on perceived future employability is typically insufficient to be considered significant, reflecting the technical and human-focused structure of the aviation sector.

Keywords

Aviation , Employment , Aviation Training , Artificial Intelligence , Artificial Intelligence Anxiety

Ethical Statement

All procedures performed in studies involving human participants conform to the ethical standards of the institutional and/or national research committee and the 1964 Helsinki declaration and its subsequent amendments or comparable ethical standards. The study was approved for ethical suitability by the Ethics Committee of the International Science and Technology University with decision number: E-4651465861-204.01.07-250 and date 30 JUL 2024 (Revision: E-4651465861-204.01.07-474/ 01 NOV 2024).

Supporting Institution

This study has not received support from any organization such as government, commercial or non-profit organizations.

Havacılık Öğrencilerinin Yapay Zekâ Kaygıları ve Gelecekteki İstihdam Edilebilirlik Algıları

Abstract

Bu çalışmanın amacı havacılık ile ilgili bölümlerde öğrenim gören üniversite öğrencilerinde yapay zekâya ilişkin kaygı ve alt boyutlarının, algılanan gelecekteki istihdam edilebilirlik üzerindeki etkisini incelemektir. Araştırma verileri çevrimiçi anket aracılığıyla 909 katılımcıdan toplanmıştır. Veri toplama aracı olarak yapay zekâ kaygı ölçeği ve algılanan gelecekteki istihdam edilebilirlik ölçeği kullanılmıştır. Çoklu doğrusal regresyon analizi yapay zekâ kaygısı alt boyutlarının algılanan gelecekteki istihdam edilebilirlik üzerindeki açıklayıcı gücünün sınırlı olduğunu göstermiştir (R² = .024, adj. R² = .020). Yapay zekâ sosyoteknik körlük boyutu algılanan gelecekteki istihdam edilebilirlik üzerinde pozitif (β = .130, p = .009), öğrenme kaygısı ise negatif (β = -.142, p = .001) etkiler göstermiştir. Yapay zekâ iş değiştirme ve yapılandırma boyutlarının etkisi anlamlı bulunmamıştır. Demografik değişkenlere dayalı testler bazı gruplar arasında anlamlı farklılıklar ortaya koymuştur. Bulgular yapay zekâ kaygısının algılanan gelecekteki istihdam edilebilirlik üzerindeki etkisinin anlamlı olmadığı, havacılık sektörünün teknik ve insan odaklı yapısıyla ilişkili olduğu sonucunu desteklemiştir.

Keywords

Havacılık , İstihdam , Havacılık Eğitimi , Yapay Zekâ , Yapay Zekâ Kaygısı

References

• Abay, E. G. (2024). Yapay zekanın küresel ekonomi ve istihdamı kökten değiştirmesi bekleniyor. Anadolu Ajansı. 23.01.2024. Retrieved from https://www.aa.com.tr/tr/bilim-teknoloji/yapay-zekanin-kuresel-ekonomi-ve-istihdami-kokten-degistirmesi-bekleniyor/3116087
• Abubakar, M., EriOluwa, O., Teyei, M. & Al-Turjman, F. (2022). AI application in the aviation sector. In 2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs), 26-28 October, Nicosia.
• Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation, and work (NBER Working Paper No. 24196). National Bureau of Economic Research. Retrieved from https://www.nber.org/system/files/working_papers/w24196/w24196.pdf
• Acemoglu, D., & Restrepo, P. (2017). Robots and jobs: evidence from US labor markets. Journal of Political Economy, 128, 2188 - 2244.
• Aghion, P., Antonin, C. & Bunel, S. (2019). Artificial intelligence, growth and employment: The role of policy, Economie & Statistique, 510-511-512, 149-164.
• 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.
• Alkın, S., Korkmaz, O. & Çelik, S. B. (2020). Algılanan gelecekteki istihdam edilebilirlik ölçeğinin Türkçeye uyarlanması. İş ve İnsan Dergisi, 7(1), 33-47.
• Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries (OECD Social, Employment and Migration Working Papers, No. 189). OECD Publishing. Retrieved from https://wecglobal.org/uploads/2019/07/2016_OECD_Risk-Automation-Jobs.pdf
• Badhurunnisa, M. & Dass, V. (2023). Challenges and opportunities involved in implementing AI in workplace. International Journal for Multidisciplinary Research, 5(6), 1-7.
• Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
• Belchik, T.A. (2022). Artificial intelligence as a factor in labor productivity. In: Bogoviz, A.V., Suglobov, A.E., Maloletko, A.N., Kaurova, O.V. (eds) Сooperation and Sustainable Development. Lecture Notes in Networks and Systems, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-030-77000-6_62
• Bendík, D. & Novák, A. (2022). Artificial intelligence and its use in air transport. Práce a Štúdie, 11, 106-113. https://doi.org/10.26552/pas.z.2022.1.18
• Betz, N. (2000). Self-Efficacy theory as a basis for career assessment. Journal of Career Assessment, 8, 205 - 222.
• Bordot, F. (2022). Artificial intelligence, robots and unemployment: Evidence from OECD countries. Journal of Innovation Economics & Management, (1), 117-138.
• Bower, M. (2019). Technology‐mediated learning theory. British Journal of Educational Technology, 50(3), 1035-1048.
• Creed, P. A. & Klisch, J. (2005). Future outlook and financial strain. Journal of Occupational Health Psychology, 10, 251–260.
• Creed, P. A., Sawitri, D. R., Hood, M. & Hu, S. (2021). Career goal setting and goal pursuit in young adults: The role of financial distress. Journal of Career Development, 48(6), 801-816.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
• Duran, C., Boz, D., Behdioğlu, S. & Kutlu, S. (2019). Yetenek yönetimi uygulamaları ölçeği geçerlilik ve güvenilirlik çalışması. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 20(2), 158-189.
• Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.
• European Commission & Directorate-General for Communications Networks, Content and Technology (2017). Attitudes towards the impact of digitization and automation on daily life: Report. European Commission. Retrieved from https://op.europa.eu/en/publication-detail/-/publication/ce5d5948-6778-11e7-b2f2-01aa75ed71a1/language-en
• European Union Aviation Safety Agency (EASA). (2023). Artificial intelligence roadmap- A human-centric approach to AI in aviation. Retrieved from https://www.easa.europa.eu/en/domains/research-innovation/ai
• Frey, C. B. & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerization?. Technological Forecasting and Social Change, 114, 254-280.
• George, D. & Mallery, M. (2010). SPSS for Windows step by step: A simple guide and reference (10th Ed.). Boston: Pearson.
• Georgieff, A. & Milanez, A. (2021). What happened to jobs at high risk of automation?. (OECD Social, Employment and Migration Working Papers, No. 225). OECD Publishing. Retrieved from https://www.oecd.org/content/dam/oecd/en/publications/reports/2021/01/what-happened-to-jobs-at-high-risk-of-automation_ffdb138f/10bc97f4-en.pdf
• Gillath, O., Ai, T., Branicky, M. S., Keshmiri, S., Davison, R. B. & Spaulding, R. (2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, 106607.
• Gillespie, N., Lockey, S. & Curtis, C. (2021). Trust in artificial intelligence: A five country study. The University of Queensland and KPMG Australia, Research Report. Retrieved from https://doi.org/10.14264/e34bfa3
• Global Labour Resilience Index (GLRI). (2025, January 23). Global Labour Resilience Index 2025: The transformative impact of AI on economies & labour markets. Whiteshield & Google @ Davos 2025. Retrieved from https://whiteshield.ai/insights/resilience-of-jobs/whiteshield-google-davos-2025
• Gorbachev, O., Shestakov, V. & Stefański, K. (2019). Assessment of professionally important qualities aviation technical staff. In AIP Conference Proceedings (Vol. 2077, No. 1). AIP Publishing.
• Guilbert, L., Bernaud, J. L., Gouvernet, B. & Rossier, J. (2016). Employability: review and research prospects. International Journal for Educational and Vocational Guidance, 16, 69-89.
• Gunawan, W., Creed, P. A. & Glendon, A. I. (2019). Development and initial validation of a perceived future employability scale for young adults. Journal of Career Assessment, 27(4), 610-627.
• Hair Jr, J. F., LDS Gabriel, M., Silva, D. D. & Braga, S. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects. RAUSP Management Journal, 54(4), 490-507.
• Hirschi, A., Herrmann, A., & Keller, A. (2015). Career adaptivity, adaptability, and adapting: A conceptual and empirical investigation. Journal of Vocational Behavior, 87, 1-10.
• Jackson, D. & Wilton, N. (2016). Developing career management competencies among undergraduates and the role of work-integrated learning. Teaching in Higher Education, 21(3), 266-286.
• Jung, J., & Lim, D. (2020). Industrial robots, employment growth, and labor cost: A simultaneous equation analysis. Technological Forecasting and Social Change, 159, 120202. https://doi.org/10.1016/j.techfore.2020.120202.
• Karasar, N. (2016). Bilimsel araştırma yöntemi: bilimsel irade algı çerçevesi ile kavramlar - ilkeler – teknikler. Ankara: Nobel Akademik Yayıncılık.
• Kılanç, B. (2024). Meslek-istihdam atlası (Lisans)- TÜİK 2024 yükseköğretim istihdam istatistikleri. Retrieved from https://drive.google.com/file/d/1cYC6o7-lNd4TFo79US3oE_Wzc1QFmOfI/view
• Kong, H., Jiang, X., Zhou, X., Baum, T., Li, J., & Yu, J. (2024). Influence of artificial intelligence (AI) perception on career resilience and informal learning. Tourism Review, 79(1), 219-233.
• Kumar, P., Hussain, M., Reddy, K. & Deep, S. (2022). Methods of air traffic management using artificial intelligence in India. International Journal of Scientific Research in Science and Technology, 9(4), 560-569. https://doi.org/10.32628/ijsrst229490
• Lapidus, L. (2023). Using artificial intelligence in employment decisions. Risk Management, 70(2), 4-6.
• Li, J. & Huang, J. S. (2020). Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory. Technology in Society, 63, 101410.
• Liu, N. & Jew, L. (2023). The impact of social capital on career adaptability in the era of artificial intelligence: The mediating role of career choice. Journal of Logistics, Informatics and Service Science, 10, 166-179.
• Lord, R. G., Diefendorff, J. M., Schmidt, A. M. & Hall, R. J. (2010). Self-regulation at work. Annual Review of Psychology, 61, 543–568.
• Lukyanenko, R., Maass, W. & Storey, V. C. (2022). Trust in artificial intelligence: From a foundational trust framework to emerging research opportunities. Electronic Markets, 32(4), 1993-2020.
• Makarius, E. E., Mukherjee, D., Fox, J. D. & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organisation. Journal of Business Research, 120, 262-273.
• Mcclure, P. (2018). “You’re fired,” says the Robot. Social Science Computer Review, 36, 139 - 156. https://doi.org/10.1177/0894439317698637.
• McKinsey & Company. (2020). İşimizin geleceği: Dijital çağda Türkiye’nin yetenek dönüşümü. Retrieved from https://www.mckinsey.com/tr/our-insights/future-of-work-turkey
• Miramontes, A., Tesoro, A., Trujillo, Y., Barraza, E., Keeler, J., Boudreau, A., ... & Vu, K. P. L. (2015). Training student air traffic controllers to trust automation. Procedia Manufacturing, 3, 3005-3010.
• Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D. & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, 39-68.
• Neudert, L. M., Knuutila, A. & Howard, P. N. (2020). Globalattitudes towards AI, machine learning & automated decision-making: Implications for involving artificial intelligence in public service and good governance. Oxford Internet Institute. Retrieved from https://oxcaigg.oii.ox.ac.uk/wp-content/uploads/sites/11/2020/10/GlobalAttitudesTowardsAIMachineLearning2020.pdf
• Özbek, A. (2024). Muhasebe meslek mensuplarının yapay zekâ kaygılarının gelecekte istihdam edilebilirlik algıları üzerine bir çalışma. Alanya Akademik Bakış, 8(1), 254-267.
• 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.
• Qenani, E., MacDougall, N., & Sexton, C. (2014). An empirical study of self-perceived employability: Improving the prospects for student employment success in an uncertain environment. Active Learning in Higher Education, 15(3), 199-213.
• Sanusi, I. T., Olaleye, S. A., Oyelere, S. S. & Dixon, R. A. (2022). Investigating learners’ competencies for artificial intelligence education in an African K-12 setting. Computers and Education Open, 3, 100083.
• Savickas, M. L. (2013). Career construction theory and practice. In S. D. Brown, & R. W. Lent, Career development and counseling: putting theory and research to work (2nd ed., pp. 147-183). John Wiley & Sons.
• Schepman, A. & Rodway, P. (2020). Initial validation of the general attitudes towards artificial intelligence scale. Computers in Human Behavior Reports, 1, 100014.
• Sheıkhı, M. (2022). Yapay zeka kullanımının iş piyasasına etkisi. Journal of Economics and Political Sciences, 2(1), 102-111.
• 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.
• Tecuci, G. (2012). Artificial intelligence. Wiley Interdisciplinary Reviews: Computational Statistics, 4(2), 168-180. https://doi.org/10.1002/wics.200
• Tymon, A. (2013). The student perspective on employability. Studies in Higher Education, 38(6), 841-856.
• Vroom, V.H. (1964). Work and motivation. New York, NY: Wiley.
• Wang, Y. Y. & Wang, Y. S. (2019). Development and validation of an artificial intelligence anxiety scale: an initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
• Webb, M. (2019). The impact of artificial intelligence on the labor market. Available at SSRN 3482150.
• Wickens, C. D., & Dehais, F. (2020). Expertise in aviation. In P. Ward, J. M. Schraagen, J. Gore, & E. Roth (Eds.), The Oxford handbook of expertise (pp. 662–689). Oxford University Press.
• Xie, M., Ding, L., Xia, Y., Guo, J., Pan, J. & Wang, H. (2021). Does artificial intelligence affect the pattern of skill demand? Evidence from Chinese manufacturing firms. Economic Modelling, 96, 295-309.
• Yılmaz, H. U., & Yımaz, A. (2024). Dijital çağın potansiyel çalışanlarının yapay zekâ kaygılarının belirlenmesi. Business and Economics Research Journal, 15(2), 171-188.
• Yizhong, X., Lin, Z., Baranchenko, Y., Lau, C. K., Yukhanaev, A. & Lu, H. (2017). Employability and job search behavior: A six-wave longitudinal study of Chinese university graduates. Employee Relations, 39, 223–239.
• Zhang, B., & Dafoe, A. (2019). Artificial intelligence: American attitudes and trends. Available at SSRN 3312874. https://dx.doi.org/10.2139/ssrn.3312874
• Zhang, X. (2023). Research on the impact of artificial intelligence on the labor market. Advances in Economics and Management Research, 8(1), 252-252.
• Zhou, G., Chu, G., Li, L. & Meng, L. (2019). The effect of artificial intelligence on China’s labor market. China Economic Journal, 13(1), 24–41.
• Zou, T., & Sun, K. (2021). Application and prospect of artificial intelligence in aircraft design. In 2021 International Conference on Networking Systems of AI (INSAI) (pp. 201-205). IEEE.