Havacılık sektöründe yapay zekâ: Pilot deneyimlerine yönelik nitel bir araştırma

Abstract

Araştırmanın amacı, havacılık sektöründe yapay zekâ uygulamalarının pilotların gözünden durumlarının analiz edilmesidir. Araştırmada nitel araştırma yöntemlerinden mülakat tekniği kullanılmıştır. Mülakat soruları literatürden destek alarak hazırlanmış ve çalışmaya uyarlanmıştır. Araştırmada veri toplama aracı olarak yarı yapılandırılmış görüşme formu kullanılmıştır. Araştırmaya kolay ulaşılabilir örneklem yöntemi ile 28 pilot dahil edilmiştir. Araştırmada cinsiyet, yaş, eğitim, havacılık sektöründeki rol, havacılık sektöründe deneyim olmak üzere beş tane demografik soru ve araştırmaya katılan pilotlara yöneltilen 11 mülakat sorusu bulunmaktadır. Elde edilen veriler MAXQDA 2022 nitel analiz programından destek alınarak analiz edilmiştir. Veriler programa aktarılmış ve içerik analiz yöntemi ile tema ve kodlar oluşturulmuştur. Örüntüler ve kod frekansları üzerinden içerik analizi yapılmıştır. Elde edilen bulgulara göre; araştırmaya katılan pilotların çoğunluğu yapay zekâyı faydalı ve kolaylık sağlayıcı bir teknoloji olarak görmekle birlikte, dikkat edilmesi gerektiği, kontrollü kullanılması gerektiği, karar destek için önemli olduğu, geleceğin belirleyicisi olduğunu düşünülmektedirler. Pilotların çoğunluğu yapay zekânın havacılık sektöründe kullanılması gerektiğini düşünmekle birlikte bazıları belirli alanlarda kullanılması gerektiğini düşünmektedirler. Pilotların çoğunluğunun yapay zekâya uyum sağlamada zorluk çekmeyeceklerini düşündükleri görülmektedir. Pilotlar iş yükünü azaltma, bilgiye ulaşma, planlama, maliyet, zaman tasarrufu, insan hatalarını azaltma, emniyet, hızlı ve doğru karar verme, destek, yakıt tasarrufu, doğru ve hızlı uçuş ve uçak planlaması gibi faydalarının olduğunu, yetenekleri köreltmesi ve rehavet, istihdam ve işgücünü azaltması, yapay zekânın kontrol edilememesi, her şeyi yapay zekâdan bekleme gibi olumsuz etkilerinin olabileceğini düşünmektedirler.

Keywords

• Havacılık Sektörü
• Yapay Zeka
• pilotlar

Artificial intelligence in the aviation industry: A qualitative research on pilot experiences

Abstract

The aim of this research is to analyze the status of artificial intelligence applications in the aviation sector from the perspective of pilots. The research utilized interview technique, a qualitative research method. Interview questions were prepared based on the literature and adapted for the study. A semi-structured interview form was used as the data collection tool. 28 pilots were included in the study using a convenient sampling method. The research included five demographic questions (gender, age, education, role in the aviation sector, and experience in the aviation sector) and 11 interview questions posed to the participating pilots. The data were analyzed using the MAXQDA 2022 qualitative analysis program. The data were transferred to the program, and themes and codes were created using content analysis. Content analysis was performed through patterns and code frequencies. According to the findings, while most participating pilots view artificial intelligence as a useful and convenient technology, they also believe it requires caution, should be used in a controlled manner, is important for decision support, and is a determinant of the future. While most pilots believe that artificial intelligence should be used in the aviation sector, some believe it should be used in specific areas. It appears that most pilots believe they will not have difficulty adapting to artificial intelligence. Pilots believe that it offers benefits such as reduced workload, access to information, planning, cost and time savings, reduced human error, increased safety, faster and more accurate decision-making, support, fuel savings, accurate and rapid flight and aircraft planning. However, they also believe that it can have negative effects such as dulling skills and leading to complacency, reduced employment and workforce, the inability to control artificial intelligence, and expecting everything from AI.

Keywords

• Aviation Industry
• Artificial Intelligence
• Pilots

References

1. Addepto. (2025). Top 10 practical applications of AI in the aviation industry. https://addepto.com/blog/top-10-practical-applications-and-use-cases-of-ai-in-the-aviation-industry
2. Akpınar, M. T. (2023). Generative artificial intelligence for air transportation sector. 21. yüzyılda mühendislikte çağdaş araştırma uygulamaları üzerine disiplinler arası çalışmalar II içinde. https://doi.org/10.58830/ozgur.pub95.c443
3. Aksoy, C., & Dursun, Ö. O. (2018). A general overview of the development of the civil aviation sector in Turkey. Elektronik Sosyal Bilimler Dergisi, 17(67), 1060–1076. https://doi.org/10.17755/esosder.343877
4. Amit, J. (2024). The future of AI in aviation 2024: A flight path to success. Aiola. https://aiola.ai/blog/future-of-ai-in-aviation/
5. Assured. (2025). AI in the aviation industry. https://zekâ.assured-systems.com/ai-in-the-aviation-industry/
6. Bertaux, D. (1981). From the life-history approach to the transformation of sociological practice. D. Bertaux (Ed.), Biography and society: The life history approach in the social sciences içinde. SAGE Publications.
7. Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. SAGE Publications.
8. Chaudhari, T. (2024). Artificial intelligence, its types and application in various fields. International Journal of Commerce and Management Research, 10(6), 49–51.

9. Çakı, N. (2023). The aviation sector from a work psychological perspective: A bibliometric analysis of graduate-level theses written in Türkiye. Journal of Aviation, 7(1), 147–155. https://doi.org/10.30518/jav.1240522
10. Demir, G., Moslem, S., & Duleba, S. (2024). Artificial intelligence in aviation safety: A systematic review and bibliometric analysis. International Journal of Computational Intelligence Systems, 17, Makale 279, 1–30.
11. EASA. (2025). Artificial intelligence and aviation. https://zekâ.easa.europa.eu/en/light/topics/artificial-intelligence-and-aviation-0
12. Ergen, M. (2019). What is artificial intelligence? Technical considerations and future perception. Anatolian Journal of Cardiology, 22, 5–7. https://doi.org/10.14744/AnatolJCardiol.2019.79091
13. Fang, Z. (2024). The impact of AI on the aviation industry: An industry view of opportunities and challenges for a sustainable future. Engineering Proceedings, 80(1), 1–4. https://doi.org/10.3390/engproc2024080002
14. FlySight. (2024). Exploring the use of artificial intelligence in aviation. https://zekâ.flysight.it/exploring-the-use-of-artificial-intelligence-in-aviation/
15. Funk, P., Sohlberg, R., Sundelius, N., Olsson, E., & Bovet-Emanuel, P. (2025). How artificial intelligence (AI) is transforming the aviation industry. Proceedings of the Swedish Aerospace Conference içinde. Stockholm, Sweden. https://doi.org/10.3384/wcc215.1201
16. Henneberry, D., Ziakkas, D., & Doerrstein, F. (2025). The role of artificial intelligence (AI) applications in aviation risk management. Human Interaction and Emerging Technologies, 161, 12–20. https://doi.org/10.54941/ahfe1005892
17. ICAO. (2025). The impact of artificial intelligence on the aviation sector (s. 1–5). International Civil Aviation Organization.
18. Janic, M. (2000). An assessment of risk and safety in civil aviation. Journal of Air Transport Management, 6(1), 43–50. https://doi.org/10.1016/S0969-6997(99)00021-6
19. Kabashkin, I., Misnevs, B., & Zervina, O. (2023). Artificial intelligence in aviation: New professionals for new technologies. Applied Sciences, 13(21), Makale 11660. https://doi.org/10.3390/app132111660
20. Kárász, I., & Edit, K. C. (2021). The characteristics of the aviation industry: Airbus SE versus Boeing Company.
21. Kashyap, R. (2019). Artificial intelligence systems in aviation. Cases on modern computer systems in aviation içinde (s. 1–26). IGI Global.
22. Kulida, E., & Lebedev, V. (2020). About the use of artificial intelligence methods in aviation. Proceedings of the 13th International Conference on Management of Large-Scale System Development (MLSD) içinde (s. 1–5). Moscow, Russia.
23. Lartey, S. (2024). Exploring the future developments in the aviation industry: Dangers, challenges, and future recommendations (Yüksek Lisans Tezi). Teesside University.
24. Leman, M. (1997). Lecture notes in artificial intelligence.
25. Lopes, N. M., Aparicio, M., & Neves, F. T. (2025). Challenges and prospects of artificial intelligence in aviation: A bibliometric study. Data Science and Management, 8(2), 207–223. https://doi.org/10.1016/j.dsm.2024.11.001
26. McCann, K. (2025). How AI is transforming the airline industry. AI Magazine.
27. Miller, M., Holley, S., & Halawi, L. (2023). The evolution of AI on the commercial flight deck: Finding balance between efficiency and safety while maintaining the integrity of operator trust. Artificial Intelligence, Social Computing and Wearable Technologies, 113, 1–10.
28. Palwe, S. (2025). Aviation analytics market. Market Research Future.
29. Pierrat, E., Rupcic, L., Hauschild, M. Z., et al. (2021). Global environmental mapping of the aeronautics manufacturing sector. Journal of Cleaner Production, 297, Makale 126603. https://doi.org/10.1016/j.jclepro.2021.126603
30. Robert, A. D., & Boullaguet, A. (1997). Analyse de contenu. Presses Universitaires de France.
31. Saini, N. (2023). Research paper on artificial intelligence and its applications. IJRTI Research Journal, 8(4), 356–360. https://doi.org/10.6084/m9.doione.IJRTI2304061
32. Saroop, P. (2025). Artificial intelligence in aviation: Enhancing efficiency, safety, and sustainability. International Journal of Advance Research, Ideas and Innovations in Technology, 11(4), 208–214.
33. Schwindt, S., & Sullivan, S. (2024). Considering security of artificial intelligence and machine learning essential in aviation. AIA Civil Aviation Cybersecurity Subcommittee, 1–11.
34. Sharma, T. (2024). AI collaboration in aircraft cockpit systems. IOSR Journal of Computer Engineering, 26(5), 7–21. https://doi.org/10.9790/0661-2605050721
35. Singh, A. (2019). The concept of artificial intelligence. JETIR Research Journal, 6(3), 566–570.
36. Şahin, Y. (2024). Havacılık ve yapay zekâ alanındaki çalışmaların bibliyometrik analizi. International Journal of Social and Humanities Sciences Research, 11(110), 1637–1648. https://doi.org/10.5281/zenodo.13732160
37. TAV. (2020). Artificial intelligence in aviation industry. https://tavtechnologies.aero/en-EN/review/pages/artificial-intelligence-in-aviation-industry
38. Umpirowicz, S. (2025). The rise of artificial intelligence in aviation: Transforming the skies. Symphony Solutions.
39. Yang, C., & Huang, C. (2023). Natural language processing (NLP) in aviation safety: Systematic review of research and outlook into the future. Aerospace, 10, Makale 600. https://doi.org/10.3390/aerospace10070600
40. Yetkin, S., Abuhanieh, S., & Yigit, S. (2024). Investigation on the abilities of different artificial intelligence methods to predict the aerodynamic coefficients. Expert Systems with Applications, 237, Makale 121324. https://doi.org/10.1016/j.eswa.2023.121324
41. Youseftorkaman, C. M., Kangaranifarahani, C. A., & Dana, D. (2023). Applications of artificial intelligence in the aviation industry and air accidents. Mathematical Statistics and Engineering Applications, 72(2), 178–192.
42. Zaoui, A., Tchuente, D., & Wamba, S. F. (2024). Impact of artificial intelligence on aeronautics: An industry-wide review. Journal of Engineering and Technology Management, 71, Makale 101800. https://doi.org/10.1016/j.jengtecman.2024.101800