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AI-driven assistants for education and research? A case study on ChatGPT for air transport management

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  • Wandelt, Sebastian
  • Sun, Xiaoqian
  • Zhang, Anming

Abstract

Artificial Intelligence is in the process to transform various parts of the aviation industry, from the reduction of delays and increasing fuel efficiency to better demand prediction models. The latest kid on the block is ChatGPT, a large language model developed by OpenAI, which has made into the news for its mind-blowing ability to create textual content in any structured language. Doing so, ChatGPT has the potential to revolutionize the way we communicate with computers, and it could have a lasting impact on aviation education and research. In this study, we investigate the potential of this impact and, the extent to which it has already materialized, based on a set of graduate student surveys and experiments with ChatGPT. The results of our surveys indicate the interest of students in efficient learning, time saving, and improvement in programming/writing skills. Our experiments on terminology explanation, state-of-the-art identification of selected research tasks as well as programming design, highlight the tradeoffs between benefits and potential risks inherent to the usage of ChatGPT and AI-driven assistants in general. Overall, we believe that our study makes a first contribution to evaluating an exciting new technology which has the potential to revolutionize our aviation system.

Suggested Citation

  • Wandelt, Sebastian & Sun, Xiaoqian & Zhang, Anming, 2023. "AI-driven assistants for education and research? A case study on ChatGPT for air transport management," Journal of Air Transport Management, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:jaitra:v:113:y:2023:i:c:s0969699723001266
    DOI: 10.1016/j.jairtraman.2023.102483
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    1. Rodríguez-Sanz, à lvaro & Fernández de Marcos, Alberto & Pérez-Castán, Javier A. & Comendador, Fernando Gómez & Arnaldo Valdés, Rosa & París Loreiro, à ngel, 2021. "Queue behavioural patterns for passengers at airport terminals: A machine learning approach," Journal of Air Transport Management, Elsevier, vol. 90(C).
    2. Aleksander Aristovnik & Damijana Keržič & Dejan Ravšelj & Nina Tomaževič & Lan Umek, 2020. "Impacts of the COVID-19 Pandemic on Life of Higher Education Students: A Global Perspective," Sustainability, MDPI, vol. 12(20), pages 1-34, October.
    3. Jardines, Aniel & Soler, Manuel & García-Heras, Javier, 2021. "Estimating entry counts and ATFM regulations during adverse weather conditions using machine learning," Journal of Air Transport Management, Elsevier, vol. 95(C).
    4. Abdelghany, Ahmed & Guzhva, Vitaly S. & Abdelghany, Khaled, 2023. "The limitation of machine-learning based models in predicting airline flight block time," Journal of Air Transport Management, Elsevier, vol. 107(C).
    5. Rajendran, Suchithra & Srinivas, Sharan & Grimshaw, Trenton, 2021. "Predicting demand for air taxi urban aviation services using machine learning algorithms," Journal of Air Transport Management, Elsevier, vol. 92(C).
    6. Wandelt, Sebastian & Shi, Xing & Sun, Xiaoqian, 2021. "Estimation and improvement of transportation network robustness by exploiting communities," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    7. Truong, Dothang, 2021. "Using causal machine learning for predicting the risk of flight delays in air transportation," Journal of Air Transport Management, Elsevier, vol. 91(C).
    8. Ilic, Aleksandar & Urosevic, Dragan & Brimberg, Jack & Mladenovic, Nenad, 2010. "A general variable neighborhood search for solving the uncapacitated single allocation p-hub median problem," European Journal of Operational Research, Elsevier, vol. 206(2), pages 289-300, October.
    9. Hong, Seock-Jin & Lee, Kang-Seok & Seol, Eun-Suk & Young, Seth, 2016. "Safety perceptions of training pilots based on training institution and experience," Journal of Air Transport Management, Elsevier, vol. 55(C), pages 213-221.
    10. Geursen, Izaak L. & Santos, Bruno F. & Yorke-Smith, Neil, 2023. "Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning," Journal of Air Transport Management, Elsevier, vol. 109(C).
    11. Yiu, Cho Yin & Ng, Kam K.H. & Yu, Simon C.M. & Yu, Chun Wah, 2022. "Sustaining aviation workforce after the pandemic: Evidence from Hong Kong aviation students toward skills, specialised training, and career prospects through a mixed-method approach," Transport Policy, Elsevier, vol. 128(C), pages 179-192.
    12. Miani, Peter & Kille, Tarryn & Lee, Seung-Yong & Zhang, Yahua & Bates, Paul Raymond, 2021. "The impact of the COVID-19 pandemic on current tertiary aviation education and future careers: Students’ perspective," Journal of Air Transport Management, Elsevier, vol. 94(C).
    13. Sun, Xiaoqian & Wandelt, Sebastian & Zhang, Anming, 2021. "Technological and educational challenges towards pandemic-resilient aviation," Transport Policy, Elsevier, vol. 114(C), pages 104-115.
    14. Diana, Tony, 2022. "Using sentiment analysis to reinforce learning: The case of airport community engagement," Journal of Air Transport Management, Elsevier, vol. 102(C).
    15. Lucini, Filipe R. & Tonetto, Leandro M. & Fogliatto, Flavio S. & Anzanello, Michel J., 2020. "Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews," Journal of Air Transport Management, Elsevier, vol. 83(C).
    16. Peksatici, Özge & Ergun, Hande Sinem, 2019. "The gap between academy and industry - A qualitative study in Turkish aviation context," Journal of Air Transport Management, Elsevier, vol. 79(C), pages 1-1.
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