A key potential advantage of modern training technologies relates to the ability to use them to automate aspects of training. Although in the early stages of adoption within the aviation industry, artificial intelligence (AI) tools and methods have many promises for training design, development, delivery, and assessment. We applied the use-case technology-mapping framework (UCTM) to identify and analyze how automation and AI technologies may be used within the flightcrew training design pipeline, integrating perspectives from relevant literature, informal discussions with stakeholders, and workshops with domain experts. Our preliminary findings highlight current/near-future applications of AI methods and tools in the training design pipeline. Here, we discuss ethical/legal, technical, and practical considerations for flightcrew training. We urge practitioners and researchers in the aviation human factors community to engage in this discussion and to conduct empirical research that will allow for a positive use of the technology across applications, including in aviation training.
Practical Takeaways/Applications.
• We present a wide range of potential use cases of AI in flightcrew training.
• We discuss the ethical/legal, technical, and practical implications of automation and AI.
• This information may inform future training processes and practices in the aviation domain.
BeierM. E. (2019). The impact of technology on workforce skill learning. Work Science Center Thinking Forward Report Series. Atlanta GA: Georgia Institute of Technology. http://hdl.handle.net/1853/61063
2.
BerrahalM.AziziM. (2020, October). Review of DL-based generation techniques of augmented images using portraits specification. In 2020 Fourth International Conference on Intelligent Computing in Data Sciences (pp. 1-8). IEEE. https://doi.org/10.1109/ICDS50568.2020.92668710
3.
BrownT. B.MannB.RyderN.SubbiahM.KaplanJ.DhariwalP.NeelakantanAShyamPm SastryG.AskellA.AgarwalA.Herbert- VossA.KruegerG.HenighanT.ChildR.RameshAZieglerDWuJ.WinterC.HesseC.ChenM.SiglerE.LitwinM.GrayS.ChessB.ClarkJ.BernerC.McCandlishA.RadfordA.SutskeverI.AmodeiD. (2020). Language models are few-shot learners. arXiv, 2005.14165. https://doi.org/10.48550/arXiv.2005.14165
4.
DaviesJ.BedwellJ.BendellR.BoloniL.DoocyL.FaulknerE.HarringtonM.HughesC.KiderJ.KueblerS.MarajC.McConnellD.MondesireS.NewtonO.SonnenfeldN.WelchG.WilliamsJ.FioreS. (2023, February23). Artificial intelligence and creativity. In FioreS. (Chair), Artificial Intelligence and Creativity [Symposium]. Eclectic Reading Group. Orlando, FL, United States: University of Central Florida, Institute for Simulation & Training
5.
DengLinY. (2023). The Benefits and Challenges of ChatGPT: An Overview. Frontiers in Computing and Intelligent Systems, 2(2), 81–83. https://doi.org/10.54097/fcis.v2i2.4465.
6.
GaoT.ZhangJ.MiQ. (2022, February). Procedural generation of game levels and maps: A review. In 2022 International Conference on Artificial Intelligence in Information and Communication (pp. 050-055). IEEE. https://doi.org/10.1109/ICAIIC54071.2022.9722624
7.
GhoseS.PrevostJ. J. (2020). Autofoley: Artificial synthesis of synchronized sound tracks for silent videos with deep learning. IEEE Transactions on Multimedia, 23, 1895-1907. https://doi.org/10.1109/TMM.2020.3005033
8.
GilsonA.SafranekC. W.HuangT.SocratesV.ChiL.TaylorR. A.ChartashD. (2023). How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education, 9(1), e45312. https://doi.org/10.2196/45312
HaqueM. U.DharmadasaI.SwornaZ. T.RajapakseR. N.AhmadH. (2022). "I think this is the most disruptive technology": Exploring sentiments of ChatGPT early adopters using Twitter data. ArXiv, 2212.05856 https://doi.org/10.48550/arXiv.2212.05856
13.
HendersonP.SinhaK.Angelard-GontierN.KeN. R.FriedG.LoweR.PineauJ. (2018, December). Ethical challenges in data-driven dialogue systems. Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 123-129). https://doi.org/10.1145/3278721.3278777
HuttunenK.KeränenH.VäyrynenE.PääkkönenR.LeinoT. (2011). Effect of cognitive load on speech prosody in aviation: Evidence from military simulator flights. Applied Ergonomics, 42(2), 348-357. https://doi.org/10.1016/j.apergo.2010.08.005
16.
LiuV.VermeulenJ.FitzmauriceG.MatejkaJ. (2022). 3DALL-E: integrating text-to-image AI in 3D design workflows. ArXiv, 2210.11603. https://doi.org/10.48550/arXiv.2210.11603
LundB. D.WangT. (2023). Chatting about ChatGPT: how may AI and GPT impact academia and libraries?Library Hi Tech Newshttps://dx.doi.org/10.2139/ssrn.4333415
19.
MacdonaldK.GermineL.AndersonA.ChristodoulouJ.McGrathL. M. (2017). Dispelling the myth: Training in education or neuroscience decreases but does not eliminate beliefs in neuromyths. Frontiers in Psychology, 8, 1314. https://doi.org/10.3389/fpsyg.2017.01314
20.
MinB.RossH.SulemE.VeysehA. P. B.NguyenT. H.SainzO.AgirreE.HeinzI.RothD. (2021). Recent advances in natural language processing via large pre-trained language models: A survey. ArXiv, 2111.01243. https://doi.org/10.48550/arXiv.2111.01243
21.
Moral-AndrésF.Merino-GómezE.ReviriegoP.LombardiF. (2022). Can artificial intelligence reconstruct ancient mosaics?ArXiv, 2210.06145. https://doi.org/10.48550/arXiv.2210.06145
22.
NewtonP. M.SalviA. (2020). How common is belief in the learning styles neuromyth, and does it matter? A pragmatic systematic review. Frontiers in Education, 5, 602451. https://doi.org/10.3389/feduc.2020.602451
23.
NicholsonD.SchatzS.StanneyK.LackeyS. (2009). Next-generation Expeditionary Warfare Intelligent Training (NEW-IT) Program Summary Booklet. Office of Naval Research. https://apps.dtic.mil/sti/pdfs/ADA514853.pdf
PaulS.YuanL.JainH. K.RobertL. P.JrSpohrerJ.Lifshitz-AssafH. (2022). Intelligence augmentation: Human factors in AI and future of work. AIS Transactions on Human-Computer Interaction, 14(3), 426-445. https://doi.org/10.17705/1thci.00174
26.
RahamanM.AhsanM. M.AnjumN.RahmanM.RahmanM. N. (2023). The AI race is on! Google's Bard and OpenAI's ChatGPT head-to- head: An opinion article. http://dx.doi.org/10.2139/ssrn.4351785
SalasE.WilsonK. A.BurkeC. S.BowersC. A. (2002). Myths about crew resource management training. Ergonomics in Design, 10(4), 20-24. https://doi.org/10.1177/106480460201000406
29.
SandersJ. P.VogelA. C.RogersJ. J. (2018, December). How can the center for navy security forces leverage immersive technologies to enhance its current training delivery?Naval Postgraduate School. Monterey, California, United States. https://apps.dtic.mil/sti/pdfs/AD1069726.pdf
30.
SchaffernakH.MoeslB.VorraberW.HolyM.HerzogE.-M.NovakR.KoglbauerI.V. (2022). Novel Mixed Reality Use Cases for Pilot Training. Education Sciences, 12, 345. https://doi.org/10.3390/educsci12050345
31.
ShenY.HeacockL.EliasJ.HentelK. D.ReigB.ShihG.MoyL. (2023). ChatGPT and other large language models are double-edged swords. Radiology, 230163. https://doi.org/10.1148/radiol.230163
32.
ShuteV.PsotkaJ. (1996). Intelligent tutoring system: Past, present, and future. In JonassenD. H. (Ed.), Handbook of Research for Educational Communications and Technology (1st ed., pp. 570-600). Simon & Shuster Macmillan: NY. https://myweb.fsu.edu/vshute/pdf/shute%201996_d.pdf
33.
SimkuteA.SuranaA.LugerE.EvansM.JonesR. (2022, October). XAI for learning: Narrowing down the digital divide between “new” and “old” experts. Adjunct Proceedings of the 2022 Nordic Human-Computer Interaction Conference (pp. 1-6). https://doi.org/10.1145/3547522.3547678
34.
SonnenfeldN. A.NguyenB.BoesserC. T.JentschF. (2021, May). Modern training practices for flightcrew training of procedural knowledge. International Symposium on Aviation Psychology, 21, 305-309. https://doi.org/10.5399/osu/1148
35.
ThomasonJ.NguyenH.V.LitmanD. (2013). Prosodic Entrainment and Tutoring Dialogue Success. In LaneH. C.YacefK.MostowJ.PavlikP. (Eds.), Artificial Intelligence in Education (Lecture Notes in Computer Science, vol.7926). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39112-5_104
36.
TuX.ZouY.ZhaoJ.AiW.DongJ.YaoY.WangZ.GuoG.LiZ.LiuW.FengJ. (2021). Image-to-video generation via 3D facial dynamics. IEEE Transactions on Circuits and Systems for Video Technology, 32(4), 1805-1819. https://doi.org/10.1109/TCSVT.2021.3083257
37.
Van den OordA.DielemanS.ZenH.SimonyanK.VinyalsO.GravesA.KalchbrennerN.SeniorA.KavukcuogluK. (2016). Wavenet: A generative model for raw audio. ArXiv, 1609.03499. https://doi.org/10.48550/arXiv.1609.03499
38.
VanLehnK. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221. https://doi.org/10.1080/00461520.2011.611369
39.
VerdolivaL. (2020). Media forensics and deepfakes: an overview. IEEE Journal of Selected Topics in Signal Processing, 14(5), 910-932.
40.
VorraberW.NeubacherD.MoeslB.BruggerJ.StadlmeierS.VoessnerS. (2019). Uctm—an ambidextrous service innovation framework—a bottom-up approach to combine human-and technology- centered service design. Systems, 7(2), 23. https://doi.org/10.3390/systems7020023
WidderD. G.NafusD.DabbishL.HerbslebJ. (2022, June). Limits and possibilities for “ethical AI” in open source: A study of deepfakes. In 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 2035-2046). https://doi.org/10.1145/3531146.3533779
43.
YiuC. Y.NgK. K. H.LeeC.-H.ChowC. T.ChanT. C.LiK. C.WongK. Y. (2021). A digital twin-based platform towards intelligent automation with virtual counterparts of flight and air traffic control operations. Applied Sciences, 11(22), 10923. MDPI AG. http://dx.doi.org/10.3390/app112210923
44.
Zawacki-RichterO.MarínV. I.BondM.GouverneurF. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?International Journal of Educational Technology in Higher Education, 16(1), 1-27. https://doi.org/10.1186/s41239-019-0171-0
