Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Yang, S; Yu, K; Lammers, T; Chen, F

Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators

Yang, S

Yu, K

Lammers, T

Chen, F

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: HCI International 2021 - Posters, 2021, 1420, pp. 581-587
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Yang, S https://orcid.org/0000-0002-6772-6805
dc.contributor.author	Yu, K https://orcid.org/0000-0001-5138-6749
dc.contributor.author	Lammers, T https://orcid.org/0000-0003-1000-2395
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date	2021-07-24
dc.date.accessioned	2021-11-08T22:35:36Z
dc.date.available	2021-11-08T22:35:36Z
dc.date.issued	2021-01-01
dc.identifier.citation	HCI International 2021 - Posters, 2021, 1420, pp. 581-587
dc.identifier.isbn	9783030786410
dc.identifier.issn	1865-0929
dc.identifier.issn	1865-0937
dc.identifier.uri	http://hdl.handle.net/10453/151409
dc.description.abstract	The aviation industry was set to see unprecedented growth over the next two decades. Key occupations predicted to be in shortage included not only pilots, but also flight instructors. Undoubtedly, Covid-19 is currently having a huge impact on the industry. Nevertheless, the current environment further strengthens the need for pilots to maintain their training. Consequently, there is pressure to deliver high-quality training outcomes for an increasing number of pilots and trainees with limited resources available. Current simulator-based training schemes are limited by placing a significant reliance on the personal experience of flight instructors to assess pilot performance. Finding ways to increase the quality and efficiency of simulator-based training is therefore of high importance. With recent advances in artificial intelligence, it is possible to use machine learning techniques to extract latent patterns from massive datasets, to analyze pilot trainees’ activities, and to provide feedback on their performance by processing hundreds of different parameters available on flight simulators. An ML-aided pilot training and education framework is needed that exploits the power of the ML techniques for more objective performance evaluation. In this paper, we describe a conceptual framework for such a system and outline steps toward the development of a full digital instructor system with the potential to overcome current limitations and enabling comprehensive and meaningful feedback that is tailored to the individual need of the trainee.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	HCI International 2021 - Posters
dc.relation.ispartof	International Conference on Human-Computer Interaction
dc.relation.ispartofseries	Communications in Computer and Information Science
dc.relation.isbasedon	10.1007/978-3-030-78642-7_78
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-78642-7_78
dc.title	Artificial Intelligence in Pilot Training and Education – Towards a Machine Learning Aided Instructor Assistant for Flight Simulators
dc.type	Conference Proceeding
utslib.citation.volume	1420
utslib.location.activity	Virtual Event
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-11-08T22:35:34Z
pubs.finish-date	2021-07-29
pubs.place-of-publication	Cham, Switzerland
pubs.publication-status	Published
pubs.start-date	2021-07-24
pubs.volume	1420
dc.location	Cham, Switzerland

Abstract:

The aviation industry was set to see unprecedented growth over the next two decades. Key occupations predicted to be in shortage included not only pilots, but also flight instructors. Undoubtedly, Covid-19 is currently having a huge impact on the industry. Nevertheless, the current environment further strengthens the need for pilots to maintain their training. Consequently, there is pressure to deliver high-quality training outcomes for an increasing number of pilots and trainees with limited resources available. Current simulator-based training schemes are limited by placing a significant reliance on the personal experience of flight instructors to assess pilot performance. Finding ways to increase the quality and efficiency of simulator-based training is therefore of high importance. With recent advances in artificial intelligence, it is possible to use machine learning techniques to extract latent patterns from massive datasets, to analyze pilot trainees’ activities, and to provide feedback on their performance by processing hundreds of different parameters available on flight simulators. An ML-aided pilot training and education framework is needed that exploits the power of the ML techniques for more objective performance evaluation. In this paper, we describe a conceptual framework for such a system and outline steps toward the development of a full digital instructor system with the potential to overcome current limitations and enabling comprehensive and meaningful feedback that is tailored to the individual need of the trainee.

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http://hdl.handle.net/10453/151409