Document Type
Article
Publication Date
2025
Abstract
The project uses a simulator-based screening approach aimed at screening forpilot potential in the Republic of Singapore Air Force (RSAF). Against thebackdrop of a shrinking talent pool and lean resources, the simulator-based pilotscreening (SBPS) aims to leverage simulators, data science and other emergingtechnology to enhance effectiveness and optimize efficiency for pilot screening inthe RSAF. SBPS uses simulator-based screeners to assess candidates over tendays, through four standardized simulated training/mission sorties. Primaryassessments include standardized task performance and behavior-basedobservations by RSAF Qualified Flying Instructors (QFIs) and AviationPsychologists (AvPsychs), as well as objective mission and task performanceparameters measured by the simulator. Psychophysiological measures (PPMs),eye trackers, electroencephalograms (EEG), electrocardiograms (ECG), videobasedemotion coding, as well as simulator data were explored as means toaugment task and behavior-based assessments.
Repository Citation
Koh, N. Z.,
Joshua, L. W.,
Qiang, A. L.,
Mark, D. C.,
Wee, A. C.,
& Frederick, T. L.
(2025). Simulator-Based, Machine Learning-Modelled, Psychophysiological Measurement-Augmented Pilot Screening in the Republic of Singapore Air Force. Proceedings of the 23rd International Symposium on Aviation Psychology, 239-244.
https://corescholar.libraries.wright.edu/isap_2025/41

Comments
Presented at the 23rd International Symposium on Aviation Psychology, May 27-30, 2025, Hosted by Oregon State University