This PhD project aims to investigate the effective delivery of training in various shifted sleep patterns on a simulation device providing consistent automatic coaching, assessment and post event feedback via the use of AI.
A significant proportion of training in simulation and live flight occurs in normal working/daylight hours when trainee biorhythms and attentions cycles are in tune with normal circadian rhythms. Yet most mission operation rehearsal and practice would likely occur outside that normal training and wakefulness cycle. This leads to the potential shortfall in performance in that training is completed at peak awareness and practice during suboptimal awareness. A risk mitigation strategy could be to follow the mantra of ‘train like you fight’ and complete some key training in out of hours conditions. To attempt this change it would be an advantage to shift peak awareness into likely mission operational hours, by altering sleep and training patterns to align operational needs. Such a proposal would require an understanding of how much you can shift the effective training window out of normal hours into likely mission hours and what effect on training efficacy results from this shift of sleep and awareness patterns.
The proposed project would investigate the effective delivery of training in various shifted sleep patterns on a simulation device capable of providing consistent automatic coaching, assessment and post event feedback via the use of AI. This analysis would be supported by the collection of trainee biometrics and cognitive load measures to assess the effect on the trainee and for task performance.
Desired background
Masters in Aviation, Human Factors and related disciplines
School of Science
Aviation | Behavioural Science