Enhancing performance criteria to improve U.S. military aviation training

The United States military is struggling with a pilot and pilot instructor shortage. The time and cost to transition entry-level pilot candidates to expert pilots is extensive. The U.S. military is in critical need of accelerating the learning process to transition novice pilots to experts, with a cost-effective solution. Several research studies are underway to solve this problem but lack the ability to assess the effectiveness of training when reducing training cycles with inexpensive technology solutions.

This dissertation focuses on the performance measures that are required to evaluate levels of fidelity, or the “realism” of simulation-based training devices. The proposed measures include quantifiable task performance scores and classifications of cognitive workload, or attention. It discusses using these same measures to develop an adaptive learning solution to personalize training for each individual. Although the limited subjects used in the study do not provide enough evidence to support an optimal adaptation scheme, measures of task performance and cognitive workload prove to be essential for assessment. This testbed provides immediate, statistical feedback to the instructor during training scenarios for lesson adaptation, resulting in reduced training cycles, and human instructor support.

A review of evidence-based research provides motivation for implementing these performance criteria measures into U.S. military aviation training. These measures will ensure the implementation of effective training strategies that reduce the time to train and minimalize cost, producing an outcome of expert pilots who are ready for battle.