Predictive validity of pilot selection instruments for remotely piloted aircraft training outcome

Area of Science:

• Aviation Psychology
• Military Science
• Human Factors Engineering

Background:

• Demand for remotely-piloted aircraft (RPA) pilots has surged, necessitating specialized training.
• Traditional pilot cross-training is unsustainable, leading to the establishment of the Undergraduate RPA Training (URT) program.
• URT selection criteria mirror manned pilot training, including aptitude tests like the Air Force Officer Qualifying Test (AFOQT) and Pilot Candidate Selection Method (PCSM).

Purpose of the Study:

• To evaluate the predictive validity of AFOQT pilot and PCSM scores for URT completion.
• To determine if current selection methods can be enhanced to improve training efficiency.

Main Methods:

• The study analyzed AFOQT and PCSM scores of 139 URT students.
• Training success was defined as URT pass/fail, with a 74.8% pass rate.

Main Results:

• Both AFOQT pilot and PCSM composites showed significant predictive validity for URT success.
• The PCSM composite (r = 0.480) demonstrated slightly stronger predictive power than the AFOQT pilot composite (r = 0.378).

Conclusions:

• Current selection methods, including AFOQT pilot scores, are effective in predicting URT completion.
• Establishing a minimum PCSM score of 25 could have screened out an additional 12 students, increasing the pass rate to 80.2%.
• The Air Force is exploring supplementary measures to further optimize RPA pilot selection based on job analyses.