Early Identification of Unmanned Aircraft Pilots Using Measures of Personality and Aptitude

Abstract

This study evaluated the extent to which personality measures (Self-Description Inventory+) could improve prediction of remotely piloted aircraft (RPA) training outcomes, beyond currently used cognitive, psychomotor, and aviation knowledge and experience predictors included in the U.S. Air Force (USAF) Pilot Candidate Selection Method 2.0. Participants were 170 USAF officers with RPA Initial Flight Screening (RFS) outcomes and 110 USAF officers with RPA Instrument Qualification (RIQ) outcomes. Both RFS and RIQ emphasize development of skills traditionally required for manned aircraft pilots, also considered essential for USAF RPA pilots. Results showed significant negative relationships between the Big Five personality trait of Openness and several RPA training outcomes, and significant incremental variance explained by Openness beyond other predictors. Additionally, RFS and RIQ training outcomes were generally predicted by measures traditionally used for selection into manned aircraft pilot training courses and careers. Together, these findings suggest that personality, specifically Openness scores, and scores on traditional pilot selection measures, can serve as useful indicators in the early identification of RPA pilot talent.