Andrea M Abler

The Department of the Air Force wants to invest in advanced training technologies--such as augmented and virtual reality, gaming, and synthetic training environments--to maintain an asymmetric advantage over competitors. In this report, the authors construct a framework for training technology transfer and lay out potential barriers and enablers associated with each step. They also examine six use cases from across the U. S. Department of Defense.

This report identifies promising advanced training-technology investments and steps that the U. S. Department of the Air Force can take to track and integrate them. The authors assess the technology landscape using a quantitative modeling approach, stakeholder perspectives on these technologies and developmental priorities, and an expert panel's analysis of the maturity, benefits, applicability, and adoption barriers for a subset of technologies.

The Federal Emergency Management Agency's (FEMA's) Qualification System (FQS) is a credentialing process that focuses on standardizing personnel abilities through training and applied experience. FEMA's Integration Branch asked the authors of this report to identify existing gaps and areas for improvement and examine ways to measure and monitor the effectiveness of FQS in the future.

The authors examined the friction between the U. S. Army's People First objectives (which focus on command climate, cohesive teams, career goals, and work-life balance) and Regionally Aligned Readiness and Modernization Model objectives (which focus on mission readiness) and developed strategies to mitigate this friction.

Amid increasing concerns about the current readiness assessment system, the U. S. Air Force is evolving its training infrastructure to adapt for operations in contested and denied environments, an increased pace of warfare, and the potential loss of superiority across multiple domains. The authors explore how addressing readiness assessment gaps can help inform related decisionmaking.

The U. S. Department of the Air Force (DAF) must develop and sustain mission-critical knowledge, skills, and abilities (KSAs) among airmen, guardians, and civilians. Yet training and education is costly. This report describes how computational cognitive models can deliver personalized and adaptive training that enhanced learning gains and reduced training time for mission-critical KSAs, with emphasis on second-language learning, for the DAF.