Flying the Unfriendly Skies
For the past two years, Learning Next initiatives out of the US Air Force (USAF) and US Army (USAR) graduated pilot candidates using experimental tools, techniques and technologies. This military-focused experimentation produced valuable data-backed insights and lessons learned in a wide variety of functional areas, to include data analysis, human performance and the use of immersive extended reality (XR) technology. A key hindrance in the experimental programs, however, is the lack of an integrated learning platform that provides a physics-based world in which to conduct all lessons, skills and activities in a single learning flow that can enable a Train Learn Reflect Train Again (TLRTA) methodology. This presentation proposes a new approach, incorporating instructional systems design and data analysis, to allow a student's successes and failures to affect their options later in the learning flow. This solution uses Commercial Off the Shelf (COTS) gaming technology to allow for rapid prototyping and demonstrates the ability to simulate the complete lifecycle of a mission. For example, a student pilot who failed to detect a hydraulic leak during the pre-flight inspection would need to manually lower and lock his landing gear prior to landing his next mission or address a more serious emergency. This approach allows students to train to specific learning objectives through consequences rather than multiple-choice assessments. More importantly, this approach allows detailed data collection for any action or decision the student makes, setting the stage for predictive analysis of a student's potential piloting skill as well as his strategic thinking ability. Finally, the approach adds retention concepts from popular commercial games such as quest based learning, leaderboards and achievements to self-motivate and reward students to log additional and high quality flight hours in the simulation. This presentation focuses on the technical challenges of creating an immersive, realistic training world that allows open-ended interaction from a variety of data analytics, biometric and learning science tools. These challenges include privacy, security, fidelity, authoritative data, extensibility, and high-value student performance tracking. This presentation also discusses the pros and cons of existing simulation engines, such as Prepar3D, XPlane and DCS, for this purpose, including the potential to incorporate live data feeds into a synthetic training environment.