CREWS -- Instructional Strategies and Methods for Pilot Training

New technological advancements in flight systems and unmanned or autonomous platforms is requiring pilots interpret, understand, and respond to new forms of data through sensor fusion or automated control inputs. It is critical to understand how pilots are responding to these new multi-domain environments and how they can create new mental models that allow them to respond more quickly and appropriately. Traditional Crew Resource Management (CRM) models have served well for instructing crew members in appropriate communications and interactions with their human counterparts. However, they have fallen short in instilling best response to automation or non-human crew. To improve pilots’ ability to synthesize data and respond to remote, autonomous, or non-human crew, TiER1 proposes developing a desktop trainer (CREWS) that will allow pilots to practice in high-context situations that is easily accessible and can be completed in an instructorless ecosystem or one that requires minimal intervention. To get there, TiER1 will seek to redefine traditional CRM models to expand crew definitions to include remote, autonomous, or non-human crew, allowing for better data analysis, decision making, and response. By expanding this definition, TiER1 can focus on the most influential tasks and behaviors that will lead to the greatest amount of change and allow pilots to create new mental models more quickly. By identifying the right set of tasks and behaviors, TiER1 can build new instructional methods that will allow pilots to practice and build their skills. The goal is to transfer these instructional methods to the CREWS platform that integrates data from autonomous sources, simulated remote crews, and context-driven mission profiles which allows pilots to repeatedly practice their new skills in a low-stakes environment.