Using AI and CTA to understand and develop training objectives for Future Vertical Lift

As technology including advanced sensors and the use of unmanned systems continues to grow, so too does the amount of data a pilot must work with. Head out the cockpit, head down looking at displays, managing multiple radios and sensors, making sense of data fusion, and in the near future with programs such as Future Vertical Lift (FVL) supervising multiple unmanned aircraft such as proposed Air Launched Effects (ALEs). Today’s warfighter is drowning in a sea of data and without future automation (and an understanding of when to hand off tasks to the system and vice-versa), artificial intelligence (AI), and enhanced visualizations tomorrow’s envisioned world pilots will be overloaded (in fact, our previous work shows how and where workload becomes excessive when comparing today’s rotorcraft pilots with envisioned world Future Attack Reconnaissance Aircraft (FARA) pilots (Scheff 2020).  The envisioned world of tomorrow will move from the traditional COIN battle and head towards more of a near-peer or peer-on-peer attack. As such, many of today’s platforms will not suffice. As platforms are updated and replaced, there will need to be improvements in training to support these new platforms, technologies, and new way of fighting. For this work, we are proposing relying on our work with FVL as well as technologies such as AI to identify user workload bottlenecks in future missions, know when to insert automation and AI, as well as develop training objectives to support the multi-domain environment including manned unmanned teaming (MUM-T) and the associated data fusion from these systems.