Adaptive AI Based - Aero Propulsion Aircrew and Maintenance Training System (AAMT) ?

There are several challenges facing the Air Force Global Strike Command (AFGSC) concerning the proficiency of maintenance technicians, in the 3, 5, and 7, levels. One of the most critical things to notice is the lack of proficiency in the removal and replacement of specialized engine components. The lack of availability of training enginesĀĀ severely limits the technician's ability to practice. Airmen are essentially training ōon the jobö when the aircraft is out of service for maintenance and while components need to be replaced. However, the maintenance time doubles when training is incorporated into the time required for aircraft maintenance. As a result, aircraft are unable to meet sortie generation requirements while airman are training. Maintenance turnaround proficiency is disrupted and therefore becomes ineffective as maintenance crews race to meet allocated installation time in accordance with technical orders. The time required to upgrade skill level suffers significantly, as does the opportunity for airmen to gain proficiency. Further, even fewer aircraft can be maintained and launched to meet sortie rates. This is a vicious cycle that up until now seemed impossible to break.ĀĀ BetaFlix developed its Learning Ecosystem ¢ BetaFlix Integrated Training System (BITS) based on the Advanced Distributed Learning (a DoD initiative) Total Learning Architecture (TLA) requirements. BITS TLA includes an adaptive and personalized system that validates competency of learners based on the assessment predefined and will be accessible to airmen through requested Platform.Ā BetaFlix will leverage BITS to build the Adaptive Aero Propulsion Aircrew and Maintenance Training System (AAMT). These new tools extend and expand the learning experience by adding initial and continuous assessment tools, synergizing these assessments with 3D animations, VR simulations, and interactive eBooks for more efficient learning. BetaFlix will develop a Machine Learning (ML) algorithm-based assessment and evaluation platform using RedShred's (Knowledge Graph database and Ingestor) patented Natural Language Processing (NLP), Computer Vision Algorithms (CVAs) to ingest technical data and CobaltÆs (Conversational AI(CAI)) deep learning algorithms including Natural Language Understanding (NLU) engine, Automated Speech Recognition (ASR) engine. The incorporation of initial and continuous assessment tools will modernize, personalize, allow for equivocal learning, and provide for adaptation while improving the learning experience for a new generation of airmen.Ā