How can algorithms, machine learning, and artificial intelligence make a more meaningful Unmanned Aerial System (UAS) training experience? Data and analytics of UAS pilots’ individual performance, whole populations, or subsets of populations- like specific industry, can contribute to a more streamlined training experience. A training provider can preemptively offer courses, should a deficiency exist, or proficiency advance learners based on experiences or that of similar UAS pilot profiles. The advancements of algorithms, machine learning, and artificial intelligence are genuine contributors to the future of UAS training.
The analysis of data gathered on students for the purposes of creating more efficient and more effective training allows a training provider to identify problems and address them much earlier in the training process. This can provide the necessary time to undo negative learning or fix poor technique and allow ample time to apply new learning under the direction of an instructor. It can be a much more positive experience for the student with better learning outcomes. Results from student-specific data analytics are added to a student’s profile. This would allow the student to begin a training event that is adapted to his/her unique challenges and capabilities. The student’s data and resulting analytics are then continually tracked going forward, developing a more robust profile of the student’s learning style, trends, and proficiencies.
When an instructor is monitoring more than one student, and when a student decides to take a shortcut, or forego procedures, or physically does not have visibility of the causal factor for the poor performance, it may or may not be caught by an instructor.