Development of an Autodiagnostic Adaptive Precision Trainer for Decision Making (ADAPT-DM)

Abstract : The Autodiagnostic Adaptive Precision Trainer for Decision Making (ADAPT-DM) is a framework for adaptive training of decision making skills. The training challenge is that decision making behavior is mostly unobservable with traditional behavioral measures, which generally only give access to outcome performance. This article describes the ADAPT-DM framework, which utilizes physiological sensors, specifically electroencephalography and eye tracking, to detect indicators of implicit cognitive processing relevant to decision making and accomplish the granularity required to pinpoint and process level issues. Using these advanced measures, the trainee's performance on these cognitive processes can be assessed in real time and used to drive smart adaptations that individualize training. As a proof of concept, the ADAPT-DM framework was conceptually applied to the contact evaluation task in submarine navigation. Simulated data from 75 students, grouped into three levels of expertise (novice, intermediate, and expert), were used for principal component analysis to identify skill dimensions that reflect proficiency levels. Then ADAPT-DM's composite diagnosis was performed, which uses an expertise model that integrates automated expert modeling for automated student evaluation machine learning models with eye tracking and electroencephalography data to assess which proficiency level the simulated students actions were most similar to. Based on additional assessments, the diagnostic engine is able to determine whether the student (a) performs to criterion, in which case training could be accelerated, (b) is in an optimal learning state, or (c) is in a nonoptimal learning state for which remediation or mitigation are needed. Using root cause analysis techniques, the ADAPT-DM process level measures then allow instructors to pinpoint where in the decision making process breakdowns occur, so that optimal training adaptations can be implemented.

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