Performance gains from adaptive eXtended Reality training fueled by artificial intelligence

Kay M Stanney, JoAnn Archer, Anna Skinner, Charis Horner, Claire Hughes, Nicholas P Brawand, Eric Martin, Stacey Sanchez, Larry Moralez, Cali M Fidopiastis and Ray S Perez

The Journal of Defense Modeling and Simulation, 2022, vol. 19, issue 2, 195-218

Abstract: While virtual, augmented, and mixed reality technologies are being used for military medical training and beyond, these component technologies are oftentimes utilized in isolation. eXtended Reality (XR) combines these immersive form factors to support a continuum of virtual training capabilities to include full immersion, augmented overlays that provide multimodal cues to personalize instruction, and physical models to support embodiment and practice of psychomotor skills. When combined, XR technologies provide a multi-faceted training paradigm in which the whole is greater than the sum of the constituent capabilities in isolation. When XR applications are adaptive, and thus vary operational stressors, complexity, learner assistance, and fidelity as a function of trainee proficiency, substantial gains in training efficacy are expected. This paper describes a continuum of XR technologies and how they can be coupled with numerous adaptation strategies and supportive artificial intelligence (AI) techniques to realize personalized, competency-based training solutions that accelerate time to proficiency. Application of this training continuum is demonstrated through a Tactical Combat Casualty Care training use case. Such AI-enabled XR training solutions have the potential to support the military in meeting their growing training demands across military domains and applications, and to provide the right training at the right time.

Keywords: Adaptive training; eXtended reality; virtual reality; augmented reality; mixed reality; artificial intelligence; Tactical Combat Casualty Care; Combat Lifesaver (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:19:y:2022:i:2:p:195-218

DOI: 10.1177/15485129211064809

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