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Performance gains from adaptive eXtended Reality training fueled by artificial intelligence

Author

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

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.

Suggested Citation

  • Kay M Stanney & JoAnn Archer & Anna Skinner & Charis Horner & Claire Hughes & Nicholas P Brawand & Eric Martin & Stacey Sanchez & Larry Moralez & Cali M Fidopiastis & Ray S Perez, 2022. "Performance gains from adaptive eXtended Reality training fueled by artificial intelligence," The Journal of Defense Modeling and Simulation, , vol. 19(2), pages 195-218, April.
    • Handle: RePEc:sae:joudef:v:19:y:2022:i:2:p:195-218
    DOI: 10.1177/15485129211064809
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    References listed on IDEAS

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    1. Robert M. Bernard & Eugene Borokhovski & Richard F. Schmid & David I. Waddington & David I. Pickup, 2019. "Twenty‐first century adaptive teaching and individualized learning operationalized as specific blends of student‐centered instructional events: A systematic review and meta‐analysis," Campbell Systematic Reviews, John Wiley & Sons, vol. 15(1-2), June.
    2. Kimiko Ryokai & Alice Agogino, 2013. "Off the Paved Paths: Exploring Nature with a Mobile Augmented Reality Learning Tool," International Journal of Mobile Human Computer Interaction (IJMHCI), IGI Global, vol. 5(2), pages 21-49, April.
    3. Angel-Urdinola,Diego & Castillo Castro,Catalina & Hoyos,Angela, 2021. "Meta-Analysis Assessing the Effects of Virtual Reality Training on Student Learning and Skills Development," Policy Research Working Paper Series 9587, The World Bank.
    4. Nykan Mirchi & Vincent Bissonnette & Recai Yilmaz & Nicole Ledwos & Alexander Winkler-Schwartz & Rolando F Del Maestro, 2020. "The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.
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