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This report documents findings and recommendations from a RAND Project AIR FORCE study that helped the U.S. Department of the Air Force (DAF) identify promising advanced training-technology investments and identified steps that the DAF can take to track and integrate the most-promising technologies. The authors provide an overview of the advanced training-technology landscape using a quantitative modeling approach based on hundreds of thousands of documents that reflect training-technology research and development (R&D) within the DAF, the U.S. Department of Defense (DoD), commercial industry, academia, and by allies and adversaries.

The authors then describe stakeholder perspectives on advanced-training technologies in use as well as developmental priorities. This is followed by an online expert panel's analysis in which training-technology experts rated and discussed the maturity, benefits, scope of applicability, and adoption barriers for a subset of training technologies. The report concludes by providing recommendations for how the DAF can target its investments into specific technologies and for how it can improve the training-technology enterprise more broadly.

Key Findings

  • Prominent technologies applied to training vary by organization. Virtual reality (VR), augmented reality (AR), synthetic environments, and physiological sensors and wearables feature prominently across military and nonmilitary training domains. DoD focuses more on AR and sensors and wearables, while non-DoD sources focus on gaming, learning analytics, synthetic agents, and natural language interfaces (NLIs). The Navy focuses on adaptive training and performance assessment, the DAF on VR, and the Army on haptics, gaming, and sensors and wearables. Among U.S. allies, the United Kingdom is developing a variety of military training technologies. Among U.S. adversaries, Russia is engaged in relatively little R&D, while China has significantly increased its activities, especially in VR.
  • Methods are available to overcome issues with tracking training technologies. Stakeholders voiced difficulty with tracking technology developments within and outside DoD. Where they do exist, data sources that catalog smaller efforts are of variable quality and are often not maintained. A combination of human annotation, large language modeling, and open-source models can be used to efficiently scale the process of identifying technologies and the contexts in which they are employed.
  • There is some convergence on promising technologies and high-value use cases. Stakeholders and panel experts converge on adaptive training, AR, VR, and performance assessment as mature technologies that hold promise for DAF training. Other technologies are assessed as maturing in the near term but have less obvious benefits, including haptics, physiological sensors and wearables, and synthetic agents. Many promising use cases involve multiple technologies.


  • The DAF should track the following technologies and organizations: (1) adaptive training (with the Office of Naval Research (ONR) and the Army Research Lab (ARL) leading in research and funding); (2) performance assessment (tracking the Navy's performance-assessment investments); (3) gaming technologies (the Army and Intelligence Advanced Research Projects Activity are key players in gaming research); (4) NLIs and LLMs (ONR and ARL are conducting research in this area).
  • The DAF has strong investments in VR and synthetic environments and should focus on applications that are validated in the scientific literature or within the military training community for improved learning outcomes. The DAF should leverage synthetic environments for dynamic, complex training.
  • The DAF should make coordinated investments in technologies that serve as enablers across multiple classes of training technologies, such as performance assessment, adaptive training, and content tagging.
  • The DAF should assign an Office of Primary Responsibility (OPR) for tracking training-technology efforts. The OPR would be responsible for putting in place a governance structure to collect data on training-technology development efforts, identifying other promising data sources to inform similar efforts, and making this information available across the DAF. Representatives from the user community may be best positioned for this role.
  • Small-scale technology efforts should be logged in a database with a clear description of the effort to make the entry amenable to language modeling methods.
  • The DAF should adopt and maintain the database and underlying method described in this report to enable stakeholders to track and explore training technologies.

Research conducted by

The research reported here was commissioned by Maj Gen Daniel DeVoe (AF/A5/7) and conducted within the Workforce, Development, and Health Program of RAND Project AIR FORCE.

This report is part of the RAND research report series. RAND reports present research findings and objective analysis that address the challenges facing the public and private sectors. All RAND reports undergo rigorous peer review to ensure high standards for research quality and objectivity.

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