The Department of the Air Force (DAF) is tasked with delivering high-quality warfighter training to develop and sustain warfighter mission-critical skills. To help communities across the DAF avoid common pitfalls in their move toward adopting personalized training methods, the authors present techniques for successfully implementing and using a computational cognitive model for adaptive training in the applied setting of language learning.

Lessons Learned from Integrating a Computational Cognitive Model for Personalized Linguist Training

Mark Toukan, Jair Aguirre, Sean Mann, Eddie Ro

ResearchPublished Aug 12, 2024

The Department of the Air Force (DAF) is tasked with delivering high-quality warfighter training to develop and sustain warfighter mission-critical knowledge, skills, and abilities to maintain technological and human capital advantages over adversaries. It is both challenging and costly to provide the breadth of training required to develop competencies across a wide variety of occupations in a manner that targets the needs of individual warfighters.

Adaptive training methods have the potential to provide a scalable and economical means to deliver effective training. To help communities across the DAF avoid common pitfalls in their move toward adopting these personalized training methods, the authors present techniques for successfully implementing and using a computational cognitive model for adaptive training in the applied setting of language learning.

Key Findings

  • Adaptive training utilization requires objective and quantifiable performance metrics, explicitly defined competencies, and clearly defined policies so that relevant data can be collected and leveraged over time and across individual careers.
  • Training planners must consider data collection requirements when making investments in learning management systems. This will aid in the organizational ability to adequately collect, integrate, and share data to create a continuum of learning across careers.
  • Training planners must thoughtfully consider integration requirements when incorporating adaptive technologies into existing training processes. Simulation methods can help ensure that content is delivered to trainees in a manner that optimizes alignment with current learning objectives and goals.
  • Adaptive learning technology developers and stakeholders need to continually engage one another to align training content with learning objectives. It is critical to acquire buy-in and trust from users of the intended population and to bolster utilization and adoption of adaptive learning technologies through education regarding how the technology will function when it is effectively used.
  • Emerging large language model capabilities hold promise for automating data validation and assessments, for providing more-efficient training experiences, and for increasing customization of learning experiences. These capabilities possess the ability to parse learning activities, determine and extract the underlying competencies being trained, help overcome data quality deficiencies, and efficiently generate content tailored to the unique needs of individual learners and within the constraints of existing curricula.

Recommendations

  • Clearly define competencies and policies for collecting data on those competencies across a a person's career in the DAF. These elements should be established collectively among the DAF training and personnel management communities: The training community would likely collect the data, and the personnel management community would likely be responsible for managing the data across a person's career.
  • Account for integration and data collection requirements across training and learning platforms when making technology investments. High-level guidance can help drive requirements that make integration and data sharing easier when communities move to adopt adaptive training methods. Integrators and those responsible for collecting and managing the data should agree on these requirements. The requirements should cover what data to collect, how often to collect them, and how to protect them.
  • Avoid disruption of existing training processes when integrating adaptive technologies. Simulation methods can be used to ensure that those involved in the integration effort deliver content to trainees in a manner that does not disrupt ongoing learning goals while providing the necessary data to aid the integration of adaptive content delivery. This requires close and continual collaboration between instructors and trainers and the integrators.
  • Continual collaboration with stakeholders across the student community, instructors, infrastructure (i.e., database) maintainers, and application developers is important to define what the available resources are and to design the integration with minimal disruptions to student learning objectives based on the complexity of the learning workflows, the existing student-facing technology, and the expected frequency and types of interactions.

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Document Details

  • Copyright: RAND Corporation
  • Availability: Available
  • Year: 2024
  • Print Format: Paperback
  • Paperback Pages: 36
  • Paperback Price: $25.00
  • Paperback ISBN/EAN: 1-9774-1388-9
  • DOI: https://doi.org/10.7249/RRA2454-1
  • Document Number: RR-A2454-1

Citation

RAND Style Manual
Toukan, Mark, Jair Aguirre, Sean Mann, and Eddie Ro, Lessons Learned from Integrating a Computational Cognitive Model for Personalized Linguist Training, RAND Corporation, RR-A2454-1, 2024. As of September 11, 2024: https://www.rand.org/pubs/research_reports/RRA2454-1.html
Chicago Manual of Style
Toukan, Mark, Jair Aguirre, Sean Mann, and Eddie Ro, Lessons Learned from Integrating a Computational Cognitive Model for Personalized Linguist Training. Santa Monica, CA: RAND Corporation, 2024. https://www.rand.org/pubs/research_reports/RRA2454-1.html. Also available in print form.
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This research was prepared for the Department of the Air Force and conducted within the Workforce, Development, and Health Program of RAND Project AIR FORCE.

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