# Project 621123 — Learning and Operational Readiness

**Program element:** 0602202F — Human Effectiveness Applied Research  
**Project:** 621123  
**Component:** U.S. Air Force  
**Appropriation:** 3600 — RDT&E, Air Force  
**Budget Activity:** 2 — Applied Research  
**Vintage:** President's Budget PB2027  
**Canonical URL:** https://hitchintel.com/programs/0602202F/621123  
**Parent:** https://hitchintel.com/programs/0602202F

## Summary

Project 621123 — Learning and Operational Readiness requests $15.4M in FY2027, 15% of the $103.2M requested for program element 0602202F, down 54% on FY2026. 4 R-2A activities decompose the request.

## Funding profile

| Fiscal Year | Estimate Type | Amount ($M) |
|---|---|---|
| FY2025 | Actual | 13.3 |
| FY2026 | Enacted | 33.2 |
| FY2027 | Request | 15.4 |
| FY2028 | Outyear | 15.9 |
| FY2029 | Outyear | 22.7 |
| FY2030 | Outyear | 25.3 |
| FY2031 | Outyear | 26.7 |

> Estimate types are not summed. This project is one leaf of PE 0602202F; the PE total is the sum of its projects, never added to them.

## What project 621123 buys

This project conducts research focused on enhancing warfighter readiness through the development of an advanced, extensible, multi-domain test and training ecosystem, implementing advanced modeling and simulation architectures and data management structures to support at-scale JAD operational training exercises. Two major thrust areas are: 1) Learning and Operational Training, focused on the development of theoretically-grounded training tools and technologies to improve knowledge and skill acquisition and retention, employing models that describe how humans learn and perform, and exploiting the synergy of joint Human-AI teams for real-time learning during operations, and 2) Digital Models of Cognition, focused on the development of scalable, high-fidelity models of human perception and cognition to predict human decision-making under operational stressors; these human information processing models are integrated into realistic mission simulation environments. Work in this project supports missions across the USAF and USSF, including pilot training in Air Education and Training, Air Combat, and Air Mobility Commands, prediction, assessment, and mitigation of cognitive workload in complex, max endurance operations, and broad support for battle management, information warfare, agile combat operations, and wargaming.

## Activities (R-2A) — 4

| Activity | FY2025 | FY2026 | FY2027 | Move | Page |
|---|---|---|---|---|---|
| Learning and Operational Training | 3.7 | 11.7 | 9.2 | −21% | — |
| Digital Models of Cognition | 2.1 | 9.5 | 6.2 | −35% | — |
| Personalized Learning | 4.7 | 0.0 | 0.0 | — | — |
| Cognitive Modeling | 2.8 | 0.0 | 0.0 | — | — |

> Activities carry the prior, current and budget year only — no five-year plan. In the request year they partition this project exactly; in earlier years they can under-cover it.

### Learning and Operational Training

**FY2027 planned work.** -Continue research on data tools and competency models by validating existing and novel pilot training models, addressing training requirements for crewed and uncrewed collaborative platforms. -Continue research on just-in-time training by analyzing primary and secondary Airmen/Guardian needs and incorporating the findings into a tutoring capability for multi-disciplinary teams. -Continue to advance the integrated human-AI learning ecosystem by validating machine-readable knowledge and intelligent tutors, applying hybrid cognitive-AI models to piloting domains across the entire training pipeline, from undergraduate to squadron-level communities. -Continue development of metrics for human-machine co-learning and complete the exploration of adaptive team training paradigms by delivering a final report and refining a simulation framework for testing Airman-AI interactions at diverse levels of autonomy. -Continue the exploration of AI-enabled learning by completing the development of an adaptive tutor and expanding the generative AI tools to be generalizable across DAF training domains. -Continue integrating team proficiency assessment in synthetic environments by developing and implementing new visual and audio measurement technologies and refining methods for accessing live training data. -Complete the identification and validation of algorithms for shared team representations by compiling data on shared mental models into a final report that will inform future Human-AI teaming efforts. -Complete the exploration of infrastructure and fidelity requirements by delivering a final report that identifies the most critical needs for data-driven, personalized instruction. -Complete the exploration of skill transfer methods by compiling all findings from the research into a final report. -Complete research on the impact of training fidelity by delivering a final report that identifies the modalities and technologies that best support training, particularly in fighter domains.

**FY2026 to FY2027 change.** FY 2027 decreased compared to FY 2026 by $2.471 million, aligning with a reduced emphasis on learning needs as several multi-year research efforts reach their planned conclusion. Final reports will be completed on critical infrastructure requirements and the impact of training fidelity (e.g., XR, VR) in fighter domains.

**FY2026 plans — current year.** -Continue exploration and assessment of infrastructure and fidelity requirements to support a peer fight, developing a requirements process for competency-based training environments to support data-driven performance assessment, readiness management, and instruction. -Continue research on applying learning theories for just-in-time training to accelerate acquisition of skill and knowledge to produce multi-capable Airmen/Guardians. -Continue research on human-AI interactive learning and co-learning mechanisms in laboratory settings. This includes generating machine-readable representations of warfighter knowledge for Air Battle Management and advancing simulation architectures to test AI interactions at diverse levels of autonomy. -Continue research evaluating the impact of training fidelity from augmented, virtual, mixed, and extended reality on readiness by validating the optimal manipulation of training elements for specific mission competencies. -Continue research integrating team proficiency assessment into synthetic training ecosystems by refining performance metrics and the synthetic environment itself. -Commence research to define data tools, analytics, and competency models for digital engineering, with an initial design and validation for undergraduate pilot training. -Commence exploration of adaptive team training paradigms, developing new methods to evaluate Human-Human and Human-AI team co-learning and interactions in advanced, mission-specific simulation architectures and building on metrics from in-house tools such as the Team Dynamics Measurement System. -Commence the exploration of AI-enabled, continuous learning methods using generative AI to maximize skill acquisition and develop course-of-action selection support.

### Digital Models of Cognition

**FY2027 planned work.** -Continue the computational cognitive modeling for NGAD and CCA efforts by finalizing data analysis and comparing human performance data against ideal performer models for managing multiple collaborative assets. -Continue research on descriptive and anticipatory analytics by developing methods to combine publicly available information and intelligence products to identify opportunities for influence operations. -Continue development of the sensemaking tool suite by creating an information environment simulation to support DAF training courses and operational exercises. -Continue experimentation on the mechanisms of influence by collecting data to investigate factors affecting the will to fight and identifying evidence-based levers of influence vulnerable to adversarial attack. -Continue research on social dynamics models by developing culturally informed, networked AI agents for the simulation and forecasting of the behavioral effects of influence maneuvers. -Complete experimentation on performance-modulating factors with a study on a MC-130J training mission, delivering a final report to inform customer requirements and internal modeling efforts. -Complete research on computational performance frameworks by delivering an implemented framework for integrating human models and a corresponding user guide to AFRL stakeholders. -Complete research on computational modeling for situational understanding via natural language. The resulting models of communication will be integrated into efforts for resilient communication analysis and will inform interface development for CCA. -Complete research on autonomy-based dynamic task allocation by preparing a final report that indicates study findings and identifies critical knowledge gaps for future work.

**FY2026 to FY2027 change.** FY 2027 decreased compared to FY 2026 by 3.286 million reflecting a reduced emphasis on digital modeling. This change aligns with the planned conclusion of major research efforts, including the transition of communication models to the Collaborative Combat Aircraft (CCA) program.

**FY2026 plans — current year.** -Continue computational cognitive modeling to enable quantitative understanding of mission effectiveness, generating data for pilots controlling crewed and uncrewed Collaborative Combat Aircraft (CCA) within the Next Generation Air Dominance (NGAD) family of systems. -Continue experimentation on digital models that account for internal and external factors that modulate cognitive performance, including fatigue, task load, and extended noise exposure, utilizing real-world measurements from F-35 and C-130 interiors. -Continue research to define descriptive and anticipatory analytics from network and content data to inform decision-making in cognitive warfare. This includes developing a playbook for the Pacific theater and analytics to categorize visual social media content. -Continue research on computational and mathematical frameworks representing human performance across multiple scales and levels of resolution. This includes developing new models to test the fidelity required to recreate Airman decision-making under operational stressors, such as during long-duration flights. -Continue computational modeling for situational understanding through natural language interaction, conducting a study pairing Airmen with a computational model to assess communication modes. -Continue research demonstrating autonomy-based dynamic task allocation driven by operator workload, using newly developed physio-cognitive models measuring operator task saturation. -Commence development of a suite of tools for sensemaking of the information environment, including refining a forecasting tool and conducting user analysis interviews. -Commence experimentation and modeling to examine the underlying psychological mechanisms of influence and resilience, including studies on the impact of multi-modal deepfakes on perception. -Commence research to support the development of digital models of social dynamics, tailoring models of the continued influence effect for individuals and population models in a mission simulator.

### Personalized Learning

**FY2027 planned work.** Not Applicable

**FY2026 to FY2027 change.** Not Applicable

**FY2026 plans — current year.** In FY 2026, the activities and associated funding under the "Personalized Learning" effort transferred to the "Learning and Operational Training" effort to better integrate readiness into learning research.

### Cognitive Modeling

**FY2027 planned work.** Not Applicable

**FY2026 to FY2027 change.** Not Applicable

**FY2026 plans — current year.** In FY 2026, the activities and associated funding under the "Cognitive Modeling" effort transferred to the "Digital Models of Cognition" effort to better integrate digital modeling into the research.

## What is NOT on this page

Congressional marks, the R-2 mission description and acquisition strategy, the industry vs government split of the whole request, and related program elements are recorded at **program-element** grain — an NDAA mark lands on a PE, never on a project. They are at https://hitchintel.com/programs/0602202F.

## Source & machine access

- **Source:** FY2027 Department of the Air Force RDT&E Budget Justification, Exhibits R-2/R-2A/R-3, PE 0602202F project 621123 (PB PB2027).
- **MCP:** `mcp.hitchintel.com` — `budget_get_program_element(pe="0602202F")`.

*HitchAI is an independent intelligence service, not affiliated with the U.S. Department of Defense. Budget figures are requests/estimates, not obligations.*