Keywords: Skill transfer, Immersive Training, Pilot Training Optimization.
The United States Air Force has experienced a sustained pilot shortage for more than two decades, creating strategic risk as global operational demands increase (Penney, 2025; Grossman, 2021). Addressing this challenge requires training systems that improve learning efficiency and skill transfer rather than relying solely on increased flight hours or legacy instructional models. Traditional approaches often emphasize exposure and correctness without fully accounting for learner motivation, cognitive load, and adaptability.
Advances in gaming, virtual reality (VR), and visual learning—particularly when integrated with adaptive learning management systems—offer a path toward data-driven, scalable training aligned with learning science. This paper synthesizes existing research and presents a case study examining simulator-to-aircraft skill acquisition in military pilot training.
Motivation is a key determinant of learning and performance in high-stakes aviation environments. Self-Determination Theory identifies autonomy, competence, and relatedness as foundational drivers of sustained engagement (Ryan & Deci, 2000). Training environments that support these needs enhance retention, resilience, and performance under pressure.
Gaming and VR provide immersive, repeatable environments that support procedural sequencing, sensorimotor integration, and time-pressured decision-making. Research shows immersive VR increases motivation and confidence compared to traditional simulation (Conrad et al., 2024), while visual learning strengthens comprehension and memory through spatial representation (Alabi, 2024).
A Case Study in Modern Pilot Training-Methods. A case study was conducted with 21 U.S. Air Force pilot trainees at Columbus Air Force Base using T-38C simulator and aircraft performance data. The study focused on skill acquisition rather than exact replication of learning transfer, evaluating whether students could perform maneuvers proficiently once instructors confirmed required criteria were met. (Elmore, 2025). The dataset used in this study is derived from the MIF Grade Delta reports from PTT Portal and was segmented into three categories based on training medium: aircraft grade sheets, simulator grade sheets, and combined grade sheets. The reports capture individual maneuver-level grade performance across selected students.
Because the Maneuver Item File (MIF) standard for a given maneuver increases across training phases (e.g., from a grade of 2 in an earlier phase to a 3 in a later phase), raw grades are not directly comparable over time. To normalize performance across phases, each maneuver grade was converted to a delta from the applicable MIF standard at the time of evaluation. Grades were expressed as a positive value when exceeding the MIF (+), zero when matching the MIF (0), and a negative value when below the MIF (–).This approach allowed for consistent comparison of maneuver performance across training phases despite changes in grading standards.
This study does not assess competency attainment, but instead examines early skill acquisition during simulator-to-aircraft transitions. In learning engineering, competencies are defined as integrated and observable combinations of knowledge, skills, behaviors, and judgment that are demonstrated consistently across contexts and over time. Because competency evaluation requires longitudinal, multi-context evidence of stable performance and adaptability, it was beyond the scope of this study. Rather, the findings inform the effectiveness of simulation for supporting foundational skill development, highlighting the need for future research that applies competency-based measurement framework.
Simulator scores exceeded Maneuver Item File (MIF) standards across all evaluated maneuvers, indicating strong foundational skill acquisition. Aircraft performance demonstrated greater variability, reflecting increased cognitive and contextual demands. Procedural skills transferred more effectively than adaptive task management and situational awareness.
Simulator performance produced positive Maneuver Item File (MIF) delta scores across all evaluated maneuvers, ranging approximately from +0.81 to +1.74, indicating consistent performance above MIF standards in the simulated environment. Aircraft performance showed greater dispersion, with delta scores ranging from approximately –0.16 to +0.51, reflecting increased variability during live execution. Procedural maneuvers such as landing and general aircraft control demonstrated higher aircraft delta scores (approximately +0.27 to +0.51), whereas maneuvers requiring higher cognitive demand, including task management and situational awareness, yielded lower aircraft deltas (approximately –0.16 to –0.08). These quantitative patterns indicate stronger simulator-to-aircraft alignment for structured procedural skills relative to tasks requiring dynamic judgment and adaptation.
Table 1.
Results-Analysis of Simulator-to-Aircraft Skill Acquisition
From a learning engineering perspective, findings from the iPerformX study indicate that certain maneuvers demonstrate stronger simulator-to-aircraft transfer because they rely primarily on procedural knowledge, perceptual cues, and structured motor sequences that can be reliably replicated in high-fidelity simulator environments (Elmore, 2025). Maneuvers such as landing, general aircraft control, and in-flight planning benefited from repeated, deliberate practice under stable conditions, reinforcing muscle memory, visual–spatial processing, and standardized decision pathways. Learning science research shows that skills governed by clear rules, consistent feedback, and limited variability are more likely to transfer successfully from simulation, as they impose manageable cognitive load and support schema formation. In contrast, maneuvers requiring rapid judgment, task prioritization, and adaptation under uncertainty—such as task management—depend on dynamic environmental cues and real-world stressors that are more difficult to fully replicate in simulated settings, requiring additional contextual variation and instructor-guided reinforcement to achieve equivalent aircraft performance.
Learning engineering provides a formal framework for interpreting these findings and guiding continuous improvement. As outlined in Engineering at a Glance in the Journal of Military Learning, this work reflects a nested learning engineering cycle (Craig et al., 2025) consisting of data collection (simulator and aircraft measures), analysis (performance patterns), interpretation (transfer strengths and limitations), and iteration planning (instructional refinement). Within this framework, the case study conducted by Elmore (2025) represents an applied learning engineering effort in which empirical performance data informed instructional decisions rather than treating simulation, gaming, or visual learning as isolated interventions. By linking learner-level performance data to instructional design and future system-level adjustments, the study demonstrates how learning engineering operates in practice—supporting data-driven refinement, improved readiness, and more efficient training outcomes over time (Goodell et al., 2023).
The pilot shortage represents a learning and performance challenge as much as a capacity issue. Evidence from learning science, immersive technology research, and the case study demonstrates that gaming, VR, and visual learning enhance motivation, retention, and procedural skill acquisition. When integrated within adaptive, data-driven training systems, these modalities strengthen pilot readiness and training efficiency.
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