Digital-Twin-Based Ecosystem for Aviation Maintenance Training

The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosystem tailored for aviation maintenance education. The system integrates three core digital twin models: the learner digital twin, which continuously reflects individual trainee competence; the ideal competence twin, which encodes regulatory skill benchmarks; and the learning ecosystem twin, a stratified repository of instructional resources. These components are orchestrated through a real-time adaptive engine that performs multi-dimensional competence gap analysis and dynamically matches learners with appropriate training content based on gap severity, Bloom’s taxonomy level, and content fidelity. The system architecture uses a cloud–edge hybrid model to ensure scalable, secure, and latency-sensitive delivery of training assets, ranging from computer-based training modules to high-fidelity operational simulations. Simulation results confirm the system’s ability to personalize instruction, accelerate competence development, and support continuous regulatory readiness by enabling closed-loop, adaptive, and evidence-based training pathways in digitally enriched environments.

Abstract licence: CC BY

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