# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 1 Introduction
The study [18] introduces a comprehensive framework for applying digital twins to aircraft lifecycle management, leveraging data-driven models to enhance decision-making and operational efficiency. A specific framework for twin-spool turbofan engines is proposed in [19], where a hybrid approach combining mechanism-based and data-driven models is employed for improved accuracy. At the fleet-wide level, a diagnostic and health management framework utilizing digital twins for comprehensive aircraft monitoring is presented in [20]. Major aviation companies have also made significant advancements in digital twin platforms to support predictive maintenance and optimize operational strategies. Notable industry initiatives include Aviatar (Lufthansa Technik) [21], Skywise (Airbus) [22], Predix (General Electric) [23], PROGNOS (Air France Industries and KLM Engineering & Maintenance) [24], and AnalytX (Boeing) [25]. In aviation maintenance training, digital twin technologies are complemented by virtual reality (VR) and adaptive game-based systems. The study [26] evaluates virtual simulation-based training for aviation maintenance technicians, demonstrating that VR-enhanced training can significantly improve skill acquisition when integrated with conventional training methods. Another study [27] explores adaptive game-based maintenance training, utilizing a pedagogic interpretation engine that adjusts scenarios dynamically to maximize training effectiveness. Interactive 3D virtual maintenance training systems have also been developed to enhance practical skills in aviation maintenance. A study [28] presents a virtual reality training system for aviation students, showing its effectiveness in improving training outcomes. The comprehensive analysis provided in [29] reviews the applications and training challenges of integrating artificial intelligence into aviation, highlighting both opportunities and barriers to its effective deployment. Beyond aviation and education, recent advances in digital twin and AI-driven predictive maintenance are transforming asset management in other industrial sectors. A systematic literature review on intelligent maintenance in the mining industry underscores the growing adoption of digital twins, deep learning, and hybrid AI models to address challenges of fault detection and operational efficiency under extreme conditions [30]. Similarly, a hybrid approach for remaining useful life prediction of rolling element bearings combines digital twin modeling with long short-term memory networks and particle swarm optimization, achieving high prediction accuracies [31]. This fusion of simulation precision and data-driven forecasting supports proactive maintenance and health management, reinforcing the applicability of digital twin architectures across high-stakes, reliability-driven domains. These recent studies expand the technological landscape from which aviation education and maintenance training systems may draw, encouraging cross-domain integration of AI-enhanced, twin-based frameworks. In contemporary aviation education research, there remains a substantial gap between the theoretical capabilities of digital twin technologies and their practical implementation in competence-based training ecosystems. While prior studies have successfully demonstrated the applicability of digital twins in simulating aircraft systems, predictive maintenance, and immersive learning environments, these approaches often neglect several critical dimensions required for full educational integration. Specifically, existing frameworks tend to focus predominantly on static system representations without incorporating adaptive learner modeling, dynamic competence tracking, or personalized instructional orchestration based on real-time performance metrics. Moreover, limited attention has been given to the formalization of orchestration algorithms that simultaneously address multi-dimensional instructional objectives such as skill alignment, cognitive complexity, resource fidelity, and regulatory compliance.