# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Digital Training Twin Model  
The core of the proposed system is the digital training twin, which provides a real-time, data-driven representation of each learner's evolving competence profile and supports personalized, adaptive training within an algorithmically controlled environment. The DTT ecosystem integrates multiple subsystems that track learner progress, analyze competence gaps, and dynamically assign appropriate training assets based on an intelligent orchestration process that operates across a hybrid cloud-edge infrastructure. Figure 1 illustrates the high-level architecture of the DTT ecosystem. The system architecture consists of eight key functional components. The Cloud Twin Server serves as the centralized knowledge base, hosting GPU-intensive simulations and immersive Extended Reality (XR) scenarios, including both virtual reality (VR) and Augmented Reality (AR), alongside the primary Training Database (DB). These XR-based training twins allow highly detailed, scalable simulation of complex aircraft maintenance tasks. The cloud infrastructure also ensures consistent access to up-to-date training resources and version-controlled instructional content across all deployment sites. At the algorithmic core of the system operates the orchestration engine (OE), responsible for continuously analyzing each learner's current skill profile, calculating competence gaps, classifying gap severity, and selecting appropriate training resources. The OE applies cost-optimization models that balance instructional relevance, training fidelity, regulatory compliance, and session duration. This allows the system to orchestrate non-redundant, efficient learning pathways tailored to individual learner needs. The Learner DTT Vectors Database stores real-time competence vectors for every learner, representing mastery levels across various technical domains. These vectors are dynamically updated after each training session, incorporating performance data collected from simulations, assessments, and practical exercises. Target competence levels are defined  
Figure 1: System architecture of the digital training twin ecosystem. in the competence profiles, which reflect external regulatory standards such as those defined by EASA Part-66 [32]. These profiles define the knowledge and skill levels that learners are required to achieve across specific aviation system modules, including ATA chapters [33]. To support detailed performance tracking and auditability, the system captures learning interactions through xAPI logs using the Experience API (xAPI) standard [34]. These logs include rich data describing simulation paths, learner decisions, scenario outcomes, completion times, and error frequencies, all of which feed directly into updating learner vectors and validating training progress. The available instructional content is organized in the Fidelity-Tagged Content Repository, where each resource is pre-annotated with structured metadata describing its instructional target (skill domain vector), Bloom's Taxonomy level, associated regulatory module, estimated duration, and fidelity classification (low, medium, or high). This structured tagging enables the orchestration engine to make precise, data-driven assignments for each learner at every stage of training. The edge device/XR delivery layer serves as the front-end interface, delivering training content directly to learners based on their competence state and local device capabilities. This layer supports Computer-Based Training (CBT), simplified diagnostic simulations, and full XR-based immersive scenarios. The edge deployment allows learners to complete foundational or remedial training even under limited network conditions while using the cloud for high-fidelity simulations as required. To maintain continuous data synchronization across the distributed architecture, the system employs a Kafka [35] and MQTT Message Backbone [36].