# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 2 Materials and Methods
### Algorithmic Orchestration Workflow
For skills where gaps exist, the orchestration engine computes the magnitude of the discrepancy. The comparison yields a multi-dimensional gap vector, highlighting which areas fall short and by how much. Each skill gap is classified into severity categories--typically low, medium, or high--based on empirically defined thresholds. These categories directly inform the fidelity level of content that will be assigned in the next step. For example, a high-severity gap might trigger a foundational CBT module, whereas a low-severity gap may prompt an operational digital twin scenario. Based on the severity classification, the system selects a training asset from the Fidelity-Tagged Content Repository. The selection considers multiple factors, including regulatory compliance (e.g., EASA Part-66 module alignment), Bloom's Taxonomy level, expected training duration, and prior learner interaction history. Figure 2: Training flow diagram illustrating the simulation loop for competence-based orchestration. The assigned training is delivered through the appropriate interface (CBT, tablet-based simulation, or immersive XR module) via the edge or cloud layer. After the session, learner performance is logged, competence vectors are updated, and the loop begins again if needed. This closed-loop orchestration approach ensures that learners receive targeted, scalable, and regulation-compliant instruction throughout the training process. The integration of dynamic learner modeling, severity-based gap analysis, and fidelity-matched content delivery represents a significant advancement over static training models, enabling real-time personalization and continuous competence validation. To enhance procedural clarity and support implementation reproducibility, the following summary outlines the core orchestration logic as a step-by-step workflow. While the preceding section has already provided the mathematical foundations and detailed descriptions of each component, this condensed summary serves to highlight the sequential decision-making process employed by the orchestration engine. Step 1. Analyze the learner's current competence vector and compare it to the regulatory target vector to compute the skill gap. Step 2. Classify each gap as high, medium, or low severity based on threshold values. Step 3: Filter candidate training resources based on alignment with the most significant competence gaps. Step 4. Evaluate each candidate resource using a multi-objective cost function that considers skill alignment, fidelity penalty, Bloom-level complexity, and session duration. Step 5. Select the resource with the minimum cost as the optimal assignment for the learner. Step 6. Deliver the assigned training via appropriate interface (e.g., CBT, simulation, or XR) depending on device capability and fidelity requirement. Step 7. After the training session, update the learner's competence vector using a gain function based on responsiveness and instructional effectiveness. Step 8. Log all orchestration actions in the validation matrix to support traceability and compliance. Step 9. Repeat the orchestration cycle until all competence gaps fall below acceptable thresholds.