# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 4 Discussion
### Integrated Evaluation of Orchestration Efficiency  
The simulation results provide evidence of the operational feasibility, adaptive performance, and regulatory auditability of the proposed DTT framework. The algorithmic orchestration engine demonstrated a consistent capacity to drive rapid competence convergence across a heterogeneous learner cohort. All simulated learners achieved substantial reductions in competence gap norms within a limited number of six orchestration cycles, despite significant variability in their initial skill profiles. This competence-driven adaptive allocation ensured that instructional resources were concentrated on critical deficiencies, thereby minimizing redundant exposure to previously mastered content while accelerating skill acquisition in underdeveloped domains. The orchestration function's multi-objective optimization framework--incorporating competence gap magnitude, Bloom's cognitive complexity levels, training fidelity tiers, and instructional efficiency--enabled precise balancing between cognitive workload, regulatory compliance, and resource utilization. The integration of Bloom-level penalties effectively regulated the introduction of higher-order cognitive challenges, ensuring that learners were progressively exposed to complex training content only when their competence levels justified such advancement. Concurrently, the fidelity allocation model prevented unnecessary overuse of resource-intensive XR-based immersive simulations, preserving these high-cost assets for advanced remediation scenarios where they delivered the greatest pedagogical value. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learner ID** & **Iteration** & **Skill Domain** & **Pre-Training Gap** & **Resource ID** & **Fidelity Tier** & **Bloom** & **Session** & **Deciteness** & **Post-Training Competence** \\ \hline L1 & 1 & PN-2 & 0.40 & R102 & Low & 2 & 20 & 0.3 & 0.62 \\ \hline L1 & 2 & PN-5 & 0.25 & R205 & Medium & 3 & 35 & 0.5 & 0.72 \\ \hline L2 & 1 & PN-3 & 0.38 & R315 & Low & 2 & 25 & 0.3 & 0.75 \\ \hline L3 & 1 & PN-2 & 0.42 & R102 & Low & 2 & 20 & 0.3 & 0.60 \\ \hline L4 & 3 & PN-5 & 0.18 & R205 & High & 5 & 50 & 0.8 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of validation matrix log. Quantitative personalization accuracy was further validated through the monitoring of redundancy and overreach metrics throughout the orchestration process. These metrics reflect the orchestration engine's capacity to maintain instructional efficiency while simultaneously preserving pedagogical safety, avoiding both unnecessary repetition and premature cognitive overload. Beyond instructional optimization, the DTT system architecture incorporates a fully integrated validation matrix, enabling comprehensive auditability and traceability of instructional decisions. This matrix records detailed metadata for each orchestration cycle, including learner identifiers, competence gaps, assigned resources, fidelity tiers, Bloom levels, session durations, and post-training competence updates. The resulting data structure provides complete longitudinal records of individual learner trajectories and supports both real-time instructional oversight and retrospective institutional audits. This traceability capability is particularly critical for compliance with external regulatory standards such as EASA Part-66, ensuring that each training decision can be transparently reconstructed and pedagogically justified in both certification and accreditation contexts. The modular integration of reinforcement learning agents, federated learning protocols, and knowledge graph-based curriculum models further extends the DTT system's adaptability, scalability, and regulatory readiness. The reinforcement learning component enables progressive refinement of orchestration policies as longitudinal learner data accumulate. Federated learning mechanisms support institution-wide analytics while preserving data privacy, thus ensuring compliance with data protection regulations such as GDPR.