# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 3 Results
### System Traceability and Auditability  
Beyond dynamic orchestration and adaptive personalization, the DTT system incorporates a robust internal traceability framework through the implementation of a structured validation matrix. This matrix functions as a real-time logging mechanism that captures all orchestration decisions, learner competence updates, and training resource allocations. The resulting dataset enables comprehensive auditability for regulatory certification, instructor oversight, institutional reporting, and advanced learning analytics.  
For each orchestration cycle, the validation matrix records detailed metadata describing the entire instructional transaction. Specifically, the following parameters are logged:  
* Learner ID--uniquely identifies each learner within the system;
* Iteration--sequential orchestration cycle;
* Skill Domain--specific competence dimension addressed during the session;
* Pre-Training Gap--calculated skill gap prior to resource assignment;
* Resource ID--internal identifier of the training asset assigned;
* Fidelity Tier--resource fidelity classification (low, medium, high);
* Bloom Level--cognitive complexity level based on Bloom's Taxonomy (levels 1-6);
* Session Duration--scheduled or actual training time for the assignment;
* Instructional Effectiveness Coefficient--resource-specific learning gain multiplier;
* Post-Training Competence--updated competence value following session completion.  
This architecture ensures that every adaptive decision is fully documented and reproducible, allowing for granular reconstruction of individual learner trajectories and institutional compliance reviews. The matrix further supports real-time auditing by mapping orchestration behavior directly to competence progression pathways, offering  
Figure 11: Personalization accuracy metrics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Value** & **Interpretation** \\ \hline Initial Competence Gap Norm & 0.38–0.60 & Significant variability in baseline learner profiles \\ \hline Final Competence Gap Norm & \textless{}0.10 & Full regulatory convergence achieved in all learners \\ \hline Convergence Iterations & \textless{}6 iterations & Rapid adaptation within limited training cycles \\ \hline Redundancy Ratio & 3.2\% & Minimal unnecessary assignments \\ \hline Overreach Ratio & 1.1\% & Very few inappropriate high-fidelity assignments \\ \hline High-Fidelity Usage (XR) & 24\% & Immersive twins used sparingly for refinement \\ \hline Low-Fidelity Usage (CBT) & 44\% & Broadly used for gap remediation \\ \hline Regulatory Threshold Achievement & 100\% & Full compliance across entire learner cohort \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed statistics of training assignments and fidelity allocation events.  
fully transparent evidence for external certification bodies such as EASA or institutional accreditation boards.  
An illustrative extract from the validation matrix is provided in Table 3.  
The structure of the validation matrix supports both horizontal auditing (across learners and scenarios) and vertical auditing (over time for each learner), enabling direct verification that each training assignment was pedagogically justified, competence-driven, and regulation-compliant. Additionally, the matrix serves as a foundation for institutional learning analytics, future orchestration algorithm calibration, and continuous improvement of training resource allocation strategies.