Professional working source library with tiered classification (Tier A for government/regulator/IR sources, Tier B for company/secondary sources, Tier Academic for peer-reviewed research). Contains 27 peer-reviewed articles across XR/VR training, AI/ML assessment, neurophysiology, maritime simulation, eVTOL/UAM, and human factors. Covers CAE baseline, cross-market demand, China hidden-risk signals, competitor records, and Gulf/India/Singapore evidence.
Peer-Reviewed Articles
Full PDF Available
DOI-Only Reference
Research Domains Covered
| Category | Tier A Count | Tier B Count | Academic | Languages |
|---|---|---|---|---|
| CAE baseline and disclosure | 10+ | 20+ | 2 | English |
| Cross-market demand signals | 25+ | 15+ | 3 | English, Chinese |
| China hidden-risk signals | 15+ | 10+ | 0 | Chinese, English |
| U.S. competitor records | 5+ | 15+ | 1 | English |
| Gulf / India / Singapore | 5+ | 10+ | 0 | English, Arabic |
| South Korea competitor records | 3+ | 5+ | 1 | English, Korean |
| XR/VR/AR training technology | 2 | 5+ | 12 | English |
| AI/ML in simulation training | 3 | 4 | 7 | English |
| Maritime simulation | 2 | 3 | 4 | English |
| eVTOL / Urban Air Mobility | 1 | 2 | 4 | English |
| # | Title / Topic | DOI |
|---|---|---|
| 1 | XR pilot simulator training — meta-analysis (Somerville et al. 2025, effect size 0.884, N=1237→67→5) | 10.1186/s42492-025-00206-w |
| 2 | XR flight simulators — scoping review (Ross & Gilbey 2023, 760K new pilots needed, PTN first-solo shift) | 10.1007/s13272-023-00688-5 |
| 3 | AR formation flight training (Arjoni et al. 2023, SIVOR Level-D FFS, Brazil AF) | 10.1016/j.heliyon.2023.e14181 |
| 4 | VR flight simulation EEG patterns (Van Weelden et al. 2024, beta-ratios p<0.001, r=0.48-0.58) | 10.1016/j.cogsys.2024.101282 |
| 5 | Spatial disorientation VR validation (Kim et al. 2025, 22 Korean F-15K pilots, SD=33% of accidents) | 10.1016/j.apergo.2024.104457 |
| 6 | VR HMD simulator, air force pilots | 10.1080/00140139.2025.2595656 |
| 7 | FSTD + AR acceptance, general aviation | 10.1038/s41598-025-85448-7 |
| 8 | XR in aerospace engineering (review) | See ScienceDirect: S0376042125000442 |
| 9 | Mixed reality flight simulator evaluation | 10.2514/1.I011738 |
| 10 | Simulator fidelity, ab initio pilots | 10.1080/00140139.2024.2449110 |
| 11 | Flight sim perceptual-motor skills | 10.1038/s41598-025-12929-0 |
| 12 | FSTD ecological/cost advantage | 10.3390/app14188401 |
| # | Title / Topic | DOI |
|---|---|---|
| 13 | ML-based performance assessment in simulation training | 10.1007/s40593-025-00464-y |
| 14 | AI in Finnish military simulators — scenario generation | See ScienceDirect: S1877050925031461 |
| 15 | Biofeedback + AI in XR training environments | 10.1177/10468781241236688 |
| 16 | Automation level impact on airline pilot performance (Causse et al. 2025, A320 Level-D, N=20) | 10.1016/j.apergo.2024.104456 |
| 17 | Affect and performance in simulated flying tasks — PRISMA review (Ruiz-Segura & Lajoie 2025, 29 articles) | 10.1080/24721840.2024.2425856 |
| 18 | Competency-based pilot assessment | 10.1007/s10111-023-00737-3 |
| 19 | EEG microstate analysis, pilot cognitive control | 10.1038/s41598-024-76046-0 |
| # | Title / Topic | DOI |
|---|---|---|
| 20 | Maritime education VR simulator — TAM study (Bacnar et al. 2025, N=84) | 10.3390/asi8030084 |
| 21 | Maritime team training for safety and security (Baldauf et al. 2016, WMU combined training) | 10.1080/19439962.2014.996932 |
| 22 | Maritime simulator curricula — competence management | 10.1007/s13437-024-00351-8 |
| 23 | Multimodal learning analytics in maritime training | 10.1007/s11412-024-09435-2 |
| # | Title / Topic | DOI |
|---|---|---|
| 24 | Urban air mobility collaborative system-of-systems simulation (Naeem et al. 2025, DLR) | 10.1007/s13272-024-00796-w |
| 25 | Mixed reality eVTOL training (CHI 2024) | 10.1145/3613904.3642060 |
| 26 | eVTOL training systemic approach (AIAA 2024) | 10.2514/6.2024-4214 |
| 27 | Digital twins for Advanced Air Mobility | 10.3390/drones9060394 |
The strongest Tier A sources are official U.S. Army PEO STRI materials, CAE SEC filings, government procurement announcements, and regulator documents. Chinese-language government and SASAC sources provide unique evidence not available in English-language coverage. Source depth is strongest for the U.S. synthetic training stack and weakest for maritime and land flank competitors.
The academic portfolio (27 peer-reviewed articles) provides the evidence base for technology domain entries. Key findings include: XR training produces a large effect size (d=0.884) for pilot training transfer; VR generates measurably higher neurological engagement than desktop simulation (EEG beta-ratio effect sizes r=0.48-0.58); ML classifiers can predict student pilot pass/fail outcomes from early-phase performance data; spatial disorientation accounts for 33% of military aviation accidents and VR-based SD training shows promising transfer; maritime VR adoption is driven by perceived usefulness rather than ease of use; and eVTOL/UAM training requires entirely new competency frameworks beyond existing rotorcraft curricula. Ten articles have full PDF available for deep citation; 17 are indexed by DOI for reference.