# Learning to Fly in World Models ## 100 Creative Commons articles for Aviation Futures Curated on 30 October 2026. This reading collection connects flight-school practice with renderer design, simulation fidelity, and the people and AI agents who act within training environments. It is a bibliography and editorial research guide, not a republication of the source articles. The structure adapts Dr. Freedom Cheteni’s [Render. Simulate. Plan. / Movement Thinking essay](https://k12.thevrschool.org/media/world-models-and-learning) and the accompanying [World Model Intelligence course](https://k12.thevrschool.org/courses/world-model-intelligence). The volume uses **Actors** for the human and synthetic participants who perceive, plan and act. These publications supply the organizing framework; they are outside the 100-article CC count. The editorial question is whether trainees can perceive relevant cues, model consequences and choose effective actions when conditions change. Each chapter connects a technical capability with an observable training question. ## Collection at a glance | Layer | Articles | Coverage | | --- | ---: | --- | | Renderer | 24 | Visual scanning, AR interfaces, scenery, sensory cues and sickness | | Simulator | 29 | Transfer, adoption, dynamics, fidelity, logging and adaptive scenarios | | Actors | 47 | Instructors, assessment, workload, uncertainty, synthetic participants and human–AI teams | The scope comprises **61 flight-training articles, 21 aviation human-factors articles, 15 flight-simulation engineering articles, two adjacent ATC-training articles and one adjacent aeronautical-engineering education article**. Supporting engineering and adjacent evidence is explicitly labeled. These are 100 distinct publications, not 100 independent trials; some publications may analyze related samples. ## Proposed volume structure | Chapter | Articles | Guiding question | | --- | ---: | --- | | R1. Cockpit perception and scanning | 7 | Which cues do trainees notice, miss and return to? | | R2. XR interfaces and displays | 8 | Does the display make task-relevant information usable? | | R3. Sensory cues and comfort | 9 | Do sensory cues support practice without excessive discomfort? | | S1. Training transfer and adoption | 14 | Which skills transfer, for whom, and to what assessment setting? | | S2. Dynamics and simulator fidelity | 9 | Do modeled dynamics and control responses match the training objective? | | S3. Training infrastructure and scenarios | 6 | Can instructors adapt scenarios and reconstruct what happened? | | A1. Curriculum, instructors and assessment | 13 | What should learners and instructors demonstrate, and how is it assessed? | | A2. Workload and adaptive learning | 12 | How should challenge and feedback respond to the learner? | | A3. Safety, uncertainty and startle | 14 | How do people respond when expectations fail? | | A4. Synthetic actors and human–AI teams | 8 | How should human and synthetic participants communicate and share authority? | ## Start with these 12 articles | ID | Reading | Why start here | | --- | --- | --- | | 029 | [Applications of extended reality in pilot flight simulator training: a systematic review with meta-analysis](https://doi.org/10.1186/s42492-025-00206-w) | Broad synthesis and evidence gaps | | 033 | [Extended reality (xR) flight simulators as an adjunct to traditional flight training methods: a scoping review](https://doi.org/10.1007/s13272-023-00688-5) | Map of XR training applications | | 038 | [Efficacy of Low-Cost PC-Based Aviation Training Devices](https://doi.org/10.28945/3682) | Flight-school cost and quasi-transfer question | | 047 | [Review of flight simulation fidelity requirements to help reduce ‘rotorcraft loss of control in-flight’ accident rates](https://doi.org/10.1007/s13272-021-00542-6) | Fidelity tied to rotorcraft training needs | | 014 | [Multimodal Augmented Reality Applications for Training of Traffic Procedures in Aviation](https://doi.org/10.3390/mti7010003) | Controlled study of AR-supported VFR procedures | | 007 | [Research on Flight Training Optimization with Instrument Failure Based on Eye Movement Data](https://doi.org/10.3390/jemr18030019) | Instructor gaze examples as a training intervention | | 032 | [Assessing evidence-based training in a collaborative virtual reality flight simulator](https://doi.org/10.1017/aer.2024.113) | Collaborative VR and crew competencies | | 065 | [Identifying Behavior Indicators for Flight Instructor Competencies and Training Needs Analysis for General Aviation Flight Instructor](https://doi.org/10.12985/ksaa.2023.31.1.068) | Flight-instructor competencies and training needs | | 063 | [The interconnection between evaluated and self-assessed performance in full flight simulator training](https://doi.org/10.1007/s12186-023-09339-6) | Debriefing, self-assessment and risk appraisal | | 077 | [Neuroadaptive Training via fNIRS in Flight Simulators](https://doi.org/10.3389/fnrgo.2022.820523) | Adaptive difficulty informed by learner workload | | 100 | [Design of simulation-based pilot training systems using machine learning agents](https://doi.org/10.1017/aer.2022.8) | Requirements for machine-learning training actors | | 093 | [A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers](https://doi.org/10.3390/aerospace10050490) | A concrete virtual pilot for communication practice | ## Editorial synthesis for flight schools 1. **Begin with a task and a transfer test.** Specify what learners should do, then decide what must be rendered and simulated to teach it. Quasi-transfer to another simulator and transfer to an aircraft are different outcomes. Read 029, 028, 038, 047. 2. **Make expert perception teachable.** Combine scan examples with scenario context and debriefing. Evaluate task performance as well as gaze measures. Read 005, 003, 007, 002. 3. **Measure comfort alongside performance.** Match sensory cues and plan how symptoms will be monitored. The evidence does not establish a universal post-simulator waiting period. Read 016, 018, 021, 023. 4. **Treat the instructor as part of the system.** Connect instructor competencies, observable behaviors, replay and learner reflection. Read 065, 066, 050, 063, 064. 5. **Use adaptation as a testable instructional hypothesis.** Sensor or AI accuracy is not itself a learning outcome. Evaluate retention and transfer after adapting challenge. Read 052, 077, 073, 072. 6. **Give synthetic actors explicit roles and limits.** Define communication, authority, scenario behavior and instructor oversight. Read 100, 093, 096, 094, 095. These are editorial applications of the collection. They are not aircraft operating procedures, qualification standards or claims that any particular commercial product has been tested by these studies. ## Connection to Ahmed’s product-led articles The source papers retain their published authors and author order. Ahmed can lead original product-focused syntheses that cite them; the cited papers must not be relabeled as Ahmed-authored work. The mappings below propose editorial questions, not evidence that the products were evaluated in the cited studies. | Product anchor | Proposed original article question | Useful source IDs | | --- | --- | --- | | Want To Be a Pilot | How should an aspiring pilot understand training pathways, practice and competency? | 055, 058, 065, 064 | | Aviation Infinity | How can a learning environment connect observation, practice, feedback and transfer? | 029, 014, 007, 077 | | New Pilot Shop | How should schools evaluate the usability and learning role of headsets, controls and feedback devices? | 021, 020, 045, 038 | | AvioCalc | How can quantitative reasoning and model limits be made visible during training? | 043, 042, 057, 050 | | AvioSharing | What makes shared simulation and remote instructor support educationally useful? | 032, 053, 063, 095 | | Avioyx | How could a connected aviation environment coordinate learners, instructors, scenarios and AI actors? | 052, 100, 093, 096 | ## Licensing and research method **Licenses:** 80 articles carry CC BY (77 CC BY 4.0 and three CC BY 3.0), seven carry CC BY-NC 4.0, and 13 carry CC BY-NC-ND 4.0. All entries include the exact license URL and its recorded version scope. Attribution is required. NC restricts commercial reuse; ND restricts distributing adaptations. Check article-specific third-party credit lines before reproducing figures or other embedded material. See the [CC BY deed](https://creativecommons.org/licenses/by/4.0/), [CC BY-NC deed](https://creativecommons.org/licenses/by-nc/4.0/) and [CC BY-NC-ND deed](https://creativecommons.org/licenses/by-nc-nd/4.0/). **License evidence:** all 100 have explicit CC license URLs in publisher-deposited Crossref records. Ninety-seven records specify version-of-record or unspecified-version rights. Three records specify only text-mining rights; their publisher article pages were separately checked and explicitly state broader CC BY-NC article licensing. For 70 articles overall, the matching CC link was also found on the retrieved article page alongside the article DOI. Thirty rely on publisher-deposited metadata and should receive an article/PDF rights check before full-text republication. No item was accepted solely on a TDM license. This distinction appears in every entry and in the workbook. See [Crossref’s explanation of license/version metadata](https://www.crossref.org/documentation/schema-library/markup-guide-metadata-segments/license-information/). **Search and selection:** 26 broad Crossref searches and 13 title-focused searches covered flight training, schools, simulators, XR, motion cueing, gaze, workload, competencies, startle, adaptive training, AI actors, helicopter operations and ATC training. Two additional targeted article lookups added instructor-training and rotorcraft-fidelity sources. Primary publisher and repository pages supported focused checks. Records were deduplicated by DOI and normalized title. Unrelated medical “pilot studies,” non-CC/free-to-read-only items, retraction notices and out-of-scope records were excluded. Publication cutoff: 30 October 2026. **Limits:** this is a purposive reading collection rather than a systematic review or formal quality appraisal. The corpus mixes reviews, experiments, surveys, conceptual work, technical studies and conference articles, each labeled. Annotations use retrieved abstracts or publisher descriptions where available; entries screened only by title and bibliographic metadata are explicitly identified. They give reading relevance rather than invented findings. Publication years follow the Crossref published-date field and can differ from later issue years. Publisher metadata may contain errors or change. ## Annotated bibliography ### R1 Cockpit perception and scanning #### 001. Improving the pilot selection process by using eye-tracking tools Slaviša Ivan Vlačić; Aleksandar Zdravko Knežević; Saptarshi Mandal; Sanja Rođenkov; Panos Vitsas (2020). *Journal of Eye Movement Research*. [Article / DOI](https://doi.org/10.16910/jemr.12.3.4). **Evidence:** Selection and eye-tracking study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines visual attention measures as a complement to pilot selection. Useful for discussing scan assessment before increasingly demanding training phases. **Interpretation:** The reported relationship with psychological tests did not extend to flight-screening selection results. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1995-8692/12/3/17)); scope `unspecified`. Checked 2026-10-30. #### 002. The Application of Flexible Areas of Interest to Pilot Mobile Eye Tracking Zbigniew Gomolka; Damian Kordos; Ewa Zeslawska (2020). *Sensors*. [Article / DOI](https://doi.org/10.3390/s20040986). **Evidence:** Tool development and comparison. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Demonstrates flexible areas of interest for mobile eye tracking in a simulator. Supports debriefs that connect instrument attention to training experience. **Interpretation:** An attention-measurement tool is not, by itself, a validated pass/fail competency standard. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1424-8220/20/4/986)); scope `vor`. Checked 2026-10-30. #### 003. From Gaze Allocation to Scan Sequences: A Segment-Sensitive Eye-Tracking Study of Pilot Scan Discipline During Simulated Instrument Landing System Approaches Bo Fu; Jonathan Peña; Oscar Arenas; Russell Harral (2026). *Journal of Eye Movement Research*. [Article / DOI](https://doi.org/10.3390/jemr19050109). **Evidence:** Eye-tracking simulator study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines scan sequences across segments of instrument approaches. Offers candidate debrief measures that consider the organization of cross-checks, not only dwell time. **Interpretation:** Candidate assessment measures require validation beyond the studied pilots, aircraft and approach task. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1995-8692/19/5/109)); scope `vor`. Checked 2026-10-30. #### 004. Impact of automation level on airline pilots’ flying performance and visual scanning strategies: A full flight simulator study Mickaël Causse; Manuel Mercier; Olivier Lefrançois; Nadine Matton (2025). *Applied Ergonomics*. [Article / DOI](https://doi.org/10.1016/j.apergo.2024.104456). **Evidence:** Full-flight simulator study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Addresses the relationship between automation level, flying performance and visual scanning. A useful reading for comparing manual and automated monitoring exercises. **Interpretation:** Included for subject relevance; detailed findings were not independently appraised in this collection. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.apergo.2024.104456)); scope `vor`. Checked 2026-10-30. #### 005. Visual scanning strategies in the cockpit are modulated by pilots’ expertise: A flight simulator study Christophe Lounis; Vsevolod Peysakhovich; Mickaël Causse (2021). *PLOS ONE*. [Article / DOI](https://doi.org/10.1371/journal.pone.0247061). **Evidence:** Expert–novice simulator comparison. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Compares professional pilots and novices during simulated landings. Helps frame scan coaching around attention distribution and scan structure as well as individual fixations. **Interpretation:** Observed expertise differences do not prove that copying an expert scan alone produces equivalent proficiency. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247061)); scope `vor`. Checked 2026-10-30. #### 006. Visual Scanning in an Air Traffic Control Tower – A Simulation Study Peer G. Manske; Sebastian L. Schier (2015). *Procedia Manufacturing*. [Article / DOI](https://doi.org/10.1016/j.promfg.2015.07.397). **Evidence:** Conference simulation study. **Scope:** Adjacent: ATC training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Provides an air-traffic-control perspective on visual scanning. Useful for understanding the attention demands of another actor in the same aviation environment. **Interpretation:** Controller tasks differ from cockpit tasks; any transfer to pilot instruction is an editorial application. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.promfg.2015.07.397)); scope `vor`. Checked 2026-10-30. #### 007. Research on Flight Training Optimization with Instrument Failure Based on Eye Movement Data Jiwen Tai; Yu Qian; Zhili Song; Xiuyi Li; Ziang Qu; Chengzhi Yang (2025). *Journal of Eye Movement Research*. [Article / DOI](https://doi.org/10.3390/jemr18030019). **Evidence:** Training intervention study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Uses instructor eye-movement examples to teach responses to instrument failure. Supports an article on making expert monitoring strategies visible during instruction. **Interpretation:** Benefits were measured in specified simulator maneuvers; wider transfer and retention need evaluation. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1995-8692/18/3/19)); scope `vor`. Checked 2026-10-30. ### R2 XR interfaces and displays #### 008. Development of multimodal augmented reality applications on the flight deck for single-pilot operations: subjective user satisfaction Wen-Chin Li; Jingyi Zhang; James Blundell; Declan Saunders; Samuel Court; Dujuan Sevillian (2026). *Virtual Reality*. [Article / DOI](https://doi.org/10.1007/s10055-026-01411-5). **Evidence:** Comparative user study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Compares voice and gesture interaction with AR landing checklists. Useful for designing prompts that pilots can use while managing flight tasks. **Interpretation:** Subjective usability in a specific task does not establish operational approval for single-pilot airline flight. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s10055-026-01411-5?error=cookies_not_supported&code=344fd8bd-af69-49f6-b568-70ea6fede685)); scope `vor`. Checked 2026-10-30. #### 009. Effect of simulator frame rate on pilot training for high-speed fighter aircraft John Jong-Jin Kim; Ramy Kirollos (2026). *Frontiers in Physiology*. [Article / DOI](https://doi.org/10.3389/fphys.2026.1763485). **Evidence:** Literature review. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Reviews how simulator frame rate relates to fast visual scenes, performance, sickness and training. Useful when specifying the renderer for demanding tasks. **Interpretation:** The review identifies evidence gaps; it does not establish one universally adequate frame-rate threshold. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1763485/full)); scope `vor`. Checked 2026-10-30. #### 010. Remote Sensing Image Super-Resolution for the Visual System of a Flight Simulator: Dataset and Baseline Wenyi Ge; Zhitao Wang; Guigui Wang; Shihan Tan; Jianwei Zhang (2021). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace8030076). **Evidence:** Dataset and technical benchmark. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Introduces airport imagery and super-resolution methods for simulator scenery. Useful for renderer discussions about terrain detail, visual references and image quality. **Interpretation:** Image reconstruction performance is not evidence of improved learning or simulator certification. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/8/3/76)); scope `vor`. Checked 2026-10-30. #### 011. Designing augmented reality for future commercial aviation: a user-requirement analysis with commercial aviation pilots James Blundell; Don Harris (2023). *Virtual Reality*. [Article / DOI](https://doi.org/10.1007/s10055-023-00798-9). **Evidence:** User-requirements study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Uses pilot and aviation-expert input to identify AR applications and requirements. Supports designing displays around situation awareness and operational needs. **Interpretation:** Requirements from eleven experts need subsequent task-specific design and validation. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s10055-023-00798-9?error=cookies_not_supported&code=cacde50e-c346-4bbe-b467-7228b264914d)); scope `vor`. Checked 2026-10-30. #### 012. Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database Karolina Nurzynska; Przemysław Skurowski; Magdalena Pawlyta; Krzysztof Cyran (2021). *Sensors*. [Article / DOI](https://doi.org/10.3390/s21227687). **Evidence:** Computer-vision benchmark. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Benchmarks cockpit-device recognition for AR training overlays. Helps connect object recognition, registration and responsiveness to usable cockpit guidance. **Interpretation:** Recognition accuracy on a dedicated database does not establish effectiveness in operational training. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1424-8220/21/22/7687)); scope `vor`. Checked 2026-10-30. #### 013. Human-computer interaction interface design of flight simulator based on situation awareness Zhengyi Shen; Guoqiang Chen; Weilong Tu; Yuchi Yang; Teng Li; Li Xu; Deyang Han (2024). *Scientific Reports*. [Article / DOI](https://doi.org/10.1038/s41598-024-78043-9). **Evidence:** Interface design and evaluation. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0). Applies a situation-awareness framework to simulator interface design. Useful for organizing information around what trainees need during each flight phase. **Interpretation:** Interface-specific evaluation should be checked against independent performance and transfer measures. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://www.nature.com/articles/s41598-024-78043-9?error=cookies_not_supported&code=634985dd-619a-47dc-a1b9-07186cbb2873)); scope `vor`. Checked 2026-10-30. #### 014. Multimodal Augmented Reality Applications for Training of Traffic Procedures in Aviation Birgit Moesl; Harald Schaffernak; Wolfgang Vorraber; Reinhard Braunstingl; Ioana Victoria Koglbauer (2022). *Multimodal Technologies and Interaction*. [Article / DOI](https://doi.org/10.3390/mti7010003). **Evidence:** Controlled training study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Tests multimodal AR support for VFR traffic procedures. Useful for lessons combining scanning, collision assessment and right-of-way decisions. **Interpretation:** AR and simulator groups achieved similar overall performance; the findings do not justify a blanket superiority claim. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2414-4088/7/1/3)); scope `vor`. Checked 2026-10-30. #### 015. Performance, Emotion, Presence: Investigation of an Augmented Reality-Supported Concept for Flight Training Birgit Moesl; Harald Schaffernak; Wolfgang Vorraber; Reinhard Braunstingl; Ioana Victoria Koglbauer (2023). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app132011346). **Evidence:** Controlled training study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Evaluates AR-supported approach training through performance, presence and emotion. Useful for comparing learning outcomes with learners' experience of immersion. **Interpretation:** No significant overall advantage over conventional simulator training was found between groups. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/13/20/11346)); scope `vor`. Checked 2026-10-30. ### R3 Sensory cues and comfort #### 016. Effects of Flight Experience or Simulator Exposure on Simulator Sickness in Virtual Reality Flight Simulation Alexander Somerville; Keith Joiner; Graham Wild (2025). *Multimodal Technologies and Interaction*. [Article / DOI](https://doi.org/10.3390/mti9030024). **Evidence:** Quasi-experimental study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines whether previous flying or desktop-simulator exposure changes VR sickness. Useful for sequencing early VR lessons and measuring discomfort alongside learning. **Interpretation:** Prior flight experience and simulator exposure had different associations; do not assume desktop practice prevents VR sickness. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2414-4088/9/3/24)); scope `vor`. Checked 2026-10-30. #### 017. Principles for Safe Use of Virtual Reality, Augmented Reality, and Mixed Reality in Flight Training Ondřej Zavila; Petr Michenka; Jan Bořil; Pavlína Engelová (2025). *Advances in Military Technology*. [Article / DOI](https://doi.org/10.3849/aimt.01978). **Evidence:** Safety guidance synthesis. **Scope:** Flight training. **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0). Organizes practical precautions for VR, AR and mixed-reality training, including recovery after exposure and users who may require closer supervision. **Interpretation:** A guidance synthesis, not a controlled demonstration that a particular headset or timetable is safe. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.aimt.cz/index.php/aimt/article/view/1978)); scope `unspecified`. Checked 2026-10-30. #### 018. Analysis of Visual and Vestibular Information on Motion Sickness in Flight Simulation Ahmad Javaid; Shahzad Rasool; Adnan Maqsood (2024). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace11020139). **Evidence:** Small experimental study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines visual–vestibular coupling and sickness on a motion platform. Useful for treating sensory consistency as part of renderer and simulator integration. **Interpretation:** Only five participants were studied; repeated responses do not constitute a large independent sample. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/11/2/139)); scope `vor`. Checked 2026-10-30. #### 019. Hear me Flying! Does Visual Impairment Improve Auditory Display Usability during a Simulated Flight? Benoît Valéry; Vsevolod Peysakhovich; Mickaël Causse (2015). *Procedia Manufacturing*. [Article / DOI](https://doi.org/10.1016/j.promfg.2015.07.596). **Evidence:** Conference article. **Scope:** Aviation human factors. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Explores auditory display usability during simulated flight with altered visual conditions. Supports a chapter on distributing information across sensory channels. **Interpretation:** Included for subject relevance; inspect the study conditions before applying conclusions to pilot instruction. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.promfg.2015.07.596)); scope `vor`. Checked 2026-10-30. #### 020. Effect of Haptic Feedback on Pilot/Operator Performance During Flight Simulation Hassam Ahmed Malik; Shahzad Rasool; Adnan Maqsood; Rizwan Riaz (2020). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app10113877). **Evidence:** Experimental UAV-control study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Compares absent, realistic and exaggerated haptic feedback. Illustrates why the usefulness of feedback should be tested separately for different flight tasks. **Interpretation:** Fifteen untrained users and simplified UAV dynamics limit generalization to licensed pilots and certified devices. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/10/11/3877)); scope `vor`. Checked 2026-10-30. #### 021. Strategies to manage human factors in mixed reality helicopter pilot training: a systematic literature review Antonio Perez; Avinash Singh; Jonathan Mitchell; Philip Swadling (2026). *Frontiers in Virtual Reality*. [Article / DOI](https://doi.org/10.3389/frvir.2026.1815082). **Evidence:** Systematic literature review. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Synthesizes headset-related human factors and mitigation approaches for helicopter training. A strong starting point for comfort, calibration, ergonomics and onboarding. **Interpretation:** Much of the evidence comes from VR or analogous settings; transfer to MR and specific devices remains conditional. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/virtual-reality/articles/10.3389/frvir.2026.1815082/full)); scope `vor`. Checked 2026-10-30. #### 022. Objective Fidelity Evaluation in Multisensory Virtual Environments: Auditory Cue Fidelity in Flight Simulation Georg F. Meyer; Li Ting Wong; Emma Timson; Philip Perfect; Mark D. White (2012). *PLoS ONE*. [Article / DOI](https://doi.org/10.1371/journal.pone.0044381). **Evidence:** Multisensory experimental study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Frames simulator fidelity around task performance and evaluates auditory cues. Useful for deciding which sensory details matter for a particular lesson. **Interpretation:** Cue value is task dependent; perceptual realism should not be treated as a universal proxy for learning. **Screening:** Publisher description screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1371%2Fjournal.pone.0044381)); scope `unspecified`. Checked 2026-10-30. #### 023. Post-exposure effects of simulator sickness in motion-based flight simulators: a review of symptom duration and implications for flight fitness Nico Willebrand genannt Overgahr; David Feibus; Stefan Sammito (2026). *Frontiers in Physiology*. [Article / DOI](https://doi.org/10.3389/fphys.2026.1848400). **Evidence:** Review. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Reviews sickness after-effects following motion-based simulation. Supports training schedules that consider post-session symptoms and subsequent flying activity. **Interpretation:** The review cannot establish an evidence-based universal waiting period before real flight. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2026.1848400/full)); scope `vor`. Checked 2026-10-30. #### 024. Generating Flight Illusions Using Galvanic Vestibular Stimulation in Virtual Reality Flight Simulations Gaurav N. Pradhan; Raquel Galvan-Garza; Alison M. Perez; Jamie Bogle; Michael J. Cevette (2022). *Frontiers in Neuroergonomics*. [Article / DOI](https://doi.org/10.3389/fnrgo.2022.883962). **Evidence:** Experimental feasibility study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Explores vestibular stimulation as a way to reproduce flight illusions in fixed-base VR. Useful as research on perceptual mismatch and disorientation training. **Interpretation:** An experimental stimulation protocol, not a ready-to-deploy school exercise or medical recommendation. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2022.883962/full)); scope `vor`. Checked 2026-10-30. ### S1 Training transfer and adoption #### 025. Effectiveness Analysis of Helicopter Flight Simulator and Actual Flight Training: Focused on Instrument Flight Training Sang-chul Kim; Jong-min Kim (2020). *Journal of the Korean Society for Aviation and Aeronautics*. [Article / DOI](https://doi.org/10.12985/ksaa.2020.28.1.075). **Evidence:** Comparative training analysis. **Scope:** Flight training. **License:** [CC BY-NC 4.0](http://creativecommons.org/licenses/by-nc/4.0). Compares helicopter simulator and actual instrument-flight evaluations. Relevant to deciding where simulator practice belongs in a flight-school syllabus. **Interpretation:** Aircraft type, trainee characteristics and the assessment design affect interpretation of the relationships. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://www.jksaa.org/archive/view_article?pid=jksaa-28-1-75)); scope `Publisher article license (Crossref: tdm)`. Checked 2026-10-30. #### 026. Evaluating the effectiveness of flight simulator training on developing perceptual-motor skills among flight cadets: a pilot study Yang Zhang; Jie Chen; Xiao Zhong; Yongcong Shao; Weiwei Fu (2025). *Scientific Reports*. [Article / DOI](https://doi.org/10.1038/s41598-025-12929-0). **Evidence:** Pilot training study. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0). Studies perceptual-motor learning with simulator-based replay and multimodal feedback. Useful for structuring deliberate practice and feedback around cadet performance. **Interpretation:** A pilot study in a specific training system does not establish broad operational transfer. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://www.nature.com/articles/s41598-025-12929-0?error=cookies_not_supported&code=529de911-d3df-45fb-bc7f-530f2c20abcd)); scope `vor`. Checked 2026-10-30. #### 027. Enhancing Pilot ‘Mission’ Projection Through a Virtual Reality Flight Simulator: A Quasi-Transfer of Training Study Alexander Somerville; Keith Joiner; Graham Wild (2026). *Sci*. [Article / DOI](https://doi.org/10.3390/sci8040070). **Evidence:** Quasi-transfer study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines basic flying and spatial mission projection after VR practice. Useful for relating an immersive world to measurable skill outcomes. **Interpretation:** Quasi-transfer and the study design limit conclusions about transfer to actual flight. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2413-4155/8/4/70)); scope `vor`. Checked 2026-10-30. #### 028. Virtual Reality in PCATD-Based Instrument Flight Training: A Quasi-Transfer of Training Study Alexander Somerville; Keith Joiner; Graham Wild (2026). *Technologies*. [Article / DOI](https://doi.org/10.3390/technologies14020094). **Evidence:** Quasi-transfer study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Evaluates a low-cost VR device for instrument approach practice. Useful for a flight-school discussion of immersion when instrument interpretation is the main task. **Interpretation:** The 44-participant study concerns a defined approach task, not an entire instrument-rating syllabus. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2227-7080/14/2/94)); scope `vor`. Checked 2026-10-30. #### 029. Applications of extended reality in pilot flight simulator training: a systematic review with meta-analysis Alexander Somerville; Keith Joiner; Timothy Lynar; Graham Wild (2025). *Visual Computing for Industry, Biomedicine, and Art*. [Article / DOI](https://doi.org/10.1186/s42492-025-00206-w). **Evidence:** Systematic review and meta-analysis. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Provides a broad entry point to XR flight training, educational rationale and empirical gaps. Use it to orient the volume's transfer-of-training questions. **Interpretation:** Only five studies entered the meta-analysis; a broad thematic literature does not mean broad causal evidence. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1186/s42492-025-00206-w?error=cookies_not_supported&code=a33af49b-af1a-4187-9327-70f5439ea9db)); scope `vor`. Checked 2026-10-30. #### 030. Exploring technology acceptance of flight simulation training devices and augmented reality in general aviation pilot training Syed A. Q. Rizvi; Umair Rehman; Shi Cao; Bradley Moncion (2025). *Scientific Reports*. [Article / DOI](https://doi.org/10.1038/s41598-025-85448-7). **Evidence:** Technology-acceptance survey. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0). Reports general-aviation pilots' views on FSTDs and AR. Helps flight schools examine adoption, perceived usefulness and practical demand. **Interpretation:** Interest and acceptance are not measurements of skill transfer or training effectiveness. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://www.nature.com/articles/s41598-025-85448-7?error=cookies_not_supported&code=90c44845-9514-43ab-97ff-0ff1a9f0ffe9)); scope `vor`. Checked 2026-10-30. #### 031. The Potential of Flight Simulation to Support Pilot Training for Mountain Helicopter Emergency Medical Services Neale A. Watson; Nicholas Fernandez; Ieuan Owen; Mark D. White (2025). *Air Medical Journal*. [Article / DOI](https://doi.org/10.1016/j.amj.2025.06.002). **Evidence:** Training application article. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Addresses simulator use for mountain helicopter emergency medical-service pilots. Adds a specialized operating context to discussions of scenario design. **Interpretation:** Included for subject relevance; detailed effectiveness claims require full study appraisal. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.amj.2025.06.002)); scope `vor`. Checked 2026-10-30. #### 032. Assessing evidence-based training in a collaborative virtual reality flight simulator J.I. Cross; T. Ryley (2024). *The Aeronautical Journal*. [Article / DOI](https://doi.org/10.1017/aer.2024.113). **Evidence:** Mixed-methods feasibility study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Explores evidence-based training with multiple pilots in collaborative VR. Useful for remote crew instruction and communication when headsets restrict normal visual contact. **Interpretation:** Feasibility in selected scenarios is a precursor to wider validation, not proof of regulatory equivalence. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.cambridge.org/core/journals/aeronautical-journal/article/assessing-evidencebased-training-in-a-collaborative-virtual-reality-flight-simulator/A64CCC4B6F0EB08B20B358A6C5E12558)); scope `unspecified`. Checked 2026-10-30. #### 033. Extended reality (xR) flight simulators as an adjunct to traditional flight training methods: a scoping review Glen Ross; Andrew Gilbey (2023). *CEAS Aeronautical Journal*. [Article / DOI](https://doi.org/10.1007/s13272-023-00688-5). **Evidence:** Scoping review. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Maps XR as an adjunct to established flight training. Helps schools distinguish promising applications from questions still requiring research. **Interpretation:** A scoping review maps a field; it does not establish a pooled estimate of effectiveness. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s13272-023-00688-5?error=cookies_not_supported&code=bfab9057-5aa8-4870-9110-ee83919db79c)); scope `vor`. Checked 2026-10-30. #### 034. Simulators in the process of flight training Rurak Adam (2017). *Safety & Defense*. [Article / DOI](https://doi.org/10.37105/sd.10). **Evidence:** Practice overview. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Introduces simulators within initial and continuing aviation training. Useful as an accessible bridge from training objectives to simulation practice. **Interpretation:** A descriptive overview rather than a controlled comparison of training approaches. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://sd-magazine.eu/index.php/sd/article/view/10)); scope `unspecified`. Checked 2026-10-30. #### 035. Ecological and Cost Advantage from the Implementation of Flight Simulation Training Devices for Pilot Training Marta Maciejewska; Paula Kurzawska-Pietrowicz; Marta Galant-Gołębiewska; Michał Gołębiewski; Remigiusz Jasiński (2024). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app14188401). **Evidence:** Cost and environmental case study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Models costs and emissions associated with greater simulator use in integrated pilot training. Supports school-level planning alongside learning requirements. **Interpretation:** Benefits depend on curriculum assumptions and electricity sources; they do not establish allowable training credit. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/14/18/8401)); scope `vor`. Checked 2026-10-30. #### 036. Flight simulator with hardware and virtual reality for aeronautical engineering education in higher education David Santiago Velásquez Cifuentes; Moisés De Jesús Ibarra García; Gabriela Mendoza; Kevin A. Peña (2026). *Discover Education*. [Article / DOI](https://doi.org/10.1007/s44217-026-01702-8). **Evidence:** Quasi-experimental education study. **Scope:** Adjacent: engineering education. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0). Compares VR-enhanced and desktop flight simulation in aeronautical engineering education. Useful for connecting theoretical concepts to interactive practice. **Interpretation:** Engineering students and course outcomes differ from licensed-pilot competency requirements. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s44217-026-01702-8?error=cookies_not_supported&code=38eefce0-ace9-4a15-aaf3-1da082f42da0)); scope `vor`. Checked 2026-10-30. #### 037. Usefulness of flight simulator as a part of military pilots training – case study: Grob G 115E Kalle Saastamoinen; Kasper Maunula (2021). *Procedia Computer Science*. [Article / DOI](https://doi.org/10.1016/j.procs.2021.08.171). **Evidence:** Conference case study. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Examines a Grob G 115E simulator in elementary military pilot training. Provides a concrete implementation example for an academy-level discussion. **Interpretation:** Findings from one aircraft and training context should not be generalized without comparison. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.procs.2021.08.171)); scope `vor`. Checked 2026-10-30. #### 038. Efficacy of Low-Cost PC-Based Aviation Training Devices Savern l Reweti; Andrew Gilbey; Lynn Jeffrey (2017). *Journal of Information Technology Education: Research*. [Article / DOI](https://doi.org/10.28945/3682). **Evidence:** Quasi-transfer study. **Scope:** Flight training. **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Compares a low-cost PC training device with a higher-cost FTD for a VFR procedure. Directly relevant to flight-school investment and lesson design. **Interpretation:** Transfer was assessed in an FTD rather than an aircraft; equivalent study results do not establish universal equivalence. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.28945%2F3682)); scope `unspecified`. Checked 2026-10-30. ### S2 Dynamics and simulator fidelity #### 039. Switched Motion Cueing Algorithm for Flight Simulator Upset Prevention and Recovery Training Dongsu Wu; Peng Li; Zesheng Zhang (2020). *IOP Conference Series: Materials Science and Engineering*. [Article / DOI](https://doi.org/10.1088/1757-899x/790/1/012162). **Evidence:** Conference engineering study. **Scope:** Flight simulation engineering. **License:** [CC BY 3.0](http://creativecommons.org/licenses/by/3.0/). Investigates motion cueing for upset-prevention and recovery simulation. Useful for explaining how platform limits affect the motion a trainee experiences. **Interpretation:** Algorithm simulation results do not establish training transfer or compliance for a particular device. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1088%2F1757-899x%2F790%2F1%2F012162)); scope `vor`. Checked 2026-10-30. #### 040. Design and Implementation of a New Training Flight Simulator System Ming-Yen Wei; Shen-An Fang; Ji-Wei Liu (2022). *Sensors*. [Article / DOI](https://doi.org/10.3390/s22207933). **Evidence:** System development and demonstration. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Describes an integrated simulator with motion cueing, controls, monitoring and recorded data. Useful for understanding how a training world is assembled. **Interpretation:** Demonstrated system operation is distinct from measured learner transfer or certification. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1424-8220/22/20/7933)); scope `vor`. Checked 2026-10-30. #### 041. Model Predictive Control Based Washout Algorithm Design for Flight Simulator Upset Prevention and Recovery Training Yu Tong; Haoyun Zhou; Zhao Wu; Qifu Li; Bei Lu (2023). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace10100886). **Evidence:** Engineering simulation study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines predictive motion cueing for upset-recovery scenarios under platform limits. Supports the simulator layer's treatment of dynamics and perceived consequences. **Interpretation:** Improved modeled cueing does not establish improved pilot recovery performance in actual flight. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/10/10/886)); scope `vor`. Checked 2026-10-30. #### 042. Research on Objective Evaluation Method of Flight Simulator Fidelity Guo-Chang Zhao; Wen-Zao Zhou; Hang-Hang Du (2020). *Journal of Physics: Conference Series*. [Article / DOI](https://doi.org/10.1088/1742-6596/1601/6/062004). **Evidence:** Conference methods article. **Scope:** Flight simulation engineering. **License:** [CC BY 3.0](http://creativecommons.org/licenses/by/3.0/). Proposes an objective method for evaluating flight-simulator fidelity. Useful for asking how a school can test that a simulated response represents the target aircraft. **Interpretation:** A fidelity metric is not automatically a measure of learning effectiveness. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1088%2F1742-6596%2F1601%2F6%2F062004)); scope `vor`. Checked 2026-10-30. #### 043. The Use of Computational Algorithms to Display the Aircraft Instruments That Work with Gyroscopic and Magnetic Physics (Theory for Programming an Elementary Flight Simulator) Adan Ramirez-Lopez (2024). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app14167099). **Evidence:** Computational development study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Explains the simulation of gyroscopic and magnetic flight instruments. Connects the rendered panel to the physical behavior trainees are expected to understand. **Interpretation:** An elementary simulator implementation does not establish instrument-training qualification. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/14/16/7099)); scope `vor`. Checked 2026-10-30. #### 044. Model, Control, and Realistic Visual 3D Simulation of VTOL Fixed-Wing Transition Flight Considering Ground Effect Erwhin Irmawan; Agus Harjoko; Andi Dharmawan (2023). *Drones*. [Article / DOI](https://doi.org/10.3390/drones7050330). **Evidence:** Modeling and control study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Models VTOL transition with ground effect in a visual simulation. Useful as an engineering example of why visual scenery and flight dynamics need separate validation. **Interpretation:** A vehicle-control study, not an evaluation of pilot learning or a flight-school best practice. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2504-446X/7/5/330)); scope `vor`. Checked 2026-10-30. #### 045. Dynamic parameters identification of a haptic interface for a helicopter flight simulator Dingxuan Zhao; Jianyao Zhang; Giuseppe Carbone; Haojie Yang; Tao Ni; Shuangji Yao (2020). *Mechanical Sciences*. [Article / DOI](https://doi.org/10.5194/ms-11-193-2020). **Evidence:** Haptic-interface validation. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Studies dynamic parameter identification for helicopter control-force feedback. Useful for explaining how control feel contributes to simulator fidelity. **Interpretation:** Hardware and model validation do not establish transfer of training. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://ms.copernicus.org/articles/11/193/2020/)); scope `unspecified`. Checked 2026-10-30. #### 046. Flight Simulator with a Wake Vortex Regime I G Golovnev; V V Vyshinsky; K V Lapshin; R Yu Zimin; D S Lyubaeva (2019). *IOP Conference Series: Materials Science and Engineering*. [Article / DOI](https://doi.org/10.1088/1757-899x/476/1/012012). **Evidence:** Conference engineering article. **Scope:** Flight simulation engineering. **License:** [CC BY 3.0](http://creativecommons.org/licenses/by/3.0/). Examines a simulator with a wake-vortex regime. Supports scenario design in which the environment changes aircraft behavior, rather than only the visual scene. **Interpretation:** Included for technical relevance; inspect model validity and training limits before reuse. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1088%2F1757-899x%2F476%2F1%2F012012)); scope `vor`. Checked 2026-10-30. #### 047. Review of flight simulation fidelity requirements to help reduce ‘rotorcraft loss of control in-flight’ accident rates Mark D. White; Gareth D. Padfield; Linghai Lu; Sunjoo Advani; Mario Potter (2021). *CEAS Aeronautical Journal*. [Article / DOI](https://doi.org/10.1007/s13272-021-00542-6). **Evidence:** Review. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Reviews rotorcraft simulation fidelity and loss-of-control training needs. A strong anchor for matching model and motion fidelity to difficult training objectives. **Interpretation:** Its standards discussion reflects the publication period; current qualification requirements need separate checking. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s13272-021-00542-6?error=cookies_not_supported&code=cf561a9b-32c3-4479-9f88-e5d3be0b09c3)); scope `vor`. Checked 2026-10-30. ### S3 Training infrastructure and scenarios #### 048. Data Processing in the Pilot Training Process on the Integrated Aircraft Simulator Yurii Hryshchenko; Maksym Zaliskyi; Svitlana Pavlova; Oleksandr Solomentsev; Tatiana Fursenko (2021). *Electrical, Control and Communication Engineering*. [Article / DOI](https://doi.org/10.2478/ecce-2021-0008). **Evidence:** Data-processing methods study. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0). Develops algorithms for analyzing control behavior during simulator training. Useful for turning recorded flights into structured feedback on piloting quality. **Interpretation:** Automated indicators need validation against instructor judgment and the intended training task. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://ecce-journals.rtu.lv/ecce/article/view/ecce-2021-0008)); scope `unspecified`. Checked 2026-10-30. #### 049. FlightTwin: A Generalized Digital Twin Accompanying Flight Framework for Fixed-Wing Aircraft Wei Zhang; Jingli Deng; Xiaodong Liu; Jing Lu (2024). *IEEE Access*. [Article / DOI](https://doi.org/10.1109/access.2024.3454197). **Evidence:** Digital-twin framework. **Scope:** Flight simulation engineering. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/). Introduces a fixed-wing digital-twin framework. Supports discussion of connecting simulated states with aircraft data and testing model consistency. **Interpretation:** Included as supporting technology; it is not evidence that a digital twin improves trainee outcomes. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1109%2Faccess.2024.3454197)); scope `vor`. Checked 2026-10-30. #### 050. Development of a Simulator System Enabling Flight Data Recording and Post-Flight Analysis for Trainee Pilots: A Proof of Concept Ugur Ozdemir; Tamer Savas (2026). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace13020149). **Evidence:** Proof of concept. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Describes logging, replay and post-flight visualization for trainee pilots. A practical reference for debriefing infrastructure and traceable training records. **Interpretation:** The demonstration is illustrative, with no control condition or repeated-trial benchmark. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/13/2/149)); scope `vor`. Checked 2026-10-30. #### 051. HIL Flight Simulator for VTOL-UAV Pilot Training Using X-Plane Daniel Aláez; Xabier Olaz; Manuel Prieto; Pablo Porcellinis; Jesús Villadangos (2022). *Information*. [Article / DOI](https://doi.org/10.3390/info13120585). **Evidence:** Hardware-in-the-loop system study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Integrates a flight controller, VTOL aircraft model and weather into X-Plane. Useful for UAV schools considering realistic control and environmental responses. **Interpretation:** Reported deployment is not a controlled demonstration of better training outcomes. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2078-2489/13/12/585)); scope `vor`. Checked 2026-10-30. #### 052. CPS prototype development for AI-based scenario adaptation in flight simulator training H. Lilla; O. Niggemann; T. Netzel (2025). *CEAS Aeronautical Journal*. [Article / DOI](https://doi.org/10.1007/s13272-025-00870-x). **Evidence:** Adaptive-training prototype. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Connects knowledge work, performance data and scenario adaptation in a cyber-physical training system. Useful for the transition from fixed lessons to adaptive practice. **Interpretation:** The presented prototype uses heuristic scoring; the proposed machine-learning extension is future work. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s13272-025-00870-x?error=cookies_not_supported&code=685324ca-f65a-4a32-9855-cbcdcb5c31de)); scope `vor`. Checked 2026-10-30. #### 053. Multi-User Drone Flight Training in Mixed Reality Yong-Guk Go; Ho-San Kang; Jong-Won Lee; Mun-Su Yu; Soo-Mi Choi (2021). *Electronics*. [Article / DOI](https://doi.org/10.3390/electronics10202521). **Evidence:** Mixed-reality training system. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Proposes remote expert guidance for novice FPV drone pilots. Useful for examining shared worlds, instructor presence and multi-user practice. **Interpretation:** A system proposal does not establish long-term retention or safer real-world operations. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2079-9292/10/20/2521)); scope `vor`. Checked 2026-10-30. ### A1 Curriculum, instructors and assessment #### 054. Flight Training Subject Identification Method Based on Multivariate Subsequence Search With Double Windows Jing Lu; Jingli Deng; Zhou Ren; Yu Shi (2023). *IEEE Access*. [Article / DOI](https://doi.org/10.1109/access.2022.3232808). **Evidence:** Automated identification method. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode). Addresses identifying training subjects from flight time-series data. Useful for organizing flight records before assessment and targeted debriefing. **Interpretation:** Task identification is a supporting capability, not a complete assessment of pilot competence. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1109%2Faccess.2022.3232808)); scope `vor`. Checked 2026-10-30. #### 055. New approach to VFR pilot training Kristýna Vodičková (2018). *MAD - Magazine of Aviation Development*. [Article / DOI](https://doi.org/10.14311/mad.2018.01.01). **Evidence:** Curriculum development article. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0). Describes changes to VFR teaching materials for GNSS use. Useful for the academy's process of keeping ground instruction aligned with cockpit technology. **Interpretation:** The curriculum context is historical; current procedures and equipment guidance must be checked separately. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.14311%2Fmad.2018.01.01)); scope `unspecified`. Checked 2026-10-30. #### 056. Wind Shear Operation-Based Competency Assessment Model for Civil Aviation Pilots Fan Li; Xuezhi Xu; Jiayuan Li; Huiyun Hu; Mingda Zhao; Hong Sun (2024). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace11050363). **Evidence:** Competency-model study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Develops observable behavior indicators for wind-shear scenarios. Useful for showing how a training objective becomes a structured assessment rubric. **Interpretation:** Agreement with examiners in the evaluated data requires confirmation in other cohorts and scenarios. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/11/5/363)); scope `vor`. Checked 2026-10-30. #### 057. Competency-based assessment of pilots’ manual flight performance during instrument flight training Hong Sun; Xin Zhou; Peiwen Zhang; Xin Liu; Yunsong Lu; Hong Huang; Wuyang Song (2023). *Cognition, Technology & Work*. [Article / DOI](https://doi.org/10.1007/s10111-023-00737-3). **Evidence:** Competency-assessment methods study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Links instrument-flight behaviors to quantitative assessment indicators. Useful for documenting what a trainee should demonstrate and how performance is judged. **Interpretation:** High agreement with experts in the tested procedure requires validation across other tasks. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s10111-023-00737-3?error=cookies_not_supported&code=74b98164-2108-442c-9c90-c87f5d0c65f7)); scope `vor`. Checked 2026-10-30. #### 058. Implementation of the digital training concept in the basic flight training in the Serbian Military Academy Slaviša Vlačić; Aleksandar Knežević; Vladimir Grbović; Panos Vitsas; Mihajlo Mihajlovic (2022). *Vojnotehnicki glasnik*. [Article / DOI](https://doi.org/10.5937/vojtehg70-34610). **Evidence:** Academy implementation review. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/BY/4.0). Describes digital training in basic military flight education. Useful for curriculum transitions when aircraft and cockpit systems become more digitized. **Interpretation:** A national academy example provides context rather than a controlled comparison applicable to every school. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://scindeks.ceon.rs/Article.aspx?artid=0042-84692201087V)); scope `unspecified`. Checked 2026-10-30. #### 059. The Effect of Studying Flight Training Materials utilizing Encoding Techniques on Situational Awareness Capabilities of Students in PPL Training Jeong Yoon Moon; Jang Ryong Lee (2020). *Journal of the Korean Society for Aviation and Aeronautics*. [Article / DOI](https://doi.org/10.12985/ksaa.2020.28.4.154). **Evidence:** Instructional-materials study. **Scope:** Flight training. **License:** [CC BY-NC 4.0](http://creativecommons.org/licenses/by-nc/4.0). Examines encoding techniques in PPL study materials and situation awareness. Supports connecting preflight knowledge preparation with simulator and cockpit tasks. **Interpretation:** The instructional and assessment context should be reviewed before adapting materials. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://www.jksaa.org/archive/view_article?pid=jksaa-28-4-154)); scope `Publisher article license (Crossref: tdm)`. Checked 2026-10-30. #### 060. Assessment of the psychomotor fitness level of cadet pilots after a 6-month flight training period - pilot study Zbigniew Wochyński; Justyna Skrzyńska-Rękawek; Zdzisław Kobos (2023). *Frontiers in Psychology*. [Article / DOI](https://doi.org/10.3389/fpsyg.2023.1205412). **Evidence:** Longitudinal pilot study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Examines psychomotor and cognitive measures before and after a period of cadet flight practice. Useful for treating development as longitudinal rather than a single score. **Interpretation:** Twenty male cadets and a before–after design limit causal and population-level conclusions. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1205412/full)); scope `vor`. Checked 2026-10-30. #### 061. Probabilistic Modeling of Variation in Pilot Performance During Flight Training Kento Yamada; Harumi Ikeshita; Yuta Kyoya; Makoto Ueno (2024). *IEEE Transactions on Reliability*. [Article / DOI](https://doi.org/10.1109/tr.2023.3294021). **Evidence:** Probabilistic modeling study. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/). Models variation in training performance. Useful for interpreting inconsistency across flights and avoiding overreliance on one successful trial. **Interpretation:** A statistical model requires local calibration and does not replace instructor assessment. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1109%2Ftr.2023.3294021)); scope `vor`. Checked 2026-10-30. #### 062. The effect of working memory training on situation awareness in a flight simulator Yue Zhou; Di Wu; Chaoxian Wang; Kewei Sun; Pengbo Xu; Ziwei Wang; Wei Xiao (2022). *Cognition, Technology & Work*. [Article / DOI](https://doi.org/10.1007/s10111-022-00707-1). **Evidence:** Randomized training study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Tests working-memory training with situation-awareness measures in a flight simulator. Useful for distinguishing underlying cognitive skills from task-specific practice. **Interpretation:** A small study and short training interval leave long-term and operational transfer unresolved. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s10111-022-00707-1?error=cookies_not_supported&code=f79a5211-8457-44a2-b698-d7be71b707b8)); scope `vor`. Checked 2026-10-30. #### 063. The interconnection between evaluated and self-assessed performance in full flight simulator training Ari Tuhkala; Ville Heilala; Joni Lämsä; Arto Helovuo; Ilkka Tynkkynen; Emilia Lampi; Katriina Sipiläinen; Raija Hämäläinen; Tommi Kärkkäinen (2023). *Vocations and Learning*. [Article / DOI](https://doi.org/10.1007/s12186-023-09339-6). **Evidence:** Evaluation and self-assessment study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Compares instructor evaluations with pilots' reflections after simulator events. Supports debriefs that examine risk appraisal and overconfidence alongside observable competencies. **Interpretation:** The sample comprises sixteen pilots; reflection differences are not a universal novice–expert rule. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s12186-023-09339-6?error=cookies_not_supported&code=9027f062-4eab-44e1-b018-b0b02c5c399f)); scope `vor`. Checked 2026-10-30. #### 064. Behavioral Indicator-Based Initial Flight Training Competency Assessment Model Hong Sun; Fangquan Yang; Peiwen Zhang; Qingqing Hu (2023). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app13106346). **Evidence:** Competency-assessment methods study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Builds behavioral indicators and assessment matrices for initial flight training. Useful for making the academy's expectations observable and consistently assessable. **Interpretation:** Agreement with instructor ratings in the reported data does not eliminate the need for local validation. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/13/10/6346)); scope `vor`. Checked 2026-10-30. #### 065. Identifying Behavior Indicators for Flight Instructor Competencies and Training Needs Analysis for General Aviation Flight Instructor Keumseok Kang; Hyungjoon Park; Kyeong Tae Kim (2023). *Journal of the Korean Society for Aviation and Aeronautics*. [Article / DOI](https://doi.org/10.12985/ksaa.2023.31.1.068). **Evidence:** Competency and training-needs study. **Scope:** Flight training. **License:** [CC BY-NC 4.0](http://creativecommons.org/licenses/by-nc/4.0). Examines instructor behavior indicators and training needs. A direct anchor for developing teaching, interaction and learning-environment competencies in flight instructors. **Interpretation:** Survey-based priorities from one national setting need local validation. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.jksaa.org/archive/view_article?pid=jksaa-31-1-68)); scope `Publisher article license (Crossref: tdm)`. Checked 2026-10-30. #### 066. Who trains the trainers? Gamification of flight instructor learning in evidence-based training scenarios Rubén Dapica; Alfredo Hernández; Federico Peinado (2022). *Entertainment Computing*. [Article / DOI](https://doi.org/10.1016/j.entcom.2022.100510). **Evidence:** Instructor-training design article. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Presents gamification of instructor learning in evidence-based scenarios. Useful for treating the instructor as a learner and a critical actor in the training system. **Interpretation:** Included for design relevance; effectiveness and transfer should be evaluated from the complete article. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.entcom.2022.100510)); scope `vor`. Checked 2026-10-30. ### A2 Workload and adaptive learning #### 067. Classification of Electrophysiological Signatures With Explainable Artificial Intelligence: The Case of Alarm Detection in Flight Simulator Eva Massé; Olivier Bartheye; Ludovic Fabre (2022). *Frontiers in Neuroinformatics*. [Article / DOI](https://doi.org/10.3389/fninf.2022.904301). **Evidence:** EEG classification study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Studies missed alarms under load and fatigue using explainable classification. Useful for training discussions about attention limits and feedback timing. **Interpretation:** Classifier performance in the experiment does not establish a reliable operational alerting system. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2022.904301/full)); scope `vor`. Checked 2026-10-30. #### 068. Detection of Pilot’s Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks Chenglin Liu; Chenyang Zhang; Luohao Sun; Kun Liu; Haiyue Liu; Wenbing Zhu; Chaozhe Jiang (2023). *Entropy*. [Article / DOI](https://doi.org/10.3390/e25071035). **Evidence:** EEG classification study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Tests wireless EEG workload classification during airfield-pattern tasks. Useful for discussing objective workload signals alongside self-report and flight performance. **Interpretation:** Within-study accuracy does not guarantee generalization to new pilots or real-flight conditions. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1099-4300/25/7/1035)); scope `vor`. Checked 2026-10-30. #### 069. Visualizing Student Pilot Mental Workload in Real Flight Training Using Wearable Heart Rate Monitoring Hana Procházková; Jaroslav Juračka (2026). *Transportation Research Procedia*. [Article / DOI](https://doi.org/10.1016/j.trpro.2026.01.012). **Evidence:** Conference article. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Addresses wearable heart-rate monitoring during real student flight training. Helps connect simulator workload discussions to measurements outside the simulator. **Interpretation:** Heart-rate variation has several causes; the title-level inclusion is not an endorsement of a workload threshold. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.trpro.2026.01.012)); scope `vor`. Checked 2026-10-30. #### 070. Examining flight time, cognitive reflection, workload, stress and metacognition on decision making performance for pilots during flight simulation Aoife Mohan; Boban Simonovic; Katia C. Vione; Edward Stupple (2024). *Ergonomics*. [Article / DOI](https://doi.org/10.1080/00140139.2024.2404642). **Evidence:** Simulator decision-making study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Examines flight experience, reflection, workload, stress and metacognition together. Useful for discussing why hours alone may not describe decision-making performance. **Interpretation:** Included for subject relevance; specific relationships require appraisal of the complete analysis. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1080%2F00140139.2024.2404642)); scope `vor`. Checked 2026-10-30. #### 071. Analysis of Human Factors in Typical Accident Tests of Certain Type Flight Simulator Guanghua Xing; Yingjun Sun; Fajiang He; Pengcheng Wei; Shicheng Wu; Haojie Ren; Zhixiong Chen (2023). *Sustainability*. [Article / DOI](https://doi.org/10.3390/su15032791). **Evidence:** Simulator human-factors assessment. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Combines subjective and physiological measures in fault scenarios. Useful for choosing complementary measures when assessing how trainees handle abnormal events. **Interpretation:** A combined measurement approach still needs validation for the aircraft, cohort and intended decision. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2071-1050/15/3/2791)); scope `vor`. Checked 2026-10-30. #### 072. Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight Hamed Taheri Gorji; Nicholas Wilson; Jessica VanBree; Bradley Hoffmann; Thomas Petros; Kouhyar Tavakolian (2023). *Scientific Reports*. [Article / DOI](https://doi.org/10.1038/s41598-023-29647-0). **Evidence:** Live-flight EEG study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Collects workload-related EEG during an instrument-training flight profile. Adds real-flight evidence to a literature otherwise dominated by simulators. **Interpretation:** Ten pilots and model-validation choices constrain generalization to other pilots and aircraft. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.nature.com/articles/s41598-023-29647-0?error=cookies_not_supported&code=0d6e8bce-e25a-4ac0-904d-16038c56397f)); scope `vor`. Checked 2026-10-30. #### 073. Multimodal workload assessment during simulator-based instrument flight training Jiri Ulvr; Petr Michenka; Vladimir Smrz; Ondrej Mach; Pavlina Engelova; Rostislav Pavlicek (2026). *Frontiers in Psychology*. [Article / DOI](https://doi.org/10.3389/fpsyg.2026.1898551). **Evidence:** Pilot feasibility study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Combines workload reports, heart rate and flight parameters during instrument approaches. Useful for debriefing differences between experienced effort and observed performance. **Interpretation:** The composite indices are exploratory and need validation in larger populations. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.3389%2Ffpsyg.2026.1898551)); scope `vor`. Checked 2026-10-30. #### 074. Simulating Flight Crew Workload Settings to Mitigate Fatigue Risk in Flight Operations Dajana Bartulović; Sanja Steiner; Dario Fakleš; Martina Mavrin Jeličić (2023). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace10100904). **Evidence:** Fatigue modeling study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Connects duty patterns and fatigue measures through operational modeling. Useful as background for considering workload across schedules as well as individual lessons. **Interpretation:** The four-pilot sample and modeled schedule changes do not establish general fatigue-management policy. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/10/10/904)); scope `vor`. Checked 2026-10-30. #### 075. Cognitive load estimation in VR flight simulator Archana Hebbar; Sanjana Vinod; Shah, A.K.; Abhay Pashilkar; Pradipta Biswas (2023). *Journal of Eye Movement Research*. [Article / DOI](https://doi.org/10.16910/jemr.15.3.11). **Evidence:** Measurement and simulator-development study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Combines ocular and EEG measures in VR flight tasks. Useful for observing how workload changes as scenarios and simulated actors become more demanding. **Interpretation:** Associations with task difficulty do not make the measures a stand-alone competency or fitness decision. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/1995-8692/15/3/23)); scope `unspecified`. Checked 2026-10-30. #### 076. Real-Time State Estimation in a Flight Simulator Using fNIRS Thibault Gateau; Gautier Durantin; Francois Lancelot; Sebastien Scannella; Frederic Dehais (2015). *PLOS ONE*. [Article / DOI](https://doi.org/10.1371/journal.pone.0121279). **Evidence:** Real-time neuroergonomic study. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Tests fNIRS-based state estimation during simulated flight and recall of controller instructions. Useful for explaining what workload sensors can and cannot observe. **Interpretation:** Experimental classification should not be equated with comprehensive understanding of a learner's competence. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0121279)); scope `vor`. Checked 2026-10-30. #### 077. Neuroadaptive Training via fNIRS in Flight Simulators Jesse A. Mark; Amanda E. Kraft; Matthias D. Ziegler; Hasan Ayaz (2022). *Frontiers in Neuroergonomics*. [Article / DOI](https://doi.org/10.3389/fnrgo.2022.820523). **Evidence:** Neuroadaptive training experiment. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Compares performance-based progression with progression informed by fNIRS workload. A central example of adapting task difficulty to the learner. **Interpretation:** Short-term gains on studied tasks require confirmation for retention, real-flight transfer and practical deployment. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2022.820523/full)); scope `vor`. Checked 2026-10-30. #### 078. Flight emotions unleashed: Navigating training phases and difficulty levels in simulated flying Alejandra Ruiz‐Segura; Andrew Law; Sion Jennings; Alain Bourgon; Ethan Churchill; Susanne Lajoie (2024). *Journal of Computer Assisted Learning*. [Article / DOI](https://doi.org/10.1111/jcal.13037). **Evidence:** Simulator learning study. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Tracks performance and emotional responses across task difficulty and learning phases. Useful for designing challenge progression and reflective debriefs. **Interpretation:** Facial-expression and arousal interpretations are context dependent and should not be treated as direct measures of understanding. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1111%2Fjcal.13037)); scope `vor`. Checked 2026-10-30. ### A3 Safety, uncertainty and startle #### 079. Flight Training Risk Identification and Assessment Based on the HHM-RFRM Model Hong Sun; Fangquan Yang; Peiwen Zhang; Yunxiang Zhao (2023). *Sustainability*. [Article / DOI](https://doi.org/10.3390/su15021693). **Evidence:** Risk-assessment methods study. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Builds a risk-identification and assessment framework for flight schools. Supports planning lessons around interacting hazards rather than isolated checklists. **Interpretation:** Model outputs depend on expert judgments, selected factors and the local training context. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2071-1050/15/2/1693)); scope `vor`. Checked 2026-10-30. #### 080. Aviation safety training methodology Krasin Georgiev (2021). *Heliyon*. [Article / DOI](https://doi.org/10.1016/j.heliyon.2021.e08511). **Evidence:** Training-methodology article. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Provides a source for aviation safety-training design. Use it when developing the volume's connection between safety objectives and instructional methods. **Interpretation:** Included for subject relevance; the collection does not independently validate all proposed methods. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.heliyon.2021.e08511)); scope `vor`. Checked 2026-10-30. #### 081. Recovery from startle and surprise: A survey of airline pilots' operational experience using a startle and surprise management method Daan Vlaskamp; Annemarie Landman; Jeroen van Rooij; James Blundell (2025). *International Journal of Industrial Ergonomics*. [Article / DOI](https://doi.org/10.1016/j.ergon.2025.103733). **Evidence:** Operational-experience survey. **Scope:** Aviation human factors. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Examines pilots' experience using a startle-and-surprise management method. Complements laboratory studies with an operational perspective on usability. **Interpretation:** Self-reported experience is not equivalent to controlled evidence of reduced accident risk. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.ergon.2025.103733)); scope `vor`. Checked 2026-10-30. #### 082. An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data Xiuyi Li; Yu Qian; Hongnian Chen; Linjiang Zheng; Qixing Wang; Jiaxing Shang (2022). *Applied Sciences*. [Article / DOI](https://doi.org/10.3390/app122412789). **Evidence:** Flight-data clustering study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Analyzes patterns of unsafe control inputs in recorded flights. Shows how operational data can help identify topics for targeted recurrent training. **Interpretation:** Clustering supports hypotheses and targeting; it does not demonstrate that a proposed intervention works. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2076-3417/12/24/12789)); scope `vor`. Checked 2026-10-30. #### 083. A narrative review of the interconnection between pilot acute stress, startle, and surprise effects in the aviation context: Contribution of physiological measurements Moussa Diarra; Mauro Marchitto; Marie-Christine Bressolle; Thierry Baccino; Véronique Drai-Zerbib (2023). *Frontiers in Neuroergonomics*. [Article / DOI](https://doi.org/10.3389/fnrgo.2023.1059476). **Evidence:** Narrative review. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Separates stress, startle and surprise and considers physiological measurement. A useful conceptual foundation for designing unexpected-event training. **Interpretation:** A narrative synthesis provides framing, not a universal measurement or intervention standard. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.frontiersin.org/journals/neuroergonomics/articles/10.3389/fnrgo.2023.1059476/full)); scope `vor`. Checked 2026-10-30. #### 084. Conceptual Models of Startle, Surprise and Automation Bias Analyzed Through Recent Aviation Accident Reports Karen Cristiane Gardini; Fernando Holanda Chiavelli; José Ricardo Silva Scarpari; Donizeti de Andrade (2022). *Journal of Aerospace Technology and Management*. [Article / DOI](https://doi.org/10.1590/jatm.v14.1282). **Evidence:** Conceptual accident analysis. **Scope:** Aviation human factors. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Applies models of startle, surprise and automation bias to accident reports. Useful for building reflective scenarios around how understanding breaks down. **Interpretation:** Retrospective conceptual analysis cannot establish the causal effectiveness of a training intervention. **Screening:** Publisher description screened. **License evidence:** Article page + publisher metadata ([record](http://www.scielo.br/j/jatm/a/dXJgk9fhFytw6tGh5KFZ4HR/?lang=en)); scope `vor`. Checked 2026-10-30. #### 085. Preliminary Multilevel Confirmatory Factor Analysis of the Startle and Surprise Inventories using Simulated Flight Scenarios Jiayu Chen; Annemarie Landman; Alexis Derumigny; Olaf Stroosma; M. M. (René) van Paassen; Max Mulder (2025). *Journal of Cognitive Engineering and Decision Making*. [Article / DOI](https://doi.org/10.1177/15553434251401784). **Evidence:** Psychometric validation study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Tests whether startle and surprise questionnaires measure distinct constructs in flight scenarios. Useful for choosing measures before evaluating an intervention. **Interpretation:** Repeated scenario observations from 26 pilots are not independent pilots; this and related studies may share participants. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1177%2F15553434251401784)); scope `vor`. Checked 2026-10-30. #### 086. Startle and surprise in helicopter operations: reported prevalence and application of mitigation strategies Daan Vlaskamp; Alex Pollitt; James Blundell; Annemarie Landman; Eric L. Groen; M.M. René van Paassen; Olaf Stroosma; Max Mulder (2025). *Cognition, Technology & Work*. [Article / DOI](https://doi.org/10.1007/s10111-025-00811-y). **Evidence:** Helicopter-pilot survey. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Documents reported startle events, training exposure and mitigation preferences. Useful for identifying recurrent-training needs in helicopter operations. **Interpretation:** Perceived usefulness and willingness to adopt a method do not demonstrate effectiveness. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s10111-025-00811-y?error=cookies_not_supported&code=57d76e7b-dfe7-4741-92b2-89adac6bb051)); scope `vor`. Checked 2026-10-30. #### 087. Relationships Between Pilots’ Startle and Surprise Responses and Information-Processing Performance During Simulated In-Flight Events Jiayu Chen; Annemarie Landman; Alexis Derumigny; Olaf Stroosma; M. M. (René) van Paassen; Max Mulder (2026). *Human Factors: The Journal of the Human Factors and Ergonomics Society*. [Article / DOI](https://doi.org/10.1177/00187208261434426). **Evidence:** Simulator experimental study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Separates the effects of startle and surprise on information processing. Useful for designing scenarios that challenge cognition without conflating different reactions. **Interpretation:** The particular secondary task and repeated scenarios constrain interpretation and may overlap with related publications' samples. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1177%2F00187208261434426)); scope `vor`. Checked 2026-10-30. #### 088. The Effectiveness of a Mnemonic-Type Startle and Surprise Management Procedure for Pilots Annemarie Landman; Sophie H. van Middelaar; Eric L. Groen; M. M. (René) van Paassen; Adelbert W. Bronkhorst; Max Mulder (2020). *The International Journal of Aerospace Psychology*. [Article / DOI](https://doi.org/10.1080/24721840.2020.1763798). **Evidence:** Training-procedure evaluation. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Evaluates a mnemonic approach to startle and surprise management. Supports comparison of structured recovery aids with broader scenario-based training. **Interpretation:** Specific effectiveness claims require full appraisal and alignment with approved operating procedures. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1080%2F24721840.2020.1763798)); scope `unspecified`. Checked 2026-10-30. #### 089. Prediction of Pilot Performance During Startle Events Based on Neuropsychophysiological Features of Stress Resilience and Cognitive Task Scores Mate Gambiraža; Siniša Popović; Matej Kurtak; Tanja Jovanovic; Seth Norrholm; Krešimir Ćosić (2025). *IEEE Access*. [Article / DOI](https://doi.org/10.1109/access.2025.3594493). **Evidence:** Predictive-model study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode). Examines physiological and cognitive predictors of performance during startle events. Useful for discussing individualized preparation and measurement uncertainty. **Interpretation:** Prediction does not establish causation or justify using the model for personnel selection. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1109%2Faccess.2025.3594493)); scope `vor`. Checked 2026-10-30. #### 090. The Influence of Surprise on Upset Recovery Performance in Airline Pilots Annemarie Landman; Eric L. Groen; M. M. (René) van Paassen; Adelbert W. Bronkhorst; Max Mulder (2017). *The International Journal of Aerospace Psychology*. [Article / DOI](https://doi.org/10.1080/10508414.2017.1365610). **Evidence:** Airline-pilot simulator study. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Examines surprise during upset recovery. A relevant anchor for contrasting rehearsed maneuver execution with adaptation to an unexpected event. **Interpretation:** Use the complete study to distinguish surprise effects from general upset-training effectiveness. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1080%2F10508414.2017.1365610)); scope `vor`. Checked 2026-10-30. #### 091. The nature and costs of civil aviation flight training safety occurrences Alexander Somerville; Timothy Lynar; Graham Wild (2023). *Transportation Engineering*. [Article / DOI](https://doi.org/10.1016/j.treng.2023.100182). **Evidence:** Training-safety occurrence analysis. **Scope:** Flight training. **License:** [CC BY-NC-ND 4.0](http://creativecommons.org/licenses/by-nc-nd/4.0/). Examines the nature and costs of safety occurrences in civil flight training. Helps connect school management priorities with prevention and instructional design. **Interpretation:** Occurrence and cost patterns do not establish the effectiveness of a specific safety intervention. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1016%2Fj.treng.2023.100182)); scope `vor`. Checked 2026-10-30. #### 092. Risk Management of Safety for Flight Training in Air Forces Wen-Kai K. Hsu; Ming-Hung Shu; Yu-Che Liu; To-Cheng Wang (2022). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace9100558). **Evidence:** Instructor-informed risk assessment. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Uses instructor input to identify and rank flight-training risks. Provides an example of converting practitioner knowledge into structured risk review. **Interpretation:** Priorities from one airbase are context dependent and should not be imported unchanged. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/9/10/558)); scope `vor`. Checked 2026-10-30. ### A4 Synthetic actors and human–AI teams #### 093. A Virtual Simulation-Pilot Agent for Training of Air Traffic Controllers Juan Zuluaga-Gomez; Amrutha Prasad; Iuliia Nigmatulina; Petr Motlicek; Matthias Kleinert (2023). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace10050490). **Evidence:** Virtual-agent system and benchmarks. **Scope:** Adjacent: ATC training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Builds a virtual pilot that recognizes controller speech and produces readbacks. A concrete actor example for shared aviation scenarios and communication practice. **Interpretation:** Speech benchmarks do not establish improved controller or pilot learning outcomes. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/10/5/490)); scope `vor`. Checked 2026-10-30. #### 094. Enhancing Decision-Making in Single-Pilot Operations Through Proactive and Explainable Human-Autonomy Teaming Charles Dormoy; Turkan Hentati; Theodore Letouzé (2026). *Journal of Cognitive Engineering and Decision Making*. [Article / DOI](https://doi.org/10.1177/15553434261477781). **Evidence:** Human-autonomy simulator experiment. **Scope:** Aviation human factors. **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Compares reactive and proactive AI assistance during a demanding single-pilot scenario. Useful for actor design that makes recommendations understandable and timely. **Interpretation:** Results from 22 pilots in one scenario do not establish safety equivalence for single-pilot airline operations. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1177%2F15553434261477781)); scope `vor`. Checked 2026-10-30. #### 095. Human-Automation Trust Development as a Function of Automation Exposure, Familiarity, and Perceived Risk: A High-Fidelity Remotely Operated Aircraft Simulation Eric T. Chancey; Michael S. Politowicz; Kathryn M. Ballard; James Unverricht; Bill K. Buck; Steven Geuther (2024). *Journal of Cognitive Engineering and Decision Making*. [Article / DOI](https://doi.org/10.1177/15553434241296573). **Evidence:** Repeated-trial trust study. **Scope:** Aviation human factors. **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Examines trust in automated systems during remotely operated aircraft simulation. Supports teaching calibrated reliance and transparent system behavior. **Interpretation:** Six operators form a small sample; reported trust is not an independent safety outcome. **Screening:** Abstract screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1177%2F15553434241296573)); scope `unspecified`. Checked 2026-10-30. #### 096. Principles for ‘intelligent assistant’ systems in future flight deck design: autonomous action integration to reduce pilot workload Declan Saunders; James Blundell; Wen-Chin Li; Peter Beecroft; Linghai Lu; Wojciech Tomasz Korek; Wenbing Shi (2026). *The Aeronautical Journal*. [Article / DOI](https://doi.org/10.1017/aer.2026.10161). **Evidence:** Pilot-in-the-loop design study. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Investigates transparency and interaction modalities for an intelligent flight-deck assistant. Useful for defining the authority and communication of synthetic partners. **Interpretation:** Four professional pilots informed the study; additional audio can also compete with important existing cues. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.cambridge.org/core/journals/aeronautical-journal/article/principles-for-intelligent-assistant-systems-in-future-flight-deck-design-autonomous-action-integration-to-reduce-pilot-workload/F6C5B6B0AB254973B34C62B73B5406C2)); scope `unspecified`. Checked 2026-10-30. #### 097. Designing human-centric intelligent systems in aviation: applications of artificial cognitive systems, AI-enhanced investigations, and immersive eVTOL simulation training Dimitrios Ziakkas; Debra Henneberry; Anastasios Plioutsias (2025). *Human-Intelligent Systems Integration*. [Article / DOI](https://doi.org/10.1007/s42454-025-00083-x). **Evidence:** Conceptual integration framework. **Scope:** Aviation human factors. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0). Connects human-centered AI, investigations, fatigue management and eVTOL training. Useful for the volume's forward-looking discussion of connected aviation systems. **Interpretation:** A proposed integration framework should be distinguished from tested learning or safety outcomes. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://link.springer.com/article/10.1007/s42454-025-00083-x?error=cookies_not_supported&code=41ac2a8d-2465-4806-ad72-9880edda1953)); scope `vor`. Checked 2026-10-30. #### 098. Modeling Air Combat Behavior for Simulation-Based Pilot Training: A Survey of Machine Learning Approaches Andreas Strand; Patrick Ribu Gorton; Karsten Brathen (2025). *IEEE Access*. [Article / DOI](https://doi.org/10.1109/access.2025.3580279). **Evidence:** Machine-learning survey. **Scope:** Flight training. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/legalcode). Surveys machine-learning approaches to synthetic behavior in simulation-based pilot training. Useful for actor requirements and limitations in constructed scenarios. **Interpretation:** Its military context limits direct applicability to civilian flight schools; no tactical implementation is supplied here. **Screening:** Title and bibliographic metadata screened. **License evidence:** Publisher metadata ([record](https://api.crossref.org/works/10.1109%2Faccess.2025.3580279)); scope `vor`. Checked 2026-10-30. #### 099. Aircraft Upset Recovery Strategy and Pilot Assistance System Based on Reinforcement Learning Jin Wang; Peng Zhao; Zhe Zhang; Ting Yue; Hailiang Liu; Lixin Wang (2024). *Aerospace*. [Article / DOI](https://doi.org/10.3390/aerospace11010070). **Evidence:** Simulation and assistance-system study. **Scope:** Flight simulation engineering. **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Explores a learned assistance strategy for aircraft upset recovery. Useful for comparing an autonomous actor's policy with human supervision and training objectives. **Interpretation:** Research-system results are not an approved recovery procedure or authorization to use the system operationally. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.mdpi.com/2226-4310/11/1/70)); scope `vor`. Checked 2026-10-30. #### 100. Design of simulation-based pilot training systems using machine learning agents J. Källström; R. Granlund; F. Heintz (2022). *The Aeronautical Journal*. [Article / DOI](https://doi.org/10.1017/aer.2022.8). **Evidence:** User-centered agent-design study. **Scope:** Flight training. **License:** [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/). Investigates capabilities required of learning agents in simulation-based training. A central actor-layer reading for realistic, useful synthetic participants. **Interpretation:** Domain analysis and experiments do not establish that automated agents can replace instructors across training contexts. **Screening:** Abstract screened. **License evidence:** Article page + publisher metadata ([record](https://www.cambridge.org/core/journals/aeronautical-journal/article/design-of-simulationbased-pilot-training-systems-using-machine-learning-agents/1E7C4AA76B4328524F71A015B9EEF76B)); scope `unspecified`. Checked 2026-10-30.