The growing use of flight-management systems, autopilot, autothrust, and other advanced automation has shifted flightcrew work from continuous manual control toward supervisory monitoring and exception management. This study develops and evaluates an Artificial Intelligence Enabled Human Factors Risk Prediction (AI-HFRP) framework that integrates pilot state, human-automation interaction, flight dynamics, and operational context into an interpretable safety-risk estimate. The supplied proof-of-concept dataset contained 24 realistic synthetic pilot-flight windows spanning four flight profiles and six phases of flight. Human state inputs included Samn-Perelli fatigue, Karolinska Sleepiness Scale, NASA Task Load Index mental demand, Psychomotor Vigilance Task response measures, and Situation Awareness Global Assessment Technique accuracy. Automation and operational inputs included mode transitions, mode awareness, mode confusion, automation surprise, Flight Management System activity, cockpit alerts, flight-path deviations, visibility, crosswind, and air-traffic-control activity. High and Critical expert labels were combined for binary evaluation because only five such observations were available. Flight-grouped four-fold cross-validation was used to limit leakage between observations from the same flight. The proposed interpretable fusion achieved an area under the receiver operating characteristic curve of 0.874, sensitivity of 0.800, specificity of 0.684, and F1-score of 0.533. Benchmark model AUC values ranged from 0.547 to 0.695. Elevated risk was associated with longer duty, greater fatigue and sleepiness, slower vigilance response, reduced situation awareness, automation surprise, and larger flight-path deviations. The results support further evaluation of human-centred predictive safety assistance, but larger operational datasets are required before deployment or accident-reduction claims are justified.

References

[1]S. G. Hart and L. E. Staveland, “Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research,” in Human Mental Workload, P. A. Hancock and N. Meshkati, Eds., vol. 52. Amsterdam, The Netherlands: North-Holland, 1988, pp. 139-183, doi: 10.1016/S0166 4115(08)62386-9.

[2] S. W. Samn and L. P. Perelli, Estimating Aircrew Fatigue: A Technique with Application to Airlift Operations, USAF School of Aerospace Medicine, Brooks AFB, TX, USA, Tech. Rep. SAM-TR-82 21, 1982.

[3] T. Åkerstedt and M. Gillberg, “Subjective and objective sleepiness in the active individual,” Int. J. Neurosci., vol. 52, nos. 1-2, pp. 29-37, 1990. Clin.

[4] K. Kaida et al., “Validation of the Karolinska Sleepiness Scale against performance and EEG variables,” Neurophysiol., vol. 117, no. 7, pp. 1574-1581, 2006, doi: 10.1016/j.clinph.2006.03.011.

[5] M. R. Endsley, “Measurement of situation awareness in dynamic systems,” Human Factors, vol. 37, no. 1, pp. 65-84, 1995, doi: 10.1518/001872095779049499.

[6] R. M. Taylor, “Situational Awareness Rating Technique (SART): The development of a tool for aircrew systems design,” in Situational Awareness in Aerospace Operations, AGARD-CP-478, NATO AGARD, 1990, pp. 3/1-3/17.

[7] M. Basner, D. Mollicone, and D. F. Dinges, “Validity and sensitivity of a brief Psychomotor Vigilance Test (PVT-B) to total and partial sleep deprivation,” Acta Astronaut., vol. 69, nos. 11-12, pp. 949-959, 2011.

[8] J.-Y. Jian, A. M. Bisantz, and C. G. Drury, “Foundations for an empirically determined scale of trust in automated systems,” Int. J. Cogn. Ergon., vol. 4, no. 1, pp. 53-71, 2000, doi: 10.1207/S15327566IJCE0401_04.

[9] Federal Aviation Administration Human Factors Team, The Interfaces Between Flightcrews and Modern Flight Deck Systems. Washington, DC, USA: FAA, Jun. 18, 1996.

[10] European Union Aviation Safety Agency, Concept Paper on Artificial Intelligence, Proposed Issue 03, 2026; and EASA Artificial Intelligence Roadmap 2.0, 2023.

[11] A. Alaimo, A. Esposito, C. Orlando, and A. Simoncini, “Aircraft pilots workload analysis: Heart rate variability objective measures and NASA-Task Load Index subjective evaluation,” Aerospace, vol. 7, no. 9, Art. no. 137, 2020, doi: 10.3390/aerospace7090137.

[12] International Civil Aviation Organization, “Accident/Incident Data Reporting (ADREP) Taxonomy,” ICAO Aviation Investigation and Reporting taxonomy resources, accessed 2026.

[13] Federal Aviation Administration, Flight Operational Quality Assurance, Advisory Circular AC 120-82, Apr. 12, 2004.

[14] NASA Aviation Safety Reporting System, “ASRS Database Online” and “About ASRS Data,” NASA Ames Research Center, accessed 2026.

[15] National Transportation Safety Board, “Aviation Investigation Search/Aviation Accident Database,” Washington, DC, USA, accessed 2026.

[16] D. A. Wiegmann and S. A. Shappell, A Human Error Approach to Aviation Accident Analysis: The Human Factors Analysis and Classification System. Aldershot, U.K.: Ashgate, 2003.

[17] J. Reason, Human Error. Cambridge, U.K.: Cambridge University Press, 1990.

[18] R. Parasuraman, T. B. Sheridan, and C. D. Wickens, “A model for types and levels of human interaction with automation,” IEEE Trans. Syst., Man, Cybern. A, vol. 30, no. 3, pp. 286-297, 2000, doi: 10.1109/3468.844354.

[19] L. Breiman, “Random forests,” Mach. Learn., vol. 45, pp. 5-32, 2001, doi: 10.1023/A:1010933404324.

[20] C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, pp. 273-297, 1995, doi: 10.1007/BF00994018.

[21] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2016, pp. 785-794, doi: 10.1145/2939672.2939785.

[22] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436-444, 2015, doi: 10.1038/nature14539.

[23] S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30, 2017, pp. 4765-4774.

[24] O. D. T. Chukwuemeka and M. N. Mupa, “Preventing the unpredictable: An integrated human factors approach to reducing aviation accidents in the era of advanced flight technology,” IRE Journals, vol. 9, no. 12, 2026, doi: 10.64388/IREV9I12-1719259.

[25] O. D. T. Chukwuemeka and M. N. Mupa, “Risk-based IFR and multi-engine training standardization for high-density airspace: A safety management framework for Part 141 and Part 61 flight training,” IRE Journals, vol. 9, no. 12, 2026, doi: 10.64388/IREV9I12-1719260.

[26] O. D. T. Chukwuemeka and M. N. Mupa, “Predictive training analytics for competency-based pilot instruction: Early detection of checkride risk, procedural noncompliance and safety-relevant learning gaps,” World J. Adv. Res. Rev., vol. 30, no. 3, pp. 1928-1942, 2026, doi: 10.30574/wjarr.2026.30.3.1774.

[27] A. A. Adeyekun, “Artificial intelligence framework for predictive sediment dynamics and environmental vulnerability assessment in data-limited coastal systems,” J. Comput. Anal. Appl., vol. 33, no. 2, 2024.

[28] A. Adeyekun and J. William, “Evaluating the impact of geological features in shaping biodiversity and ecosystem health,” i TECH MAG, vol. 7, pp. 97-101, 2025, doi: 10.26480/itechmag.07.2025.97.101.

[29] A. A. Adeyekun, O. H. Orekoya, and O. A. Abiola, “Data-driven monitoring and modelling of coastal and sedimentary systems for climate resilience,” Int. J. Data Sci. Eng., vol. 1, no. 1, pp. 10-42, 2023, doi: 10.34218/IJDSE_01_01_002.

[30] M. B. Ashimolowo, “A computational model for risk-based quality governance assessment in high-risk infrastructure projects: A weighted composite scoring approach,” J. Comput. Anal. Appl., vol. 33, no. 8, 2024.

[31] M. B. Ashimolowo and A. G. Oginni, “Quality governance and performance stability in high-risk projects: A framework for strategic assurance,” Int. J. Sci. Res. Mod. Technol., vol. 1, no. 2, pp. 16 40, 2022, doi: 10.38124/ijsrmt.v1i2.1282.

[32] B. A. Mu'awiya, R. Ayanwunmi, E. Sangodiji, and A. Adeyekun, “Geotechnical and groundwater assessment for helipad construction on southwestern Nigerian coastline,” presented at the GSA Connects 2025 Meeting, San Antonio, TX, USA, 2025, doi: 10.1130/abs/2025AM-4897.

[33] A. J. Olaseinde, “The speed of theft: Developing real-time detection protocols for authorized push payment (APP) fraud in instant payment systems,” J. Comput. Anal. Appl., vol. 33, no. 6, 2024.

[34] A. J. Olaseinde and B. B. Azeez, “The trust paradox: Analyzing auditor reliance on hallucinating generative AI models in internal control testing,” Int. J. Artif. Intell. Eng. Transform., vol. 3, no. 1, pp. 38-49, 2022, doi: 10.54660/IJAIET.2022.3.1.38-49.

[35] C. E. Isalar, “Role of activated charcoal and polyvinylpyrrolidone (PVP) in enhancing callus quality in recalcitrant plants,” Int. J. Sci. Res. Sci. Technol., vol. 12, no. 6, pp. 721-747, 2025, doi: 10.32628/IJSRST25126402.

[36] C. E. Isalar, “Comparative evaluation of auxins and cytokinins on callus morphology and biomass accumulation,” Int. J. Sci. Res. Sci. Technol., vol. 7, no. 4, pp. 504-522, 2020, doi: 10.32628/IJSRST2293198.

[37] E. P. Aisagbonhi, C. E. Isalar, V. D. Odenore, J. U. Ogbebor, C. R. Eke, O. Asemota, and H. O. Shittu, “The interplay between explant developmental stages and phytohormone type in callogenesis of shea tree (Vitellaria paradoxa C. F. Gaertn),” Eur. Int. J. Sci. Technol., vol. 4, no. 7, pp. 50-57, 2015.

[38] C. E. Isalar, “Optimization of phytohormone combinations for enhanced callus induction in medicinal plant explants,” Int. J. Sci. Res. Sci. Technol., vol. 3, no. 4, 2017, doi: 10.32628/IJSRST2316114.

[39] C. E. Isalar, “Soil nutrient dynamics and their influence on shea nut productivity and butter quality,” Int. J. Sci. Res. Civ. Eng., vol. 6, no. 2, pp. 234-251, 2022, doi: 10.32628/IJSRCE226226.

[40] C. E. Isalar, “Effect of explant type and developmental stage on in vitro regeneration efficiency of tropical tree species,” Int. J. Sci. Res. Sci. Technol., vol. 13, no. 4, pp. 314-326, 2026, doi: 10.32628/IJSRST2613435.

[41] A. P. Nwite, N. B. Owoh, B. O. Emoghene, C. E. Isalar, and H. O. Shittu, “Controlling microbial contamination and browning of coconut (Cocos nucifera L.) inflorescence culture,” Niger. J. Biotechnol., vol. 35, no. 1, pp. 93-98, 2018, doi: 10.4314/njb.v35i1.11.