Predictive training analytics for competency-based pilot instruction: Early Detection of Checkride Risk, Procedural Noncompliance and Safety-Relevant Learning Gaps

This paper develops a predictive training analytics framework for competency-based pilot instruction. The objective is to detect checkride risk, procedural noncompliance, and safety-relevant learning gaps early enough for instructors, chief instructors, and training managers to intervene before deficiencies become repeated stage-check failures, unsafe normalization of deviations, or weak practical-test readiness. The study combines public aviation safety sources, including the National Transportation Safety Board aviation accident database, the NASA Aviation Safety Reporting System, FAA Airman Certification Standards, FAA risk-management and aviation-instructor guidance, and Part 61/Part 141 regulatory structures, with a reproducible synthetic training-record dataset representing variables commonly available to flight schools. Because individual student pilot lesson records and practical-test outcomes are not publicly released at scale, the synthetic dataset is used only as a transparent modeling demonstration that can be replaced with real school records in implementation.
The analytical design compares logistic regression, random forest, and gradient-boosting classifiers using lesson-stage variables such as repeat lessons, unsatisfactory maneuvers, procedural-deviation rate, radio-readback errors, IFR task incompletion, approach-stability violations, weather and workload exposure, instructor interventions, safety-event flags, knowledge-test score, stage-check score, lesson gaps, instructor-comment risk terms, and checklist-adherence scores. The logistic-regression model produced the strongest test performance in this simulation, with a test AUC of 0.837 and a five-fold cross-validation AUC of 0.850. The framework is not proposed as a punitive student-ranking system; it is proposed as a non-punitive, safety-management-aligned decision support tool that converts training records into early warning indicators, debrief priorities, proficiency gates, and individualized remediation.