"Exploring data literacy and human-AI collaboration skills in Predictive Maintenance Training."

The aviation industry’s shift toward Predictive Maintenance (PdM), powered by ArtificialIntelligence (AI) and Big Data, is transforming the role of maintenance technicians from manual troubleshootersto data-driven decision-makers. While PdM systems can forecast component degradation and optimizemaintenance schedules, their success ultimately depends on the technician’s ability to interpret and act uponprobabilistic information. Current research has concentrated on the technical development of PdM algorithmsand traditional human error, leaving a significant gap in understanding the cognitive and collaborative skillsrequired in this emerging digital environment.This paper explores the competencies of data literacy and human AI collaboration as critical enablers for effectivePdM implementation. It argues that existing Maintenance Resource Management (MRM) training does notadequately prepare technicians for decision-making under uncertainty, thereby increasing the risks of automationbias and data misinterpretation.Using a conceptual qualitative synthesis, the study develops a structured competency framework that integratestwo key domains: (1) data literacy emphasizing the evaluation of data quality and probabilistic Remaining UsefulLife (RUL) outputs and (2) human AI collaboration focusing on calibrated trust, interpretability, and feedbackmechanisms in AI-assisted maintenance environments. By shifting the emphasis from manual proficiency tocognitive readiness, this framework supports a safer, human centered integration of AI into aircraft maintenancepractice and establishes the foundation for future curriculum design and regulatory guidance in predictivemaintenance training.