AI Competency Model for Aerospace Engineering Managers

Abstract

As crewed spaceflight and deep-space exploration accelerate, aerospace engineering organisations are adopting digital and AI-enabled management to meet higher mission cadence, stringent compliance, and evidence-based assurance needs. Yet generic competency frameworks do not adequately capture the role-specific competencies required under safety-critical and highly traceable governance. This study developed and validated an AI competency model and closed-loop assessment framework for aerospace engineering managers. Competency constructs were elicited via RepGrid interviews (n=30) and validated using PCA (n=217) and CFA (n=209). The resulting five-dimensional model showed good internal consistency (Cronbach’s α=0.900) and acceptable fit (RMSEA=0.026; SRMR=0.045). Expert judgements (n=14) were used to model interdependencies and derive system-aware weights via DEMATEL-DANP. In a recruitment scenario with five candidates (H1-H5) rated by four experts, an improved VIKOR method produced a compromise ranking and gap diagnosis; H5 remained top-ranked across the tested V settings (V=0.1-0.9). The framework provides interpretable ranking and diagnostic outputs to support human-centered, AI-enabled, traceable decisions for selection and development across the project lifecycle.

Keywords: Aerospace Engineering Management, AI Competency, Human-centered Aerospace Systems, Space Sustainability, Repertory Grid, DEMATEL-DANP, VIKOR

DOI: 10.54941/ahfe1007846