AI Competency Model for Aerospace Engineering Managers: A Multi-Attribute Decision-Making Approach

Space Science & Technology, vol. 6

Abstract

Against the backdrop of advancing deep-space missions such as crewed lunar exploration and Mars exploration, aerospace engineering management is accelerating the integration of artificial intelligence (AI) into management workflows. Consequently, the AI competency of aerospace engineering managers has become a critical factor influencing mission delivery performance and safety compliance. This study proposes a closed-loop modeling and assessment framework for aerospace engineering management roles, based on multi-attribute decision-making (MADM) framework. First, 20 core competencies were identified through repertory grid interviews with aerospace engineering managers; subsequently, a 5-dimensional AI competency model was constructed and validated using questionnaire data (exploratory sample n = 217, validation sample n = 209). Further, an influence network relationship map (INRM) was established via decision-making trial and evaluation laboratory (DEMATEL) using expert ratings, with the DEMATEL-based analytic network process (DANP) employed to derive dimensional and feature weights. Finally, a proof-of-concept assessment was conducted on 5 genuine candidates for aerospace engineering management roles, using an improved VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approach to demonstrate candidate ranking, gap diagnosis, and robustness to compromise preference settings. The proposed framework yields interpretable influence-aware weights and produces stable ranking patterns across trade-off coefficients, providing traceable decision support for mission-role readiness assessment and targeted competency strengthening under safety-critical, strongly regulated AI-enabled project environments.

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