Agile Aerospace Engineering Education: GenAI to Navigate Resource Constraints

Abstract

The traditional paradigm of aerospace engineering education in emerging economies is currently trapped in a “resource mirage”—an attempt to replicate heavily funded Western laboratory models within fiscally constrained environments. This mismatch creates a static curriculum decoupled from local industrial realities. This study addresses this dependency by proposing a Dual-Stream Agile Framework that shifts pedagogical focus from rigid course-level structures (“molecular”) to fundamental engineering competencies (“atoms”). Leveraging Generative Artificial Intelligence (Gemini 1.5 Pro) as a human-in-the-loop diagnostic engine, the pipeline text-mines an audited screening corpus of N = 1,420 public news and state enterprise records from 2022 to 2026, isolating N = 40 core industrial operational frictions decomposed into 109 canonical pedagogical atoms. A two-gate audit protocol established high inter-coder reliability for atom extraction (Cohen’s κ = 0.812) and expert policy alignment evaluation (Fleiss’ κ = 0.782 across N = 16 domain experts). Quantitative policy mapping across 15 curriculum clusters (C01–C15) and ten strategic pillars of Zimbabwe’s National Development Strategy 2 (NDS-2) yielded an Aggregate Alignment Coefficient of Γ = 0.730. Expert validation survey results confirmed strong professional agreement on Industrial Relevance (Median = 4.0, µ = 4.38/5.0, CV = 14.1%) and Academic Rigor (Median = 4.0, µ = 4.06/5.0, CV = 14.1%), alongside high Equipment Substitution Fidelity (Fs = 0.912, 95% CI [0.884, 0.938]). Rather than serving as an autonomous accreditation mechanism, the framework provides a transparent, data driven decision-support template for emerging economies to navigate resource constraints and co-design technically rigorous, industry aligned engineering curricula.