Reimagining human agency in AI-driven futures: a co-evolutionary scenario framework from aviation

Abstract

This study develops a co-evolutionary foresight framework to explore the future of human roles in AI-integrated aviation. Moving beyond deterministic models of automation, it conceptualizes AI integration as a recursive process shaped by technological innovation, institutional adaptability, and workforce transformation.

Drawing on evolutionary economics and socio-technical systems theory, the research integrates three methodological layers: historical case analysis of aviation transitions, theory-informed scenario construction, and a Delphi-based expert validation process with senior aviation stakeholders. The resulting 2×2 scenario matrix outlines four plausible futures: Strategic Co-evolution, Human-Centric Continuity, Latent Obsolescence, and Human Displacement, each reflecting different configurations of AI intensity and institutional responsiveness. Among these, Strategic Co-evolution emerges as the most plausible and desirable path, highlighting the importance of anticipatory governance, hybrid role design, and institutional alignment. In contrast, scenarios marked by institutional inertia or fragmented oversight raise concerns about skill erosion, trust degradation, and systemic risk. The study contributes a transferable methodology for futures research and provides actionable insights for regulators, training institutions, and labor actors. It underscores that the trajectory of AI integration in aviation is not technologically preordained but depends critically on the strategic codesign of institutional safeguards, workforce readiness, and socio-technical trust. The framework offers a model for examining AI transitions in other high-stakes domains, advancing a participatory and empirically grounded approach to exploring human–AI futures.