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

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Publicado en:European Journal of Futures Research vol. 13, no. 1 (Dec 2025), p. 16
Autor principal: Navarro-Meneses, Francisco J.
Otros Autores: Pablo-Marti, Federico
Publicado:
Springer Nature B.V.
Materias:
Federal Aviation Administration > FAA
Futures
Technological change
Evolution
Collaboration
Adaptation
Aviation
Automation
Research methodology
Workloads
Pilots
Certification
Skills
Case studies
Co-design
Regulation
Perceptions
Artificial intelligence
Systems theory
Decision making
Human agency
Recursive functions
Workforce
Skill development
Literature reviews
Delphi method
Regulated industries
Safeguards
Responsiveness
Degradation
Adoption of innovations
Human evolution
Innovations
Frame analysis
Economics
Governance
Recursion
Evolutionary economics
Future
Transformation
Obsolescence
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2195-4194
2195-2248
DOI:10.1186/s40309-025-00260-w
Fuente:ABI/INFORM Global