The emergence of Human-AI teaming within the aviation ecosystem introduces profound implications for fatigue management, operational resilience, and safety assurance. As artificial intelligence systems become increasingly embedded in flight operations, predictive analytics, crew scheduling, and cockpit decision support, the integration of Fatigue Risk Management Systems (FRMS) takes on an expanded and critical role. This paper examines how FRMS principles can support, and must be adapted to support, the safe and effective implementation of human-AI teaming, ensuring that technological innovation enhances rather than undermines human performance in safety-critical environments. The paper situates fatigue as a persistent human factor risk that continues to shape error pathways, cognitive performance, and decision-making under operational stress. AI-enabled systems hold the potential to augment fatigue management by providing real-time physiological monitoring, predictive fatigue modelling, adaptive workload distribution, and decision-support cues during periods of reduced alertness. However, the introduction of AI also reshapes the operational landscape: as tasks shift between human and machine, the cognitive workload profile of pilots may fluctuate unpredictably. In highly automated or single-pilot contexts, AI systems may inadvertently increase fatigue risks by imposing vigilance demands, imposing excessive monitoring burdens, or providing poorly calibrated levels of assistance. These emerging risks underscore the need for FRMS frameworks capable of recognising and governing the unique human-machine interactions associated with advanced automation. The paper examines how each core component of FRMS, policy, risk assessment, data-driven monitoring, and training, contributes to the governance of human-AI teaming. FRMS Policy must explicitly acknowledge AI as both a potential fatigue mitigator and a fatigue hazard, emphasising a human-centric philosophy that prioritises pilot wellbeing alongside operational efficiency. Fatigue Risk Assessment processes must incorporate new hazard categories, including over-reliance on algorithmic alerts, cognitive underload resulting from task redistribution, and increased monitoring pressures associated with supervising autonomous functions. Safety Assurance within FRMS must transform into a continuous, adaptive process capable of monitoring AI performance, evaluating AI-human workload balance, and detecting early signs of fatigue-induced system drift. The paper proposes an integrated FRMS-AI governance model tailored to the future of human-AI teaming. This model incorporates predictive fatigue analytics, adaptive automation strategies, human-machine workload harmonisation, and AI-specific fatigue indicators within FRMS oversight. The findings emphasise that the success of human-AI teaming in aviation will depend not solely on technological progress but on the robustness of FRMS to manage evolving human cognitive vulnerabilities in increasingly automated operational ecosystems.
The increasing adoption of Human–AI teaming across the transportation sector is reshaping operational roles, organisational structures, and future workforce competencies. As artificial intelligence systems evolve from decision-support tools to collaborative teammates capable of perception, prediction, and autonomous action, transportation organisations must fundamentally rethink how they design, train, allocate, and sustain their workforce. Workforce planning—traditionally centred on staffing levels, qualification pipelines, and operational forecasts—now plays a pivotal role in ensuring that human capabilities remain aligned with the cognitive, technical, and ethical demands of future human–AI teams. This paper examines the strategic function of workforce planning in supporting the safe and effective implementation of human–AI teaming across aviation, rail, maritime, and ground transportation environments.The analysis begins by outlining the systemic transformations introduced by AI-driven operations. In contrast to earlier waves of automation, contemporary AI systems introduce non-deterministic behaviour, dynamic adaptability, and shared decision-making responsibilities. These characteristics challenge legacy workforce models premised on stable task distributions and predictable human roles. Across transportation modes, the shift toward human–AI teaming requires workforce planners to anticipate emerging competencies such as AI oversight, interpretability skills, mixed-initiative collaboration, and algorithmic risk assessment. The alignment of these competencies with recruitment, selection, training, and career progression becomes essential to preventing skill gaps, cognitive overload, and mismatches between human capabilities and system demands.The paper further examines workforce planning as a human factors instrument that supports organisational readiness. Effective planning requires an integrated understanding of future task redesign, shifts in workload dynamics, and the redistribution of responsibilities between humans, AI agents, and remote support structures. In aviation, for example, workforce planners must prepare for mixed-crew configurations, single pilot operations with AI copilots, and remote supervisory roles; in rail and maritime systems, the emergence of autonomous navigation and predictive maintenance similarly redefines operator roles. Planning must therefore incorporate scenario-based forecasting, human reliability analyses, and long-term modelling of human–machine performance interactions to ensure that human resource strategies align with technological evolution.Training and competency development are examined as critical components linking workforce planning to operational implementation. As human–AI teaming becomes central to safety-critical operations, organisations must develop training pathways that cultivate adaptive expertise, trust calibration, interpretability awareness, and resilience under automation. Workforce planning provides the structural basis for identifying training populations, sequencing AI-focused skills acquisition, and embedding competency-based training and assessment (CBTA/EBT) methods across the transportation ecosystem. The paper argues that overlooking this alignment risks creating workforces that are operationally certified but cognitively unprepared for high-automation environments.The study also addresses regulatory, cultural, and organisational constraints. Many transportation regulators have yet to establish competency frameworks for human–AI teaming, leaving workforce planners without standard definitions of required skills or acceptable performance thresholds. Organisational cultures may further influence AI acceptance, trust, and reporting behaviours, requiring workforce planning to integrate cultural readiness assessments and targeted change-management strategies.The paper concludes by proposing a workforce planning model designed to support large-scale adoption of human–AI teaming in transportation. This model integrates technological forecasting, human factors analysis, competency mapping, and AI-focused resilience strategies. The findings emphasise that technological innovation alone is insufficient; the success of human–AI teaming depends on a strategically designed workforce that is cognitively, operationally, and organisationally prepared for the transport systems of the future.
Dimitrios Ziakkas, Ibrahim Sarikaya, Konstantinos Pechlivanis et al.· AHFE International· 0 citations
Human–AI teaming is rapidly emerging as a defining paradigm in next-generation aviation operations, reshaping pilot roles, altering cockpit task distribution, and challenging established assumptions regarding expertise, decision-making, and training. As artificial intelligence systems evolve from deterministic support tools into adaptive, autonomous teammates capable of perception, prediction, and intent-driven action, the aviation training ecosystem faces a suite of unprecedented challenges. These challenges extend beyond purely technical skills and encompass deeper questions of trust calibration, cognitive adaptation, workload redistribution, ethical responsibility, and sustained human performance. This paper examines the central training challenges associated with preparing pilots, instructors, and organisational systems for effective human–AI teaming across current and expected future aviation environments.First, the paper analyses the shifting cognitive and operational landscape introduced by AI-enabled systems, including adaptive automation, predictive analytics, natural-language interfaces, and mixed-initiative control architectures. Whilst these technologies promise enhanced situational awareness, reduced workload, and strengthened predictive safety nets, they simultaneously introduce risks such as automation complacency, algorithmic over-reliance, erosion of manual competencies, and emergent forms of mode confusion. Training organisations must therefore rethink curriculum design to cultivate appropriate levels of trust in AI agents while strengthening pilots’ abilities to monitor, interrogate, and, where necessary, override AI behaviour during uncertainty or system drift. Traditional training paradigms based on linear automation logic are insufficient to address the probabilistic and at times opaque behaviour of modern AI systems.Second, the paper explores the pedagogical complexities inherent in developing joint human–AI decision-making skills. Effective teaming requires robust communication transparency, alignment of mental models, and the formation of shared situational awareness between human operators and algorithmic agents. Yet many AI systems operate as “opaque teammates,” offering outputs without interpretive depth or explainable reasoning. Training must therefore introduce strategies for evaluating machine-generated recommendations, identifying algorithmic bias, integrating AI insights with experiential human judgement, and managing discrepancies between human and AI interpretations. Scenario-based training, explainable AI (XAI) tools, and structured failure-mode exploration are presented as essential approaches for mitigating these challenges.Third, organisational, regulatory, and standardisation constraints are evaluated. The absence of harmonised human–AI competency frameworks, variability in AI system behaviour across aircraft types, and ambiguities regarding accountability pose obstacles for both initial and recurrent training. A critical need exists for evidence-based human factors methodologies that define the skills required for pilots operating in mixed-initiative or partially autonomous environments. Emerging competency-based training and assessment (CBTA/EBT) methodologies offer a promising foundation but require expansion to incorporate AI teaming competencies, error management strategies, and resilience-building mechanisms.The paper argues that training for human–AI teaming must remain fundamentally human-centric, preserving pilots’ adaptive expertise, situational awareness, and critical thinking while ensuring that AI systems remain compatible with human cognitive strengths and limitations. It concludes by proposing an integrated training model to support safe, resilient, and ethically aligned human–AI cooperation in future aviation operations.
Dimitrios Ziakkas, Ibrahim Sarikaya, Debra Henneberry· AHFE International· 0 citations
Resilience has become a defining attribute of effective military organisations, particularly within aviation domains where uncertainty, operational tempo, and mission-critical decision-making place continuous cognitive and organisational demands on personnel. This paper examines the role of resilience in military aviation operations through an in-depth case study of the Hellenic Air Force Academy (HAFA), analysing how resilience is cultivated, supported, and operationalised across training, leadership development, organisational structures, and the socio-technical systems that underpin flight operations. As modern air forces confront evolving threats, technological complexity, and dynamically changing geopolitical environments, resilience emerges as both a human performance capability and a strategic organisational asset essential for mission success.The analysis begins by conceptualising resilience as a multi-level construct encompassing individual adaptability, team cohesion, organisational flexibility, and systemic robustness. Within military aviation, resilience supports the ability to anticipate disruptions, absorb operational pressures, adapt strategies under uncertainty, and recover effectively from setbacks or unexpected events. The Hellenic Air Force Academy provides a compelling context to explore resilience development due to its integrated approach to academic education, flight training, physical conditioning, and ethical leadership formation.The paper explores the Academy’s training philosophy, emphasising how resilience is deliberately embedded into the curriculum through progressive exposure to complexity, stress inoculation, scenario-based simulation, and disciplined team coordination exercises. Cadets are trained to manage cognitive load, maintain situational awareness, and exercise adaptive decision-making under time pressure and operational ambiguity. Cultural factors, including the Academy’s emphasis on honour, collective responsibility, and disciplined autonomy, further reinforce resilience by creating a psychologically safe yet demanding environment where cadets learn to navigate failure constructively.Team-level resilience is analysed through flight training practices, where cadets engage in high-risk, precision-dependent training missions that require constant communication, mutual support, and cross-monitoring. Instructors act as resilience facilitators, teaching cadets to recognise early signs of performance degradation, manage emotional responses, and apply recovery strategies. The paper highlights how these competencies translate directly into the operational needs of military aviation where team resilience underpins mission reliability and survivability.At the organisational level, the case study examines HAFA’s structural enablers of resilience, including its Safety Management System, debriefing culture, leadership development programmes, and integration of emerging technologies such as advanced simulators, data-driven training feedback systems, and human performance monitoring tools. These mechanisms support continuous learning, error tolerance, and adaptive improvement—key components of organisational resilience in complex military environments.The study also situates HAFA within broader geopolitical and technological challenges faced by modern air forces: increased mission complexity, hybrid threats, automation, cybersecurity demands, and multinational operations. Resilience is discussed as a strategic capability that enables the Hellenic Air Force to maintain readiness, ensure force protection, and adapt effectively to evolving operational landscapes.The paper concludes by proposing a resilience-centred framework for military aviation training and organisational development, positioning the Hellenic Air Force Academy as a model for cultivating human and organisational resilience within high-reliability military systems. The findings underscore that resilience is not a supplementary attribute but an operational necessity for sustaining performance, safety, and mission success in contemporary military aviation.
Ioanna K. Lekea, Dimitrios Ziakkas, D. Stamatelos et al.· AHFE International· 0 citations
The integration of Human–AI teaming within the aviation ecosystem represents a transformative evolution in safety-critical operations, demanding robust organisational frameworks capable of managing emerging risks, validating new operational concepts, and sustaining human performance. As artificial intelligence becomes increasingly embedded in flight operations, maintenance, training, and safety analytics, the role of Safety Management Systems (SMS) becomes central to ensuring that human–AI collaboration is introduced, monitored, and governed in a manner consistent with international safety expectations. This paper examines how contemporary SMS principles support—and in many cases must be adapted to support—the safe and effective implementation of human–AI teaming across the aviation industry.The analysis begins by framing AI integration as a socio-technical challenge that profoundly alters hazard identification, risk modelling, and safety assurance processes. AI-enabled systems introduce unique characteristics—opacity, non-determinism, continuous learning, and probabilistic behaviour—that challenge conventional safety assumptions. SMS, traditionally grounded in predictable system performance, must expand to accommodate risks arising from algorithmic drift, data quality variability, automation bias, and human–machine misalignment. The paper argues that SMS frameworks must evolve beyond compliance-driven practices to embrace dynamic, data-rich safety monitoring capable of detecting emergent patterns of human–AI interaction.The study further explores how each component of SMS—Safety Policy, Safety Risk Management, Safety Assurance, and Safety Promotion—contributes to the governance of human–AI teaming. Within Safety Policy, organisational commitments must reflect a human-centric philosophy ensuring that AI systems complement, not replace, human cognitive strengths. Safety Risk Management must incorporate new methodologies for identifying hazards associated with collaborative automation, including unintended consequences of predictive algorithms, mismatches between AI intent and pilot expectation, and reduced redundancy in single-pilot or high-automation environments. Safety Assurance processes must evolve to include continuous performance monitoring of AI agents, explainability audits, validation of training effectiveness, and mechanisms for detecting shifts in human–AI trust relationships.Safety Promotion is examined as a crucial enabler of cultural readiness. The introduction of AI into safety-critical operations requires transparent communication, cross-disciplinary literacy, and training programmes that cultivate both confidence and critical scepticism toward AI-generated outputs. Emphasis is placed on building a safety culture that encourages reporting of anomalies involving AI systems, fosters shared understanding between technical and operational personnel, and supports learning from human–AI interaction events. The Turkish Airlines, Lufthansa Group, and FAA/EASA regulatory developments are referenced as indicative of industry movement toward SMS-driven oversight of intelligent systems.The paper concludes by proposing a strengthened SMS framework tailored to human–AI teaming. This enhanced model integrates explainable AI within risk assessment processes, adopts resilience engineering principles to manage uncertainty, incorporates AI-specific safety indicators, and emphasises adaptive training frameworks aligned with CBTA/EBT approaches. The findings suggest that the long-term success of human–AI teaming in aviation will depend not solely on technological capability but on the ability of SMS to anticipate, govern, and continuously validate the evolving dynamics of human–AI collaboration.
Resilience has become a central concept in contemporary aviation safety, reflecting the industry’s need to manage complexity, uncertainty, and unexpected disturbances across increasingly automated and dynamic operational environments. While flight operations depend on the capacity of individuals, teams, and organisations to anticipate, adapt, and recover from disruptions, the practical implementation of resilience remains challenging. Safety Management Systems (SMS), as mandated frameworks across global aviation, play a critical role in shaping how resilience is operationalised, monitored, and sustained. This paper examines the challenges associated with implementing resilience in flight operations and analyses how SMS can support or hinder this integration.The analysis begins by defining resilience as a multi-dimensional capability, encompassing anticipation of potential threats, monitoring of system variability, adaptation to changing conditions, and recovery from disruptions. Within flight operations, resilience extends beyond pilot decision-making to include coordination between dispatchers, maintenance personnel, air traffic controllers, and organisational structures that guide operational decisions. Despite its conceptual prominence, resilience is often poorly translated into training programmes, procedural design, and operational policies, leading to fragmented or superficial implementation.The paper identifies several systemic challenges that hinder resilience adoption. First, traditional safety approaches remain predominantly reactive, focusing on compliance and incident investigation rather than proactive monitoring of system variability and weak signals. This reactive orientation limits the ability of SMS to identify early indicators of fragility or organisational drift. Second, existing performance metrics often prioritise efficiency and procedural adherence, inadvertently discouraging the adaptive behaviours that resilience requires. Third, high automation in modern flight decks can lead to reduced pilot engagement, erosion of manual flying skills, and over-reliance on automated systems—conditions that undermine adaptive capacity during system surprises or degraded modes.Human factors challenges are also examined. Pilots and operational personnel must maintain cognitive flexibility, situational awareness, and collaborative communication under dynamic conditions, yet training programmes frequently emphasise standardisation over adaptability. Additionally, organisational cultures that struggle with Just Culture principles may inhibit the open reporting and learning necessary for resilience development.The role of Safety Management Systems is critically analysed as both an enabler and a constraint. SMS offers structured processes for hazard identification, risk assessment, safety assurance, and safety promotion—all of which can support resilience if applied through a proactive, systems-oriented lens. However, many organisations implement SMS in a compliance-driven manner that prioritises documentation over learning, thereby limiting opportunities to build adaptive capacity. The paper argues that SMS must evolve to integrate resilience engineering principles, including system variability analysis, predictive monitoring, scenario-based learning, and cross-functional coordination mechanisms. Embedding resilience within SMS requires cultural transformation, leadership commitment, and the inclusion of resilience-focused competencies within CBTA/EBT frameworks.The paper concludes by proposing a resilience-enhanced SMS model tailored for flight operations. This model incorporates continuous monitoring of operational variability, systemic learning loops, transparent reporting structures, and training designed to cultivate adaptive cognitive and teamwork skills. The findings underscore that achieving genuine resilience in flight operations requires shifting SMS from a compliance instrument to a dynamic organisational capability that sustains safety performance in the face of uncertainty.
Ibrahim Sarikaya, Dimitrios Ziakkas, Eleftherios Bokas et al.· AHFE International· 0 citations
This paper explores resilience at the individual, team, organisational, and system levels, which are crucial for anticipating, absorbing, adapting to, and recovering from disruptions in complex environments, and concludes with a resilience framework emphasising human–machine teamwork, adaptive governance, cross-sector learning, and socio-technical integration.
A multi-layered safety model that integrates emerging technologies with human-centred practices, emphasising resilience engineering, adaptive training, transparent AI governance, and continuous learning across transportation ecosystems is proposed, arguing that technological innovation must be framed not as a replacement for human expertise but as an enabler of enhanced human performance.