Aug 2026· Human Factors· pp.
187208261474345
· 0 citations· 220 references
Medicine
TL;DR
Communication inflexibility, limited shared understanding, and trust miscalibration emerge as recurring barriers to HAT, while regulatory capacities represent particularly critical dimensions of HAT readiness that remain to be fully operationalized.
Abstract
ObjectiveThis narrative review examines the cognitive, metacognitive, and team competency requirements that may contribute to productive and reliable collaboration between human and AI to address two questions: What capabilities make AI a competent collaborator? What makes humans ready for AI collaboration?BackgroundAs AI systems are increasingly integrated into workplaces and framed as teammates rather than tools, humans face challenges that include maintaining situation awareness, calibrating trust, and working with systems that may surpass them cognitively. We analyzed Human-Agent Teaming (HAT) readiness around two complementary levels: operational team competencies (communication, coordination, and adaptability) and regulatory capacities (trust calibration and metacognitive awareness).MethodWe conducted a structured narrative review of literature from 2010 through January 2026, searching Google Scholar, Scopus, PsycINFO, IEEE Xplore, ACM Digital Library, and Semantic Scholar, complemented by forward citation tracking. After screening 572 records, 192 articles were included for synthesis.ResultsCommunication inflexibility, limited shared understanding, and trust miscalibration emerge as recurring barriers to HAT, while regulatory capacities (trust calibration and metacognitive awareness) represent particularly critical dimensions of HAT readiness that remain to be fully operationalized.ConclusionHAT requires mutual readiness, with both humans and AI developing metacognitive and adaptive capabilities. Despite methodological heterogeneity limiting clear conclusions, cross-training and co-learning methods offer a promising avenue for building shared understanding and calibrated collaboration.ApplicationThis review provides practical principles for designing AI systems that support calibrated collaboration and for preparing humans to work adaptively with AI, thereby enhancing team effectiveness, reliability, and resilience in collaborative work environments.
Human-AI collaboration (HAIC) has moved from research curiosity to strategic priority for organizations pursuing faster, more inventive, and more reliable innovation outcomes. Many implementations underperform, however, because leaders treat artificial intelligence as a generic productivity tool rather than as a designed teammate whose role, capabilities, and trust requirements must match the task. This article synthesizes recent scholarship and practitioner experience into a structured playbook for executives, design leaders, and human resources partners. It argues that the value of HAIC depends on three deliberate design choices: who initiates the collaboration, how broad the AI's knowledge scope must be, and whether the cognitive mode is analytical or synthetic. Drawing on engineering design, aerospace, industrial product development, hospitality, and mental health contexts, the discussion translates research findings into operating practices, governance structures, and capability investments. The contribution is practical: a clearer way to decide what kind of AI teammate to build, deploy, and trust for any given problem.
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design.
M. R· International Journal of Phi...· 0 citations
Human–Autonomy Teams (HATs), composed of humans and agents such as artificial intelligence, face distinctive challenges in dynamic environments. As a result, they often struggle to adapt effectively, limiting their potential. Traditional team theories do not fully capture the unique characteristics of HATs, and research remains fragmented across disciplines, offering limited theoretical and practical guidance. Consequently, we propose a holistic framework that integrates the dynamic interplay between team members, tasks, and the environment. Drawing on multidisciplinary insights, we identify four key conditions—predictability, observability, plannability, and directability—that govern how HATs respond to shifting interdependencies. For each condition, we advance testable propositions, illustrate their relevance through established HAT testbeds, and outline technical, organizational, and interactional strategies to foster their emergence. Finally, we discuss how the framework provides a conceptual foundation to guide future research, training, and the design of adaptive HATs, by reflecting on its broader theoretical and practical implications.
This study examines how the technological characteristics of Agentic AI relate to employees' psychological states and behavioral responses in organizations. Drawing on Job Demands-Resources (JD-R) theory and Conservation of Resources (COR) theory, we conceptualize Agentic AI technological characteristics (AITC) as a higher-order construct comprising autonomous planning, multi-tool orchestration, and proactive feedback, and propose that these characteristics function as job resources associated with greater cognitive surplus and psychological safety, which in turn relate to AI-human task reallocation. We further investigate whether these relationships differ between leaders and staff. A cross-sectional, anonymous online survey was administered via an online survey platform to participants recruited through a research panel using purposive nonprobability sampling; eligibility was restricted to adults with current or prior AI-related work experience, and screened, attention-checked responses yielded a final sample of 331 employees (171 leaders, 160 staff). All constructs were measured with validated scales adapted to the Agentic AI context on 7-point Likert scales. Data were analyzed using partial least squares structural equation modeling (PLS-SEM), applying a two-stage approach for the second-order construct, bootstrapping with 5,000 resamples for direct and indirect effects, and multi-group analysis (MGA) for leader-staff comparisons. The measurement model demonstrated acceptable reliability and validity, and the structural results were consistent with the theorized resource-gain model: AITC was positively associated with cognitive surplus, psychological safety, and task reallocation, with significant mediation pathways and stronger AITC-resource associations among leaders. By focusing on psychological mechanisms rather than technical performance, this study extends emerging Agentic AI research and offers practical implications for role-differentiated AI adoption strategies.
J. Han, Cheong Kim· Journal of Visualized Experi...· 0 citations
Purpose: Explainable artificial intelligence (XAI) is increasingly promoted as a mechanism for improving transparency, trust, and accountability in AI-assisted decision-making. However, its ethical value remains uncertain when AI systems are embedded within human teams and organizational workflows. This systematic literature review examines ethical issues associated with XAI in human–AI team contexts and explores how trust, decision-making, and accountability are associated with the use of XAI systems across organizational domains. Methodology: Guided by the SALSA framework and PRISMA 2020 guidelines, the review synthesized 25 studies drawn from healthcare, business and finance, cyber–physical systems, Earth Observation, and broader human–AI collaboration contexts. Findings: The findings revealed four interrelated themes: the paradox of explainability, in which transparency does not necessarily produce understanding; trust as a dynamic and fragile process shaped by user expertise, task stakes, and workflow integration; distributed accountability and the unresolved ethics of responsibility in human–AI collaboration; and bias, fairness, and the limits of explainability as a standalone ethical solution. Across the reviewed literature, XAI did not function as a self-sufficient safeguard against ethical risk. Rather, its ethical value depended on how explanations were designed, interpreted, coordinated within teams, and governed by organizations. Based on these findings, the study proposes the Sociotechnical Explainability Alignment (SEA) framework, which conceptualizes ethical XAI as requiring alignment across four levels: design, user, team, and organization. The review contributes to XAI scholarship by reframing explainability as a sociotechnical capability rather than a purely technical feature. It also offers practical guidance for organizations seeking to implement AI systems responsibly through human-centered design, trust calibration, role-sensitive explanation practices, and governance mechanisms that support accountability and fairness.
Hoda Parvaneh Shirazi, Ahmed Al‐Asfour, Sami El Ahmadie et al.· Systems· 0 citations
The rapid integration of large language models (LLMs) into organizational workflows raises fundamental questions about the long-term effects of AI assistance on human creative capabilities. This article provides a comprehensive theoretical extension of Sun et al.'s (2025) field experiment, which demonstrated that LLM assistance enhances creative output during use but diminishes subsequent independent creativity—particularly for individuals with lower metacognitive ability. We develop the metacognitive paradox framework to explain this phenomenon: AI tools designed to augment human creativity may inadvertently suppress the very cognitive processes that sustain independent creative capacity. Drawing on metacognition theory, cognitive offloading research, automation and human factors literatures, and the componential model of creativity, we articulate the mechanisms through which LLM assistance affects creative cognition, specify temporal dynamics and boundary conditions, and generate testable propositions for future research. We explicitly compare our framework against alternative theoretical explanations—including cognitive load theory, motivational accounts, and expertise development perspectives—and provide methodological guidance for testing our propositions. Our analysis reveals that the relationship between AI assistance and human creativity is neither uniformly beneficial nor detrimental but contingent upon individual metacognitive capabilities, patterns of tool use, task characteristics, and organizational context. We conclude with implications for theory, research methodology, organizational practice, and the ethical dimensions of AI-augmented work.
Jonathan H. Westover· The Innovation· 0 citations
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