It is argued that AI does not produce outcomes directly; rather, its effects are contingent on how it is accepted, interpreted, and enacted within school contexts, and trust shapes whether AI leads to constructive outcomes, including ethical use, professional engagement, and improvement, or to disruptive consequences such as resistance and mistrust.
Abstract
Artificial intelligence (AI) is rapidly reshaping school leadership within Education 4.0, offering enhanced decision-making and organisational efficiency while intensifying ethical concerns regarding transparency, bias, and accountability. Existing research has largely treated these opportunities and risks as separate phenomena, overlooking the relational processes through which AI is enacted in practice. This paper advances a process-based conceptualisation by positioning trust as the central mediating mechanism in AI-enabled school leadership. It argues that AI does not produce outcomes directly; rather, its effects are contingent on how it is accepted, interpreted, and enacted within school contexts. The proposed framework shows that trust shapes whether AI leads to constructive outcomes, including ethical use, professional engagement, and improvement, or to disruptive consequences such as resistance and mistrust. Leadership is conceptualised as a key antecedent of trust, highlighting the centrality of relational governance in the effective and responsible integration of AI in schools.
As organisations increasingly embed artificial intelligence (AI) into the routines of leadership, from predictive performance dashboards to AI-assisted feedback and coaching tools, a pressing but underexamined question is how this technological augmentation of leadership shapes employees' willingness to speak up. This paper develops and defends a conceptual model in which AI-augmented leadership predicts employee voice behaviour through two theoretically distinct but interlocking psychological mechanisms: trust in the combined leader-AI system, and psychological empowerment. Drawing on social exchange theory) the integrative model of organisational trust, Spreitzer's theory of psychological empowerment, and the growing empirical literature on trust in artificial intelligence, the paper argues that AI-augmented leadership is neither inherently emancipatory nor inherently silencing; its consequences for voice depend on whether it is experienced by employees as a resource that expands judgement and relational engagement or as a mechanism of algorithmic control that narrows it. The paper further situates trust and empowerment within the longer-standing voice literature on psychological safety and implicit theories of self-censorship, showing how these established constructs sharpen, rather than duplicate, the proposed model. Five research propositions are advanced, a boundary condition involving AI transparency is specified, and a research agenda for empirically testing the model is outlined. The paper contributes to the emerging literature on AI and leadership by reframing the central question from whether AI replaces leaders to how leaders' use of AI is interpreted by employees, and by clarifying the specific psychological pathways through which that interpretation either encourages or forecloses constructive dissent.
Kingsley I. Amadi· Journal of Management Resear...· 0 citations
This conceptual paper examines how artificial intelligence (AI) reshapes leadership dynamics. It addresses a critical gap: the lack of an integrated framework explaining how AI leadership influences employees' psychological and social resources.
The paper develops the AI-augmented leadership–well-being–relational energy model. This framework challenges the job demands-resources model's static view, theorizing digital well-being as a dynamic regulatory mechanism linking leadership behaviors to relational energy.
The analysis finds that AI-empowering leadership enhances digital well-being and strengthens relational energy. Conversely, AI-monitoring leadership depletes these same vital resources.
The proposed model is conceptual and needs empirical validation across different cultural and organizational settings.
The model provides leaders and organizations with a critical, human-centered framework for the ethical integration of AI into leadership practice, aiming to foster more resilient and energized workplaces.
Socially, it promotes digitally healthy, resilient workplaces.
The primary contributions are extending the job-demands and resource model, uniquely re-conceptualizing digital well-being as a regulatory resource and relational energy as a collective amplifier and recasting the leader's role as a sociotechnical architect.
P. K. Nanda· Leadership & Organizatio...· 0 citations
Universities rapidly scale learning and administrative analytics to support strategic and operational decisions, yet staff acceptance often lags when data use appears opaque, compliance-driven, or misaligned with academic values. Drawing on a sequential explanatory mixed-methods study of 238 academic and administrative leaders in four comprehensive Chinese universities, with follow-up interviews sampled across high, medium, and low levels of trust, literacy, and governance, we find that leadership transparency and dialogue predict institutional trust in analytics, while perceived staff trust remains more limited and constrained by concerns about surveillance, fairness, and participation, with middle leaders improvising data literacy support and ethical safeguards in context. The article proposes an Ethical Analytics Leadership framework in which transparency, dialogue, capacity building, and ethical governance reinforce trustworthy analytics, and explains how leaders navigate tensions between accountability and autonomy in data-intensive, highly centralized systems.
Artificial intelligence (AI) increasingly mediates leadership-relevant judgment through models, dashboards, metrics, decision-support systems, and autonomous agents. This conceptual article develops a socio-technical systems theory of systemically mediated leadership, defined as a nested system-level condition and recurrent process configuration through which human actors, AI systems, organizational routines, governance institutions, and affected stakeholders jointly produce and revise direction, meaning, consequential judgment, legitimacy, and accountability through recursive feedback. A problem-driven conceptual synthesis was updated through 3 August 2026. A structured discovery pass yielded 97 candidate records; 85 sources were retained after relevance screening, citation chaining, concept mapping, and comparison of eight candidate mechanism families. Four proposed qualification conditions jointly define the construct within the present framework: AI mediation, leadership relevance, distributed judgment, and recurrent institutional embedding. Five mechanism families explain transformations in responsibility, legitimacy, control, attention, and feedback timing: moral delegation, interpretive laundering, ceremonial oversight, metric-driven sensemaking, and ethical latency. A causal-loop model specifies justificatory reinforcement, capability atrophy, power insulation, and accountable correction. Their relative dominance produces three ideal-type dynamic regimes: accountable adaptation, stabilized trade-offs, and destructive drift. The theory predicts that organizations using equally accurate models may produce divergent leadership and accountability outcomes because their feedback, power, and oversight architectures differ. Responsible AI leadership thus depends on system architecture and contestable institutional practice, not leader intention, formal human approval, or model accuracy alone.
Generative artificial intelligence (GenAI) has rapidly become embedded in the strategic communication function, reshaping how organizations produce messages, engage stakeholders, and manage crises. Yet evidence on whether these capabilities strengthen organizational communication effectiveness or undermine public trust remains fragmented across disciplines. This article presents a systematic narrative literature review of twenty-five peer-reviewed studies published largely between 2022 and 2026, synthesizing findings from public relations, information systems, organizational behavior, and communication research. Using thematic synthesis, the review identifies four interrelated clusters: generative AI-enabled message production and personalization, algorithmic transparency and disclosure, trust calibration through tone and competence signaling, and organizational or institutional adaptation. Results show that efficiency and personalization gains are frequently offset by a transparency-trust paradox, whereby disclosing AI authorship can simultaneously legitimize and erode credibility depending on stakeholder AI literacy, message context, and institutional responsibility signaling. The novelty of this study lies in proposing an integrative Trust-Transparency-Effectiveness framework connecting micro-level message design, meso-level organizational practice, and macro-level public trust outcomes. The framework offers strategic communicators, corporate leaders, and policymakers a structured lens for deploying generative AI responsibly while safeguarding organizational legitimacy, stakeholder relationships, and public confidence in an increasingly algorithmically mediated communication environment.
It is found that AI use is associated with higher perceived negotiation efficiency, but also with lower AI system trust when automation displaces relational cues and explanations are weak, and multi-group analyses indicate stronger XAI–trust associations in the EU and stronger HIL associations in ASEAN.
Raja Mejri, Sameh Skhiri· Frontiers in Artificial Inte...· 0 citations
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