WHEN AI CAPABILITY OUTPACES GOVERNANCE: GOVERNANCE–CAPABILITY MISALIGNMENT IN EDUCATIONAL LEADERSHIP

Abstract

Introduction: Artificial intelligence (AI) is expanding from educational support into institutional processes involving administrative, strategic, and consequential decision-making. This development creates a leadership challenge when AI increasingly shapes decision formation while formal authority and accountability remain assigned to human educational leaders (Chen & Ma, 2026; Dai et al., 2025). Although existing literature addresses educational AI governance, leadership, human oversight, decision authority, and adaptive governance, these streams remain insufficiently integrated in explaining changing governance fit (Cavalcante Siebert et al., 2023; Crompton et al., 2026; Jiang & Abdullah, 2026).

Methods: This study employs a structured integrative conceptual review and theory-extension approach to synthesise intersecting literature and extend governance–capability misalignment reasoning to educational leadership.

Results: The theoretical synthesis indicates that governance–capability misalignment can emerge when material changes in AI capability and actual AI decision influence are not accompanied by corresponding adjustments in educational governance configuration. The analysis identifies implications for leadership authority, meaningful human control, intervention capacity, and institutional accountability. Governance recalibration capacity is specified as the mechanism through which institutions can detect emerging divergence and adjust governance arrangements.

Discussion: The study conceptualises educational AI governance as a dynamic alignment problem requiring continuing correspondence between evolving AI capability, substantive decision influence, and institutional governance. It thereby extends educational leadership analysis from the existence of AI governance toward its continuing substantive adequacy.