When AI Capability Outpaces Governance: Governance–Capability Misalignment in Educational Leadership

Abstract

This study extends AI Governance–Capability Misalignment (AI-GCM) into educational leadership and AI-mediated institutional decision-making. It examines how governance arrangements that were initially appropriate can become misaligned when AI capabilities and their substantive influence over institutional decisions evolve without corresponding governance adjustment. The study distinguishes AI Capability Configuration (ACC), Actual AI Decision Influence (ADI), and Educational Governance Configuration (EGC) to explain why formal decision authority may remain with educational leaders even as AI-generated predictions, recommendations, rankings, and other outputs acquire increasing influence over decision formation. It conceptualises educational governance–capability misalignment as a dynamic condition arising when material changes in AI capability and actual decision influence are not matched by sufficient changes in institutional governance. The theoretical model identifies three principal governance consequences: leadership authority divergence, weakening of meaningful human control, and accountability–control incongruence. It further develops Governance Recalibration Capacity (GRC) as the corrective mechanism through which educational institutions can detect governance-relevant changes and adjust decision rights, oversight, intervention, escalation, traceability, and accountability arrangements. Using a structured integrative conceptual review and theory-extension approach, the study integrates literature on educational AI governance, educational leadership, human–AI decision-making, meaningful human control, accountability, and adaptive governance. The resulting framework shifts attention from whether AI governance exists toward whether governance continues to correspond substantively to what AI can do and how much influence it actually exercises within educational decision processes.