Who Can Change Course? An Integrative Review of AI Sovereignty and Collective Learning Capacity

Abstract

AI sovereignty is commonly associated with control over data, infrastructure and technological dependencies. This integrative review examines how institutions convert those resources into capabilities that remain accountable and open to revision. It brings together 72 references on digital sovereignty, absorptive capacity, innovation systems and sociotechnical governance, using six reading dimensions: subject, object, scale, mechanism, legitimacy and evidence. The synthesis proposes five conditions of collective learning capacity: recognizing and retaining knowledge, transmitting it across institutions, protecting consequential challenge, authorizing correction and preserving feasible alternatives. Authoritative corrigibility connects corrective evidence to decision authority; retained option value concerns the practical alternatives that survive a technological commitment. Comparisons of the United States, China and France within Europe distinguish resources and formal arrangements from documented operation and corrective outcomes. Building on this review, Human-Centered Learning Mechanics (HCLM) organizes a descriptive analysis of OECD, IMF and World Bank indicators around information resources, absorption and institutional coordination. The indicators show different resource profiles and trajectories, while leaving the operation of correction and feasible exit largely unmeasured. HCLM connects these observations to questions for institutional investigation; its national-level parameters are not estimated. Purposeful source selection, uneven case coverage and aggregate measurement constrain the conclusions. The resulting research agenda combines reproducible indicator analysis with decision-level evidence of learning, correction and retained choice.