A six-element governance framework is developed comprising legitimate purpose and proportionality; explicit allocation of roles and responsibility; traceable data, evidence, and uncertainty; competent human oversight and calibrated reliance; stakeholder participation, contestability, and redress; and continuous monitoring, audit, and institutional learning.
Abstract
This conceptual article examines the conditions under which AI-assisted decisions in education and public governance can strengthen institutional capacity without displacing human judgement, agency, or accountability. It employs a purposive conceptual synthesis of interdisciplinary scholarship and legal and policy materials and compares governance approaches in the European Union, the Republic of Korea, and the United States with regard to legal force, risk classification, human oversight, transparency, contestability, and institutional capacity. Rather than treating the technical system alone as the unit of ethical analysis, the article focuses on the AI-assisted decision episode: the sequence through which data, model outputs, human judgement, and institutional authority combine to affect a learner, citizen, or community. On this basis, it develops a six-element governance framework comprising legitimate purpose and proportionality; explicit allocation of roles and responsibility; traceable data, evidence, and uncertainty; competent human oversight and calibrated reliance; stakeholder participation, contestability, and redress; and continuous monitoring, audit, and institutional learning. Applied to educational assessment and public-service decisions, the framework demonstrates that a nominal human-in-the-loop is insufficient unless reviewers possess the competence, time, authority, alternative evidence, and records necessary to challenge model outputs. The article’s contribution lies in connecting legal safeguards, organisational capacity, and cognitive risks within a process-based model of human-centred AI. The framework is conceptual and requires empirical validation. Human-centred AI ultimately depends not only on technical accuracy but also on a decision architecture that preserves agency, provides effective remedies, and keeps responsibility visible.
. AI ethics has recently emerged as a dominant governance paradigm, increasingly implemented through institutionalised, specialised and expert-driven tools and mechanisms, thereby compelling us to revisit the question of democratic deficit . This ethics-oriented technocratic approach is exemplified by the EU AI Act (2024), which promotes soft-law tools as part of a layered, risk-based governance framework. This article critically examines whether that model can address the democratic deficit in AI governance, focusing on harmonised standards, voluntary codes of conduct and regulatory sandboxes . Drawing on democratic theory and governance scholarship, it explores the extent to which these instruments advance participation, deliberation and accountability. By assessing these mechanisms as key sites of governance, the article concludes that they prioritise flexibility, technical expertise and market integration over public contestation and participation, offering little response to the democratic deficit. It argues that AI ethics can address this deficit only when complemented by institutional reforms that embed deliberation, broaden participation and ensure meaningful public accountability, including the involvement of non-expert citizens.
The study proposes a phased, ethically grounded governance framework tailored to Africa’s educational context, contributing new insights into readiness differentials, governance diffusion, and policy convergence, offering a foundation for inclusive, future-oriented AI policy in African higher education.
Dr. Sixbert Sangwa, Dennis Ngobi, Emmanuel Ekosse et al.· Artificial Intelligence and...· 11 citations· ⚡1
Artificial intelligence is increasingly used in public administration to classify individuals, assess risks, prioritize cases, support eligibility determinations and guide the allocation of public resources. In the European Union, these uses are governed by the Artificial Intelligence Act, the GDPR and the Charter of Fundamental Rights. Formal compliance, however, does not by itself ensure lawful and accountable administration. AI relocates discretion from the visible act of decision-making to less visible choices concerning data, model design, procurement, thresholds and interface architecture. This Policy and Practice Review therefore treats human-centric AI governance not as a general ethical aspiration, but as an administrative and constitutional framework for governing public power. Drawing on EU law, public administration scholarship and a comparative institutional analysis of selected Member State practices, it develops six interdependent dimensions: legal anchoring, accountable discretion, fundamental rights by design, meaningful human oversight, contestability and justification, and institutional resilience. The analysis shows that common EU rules may produce unequal levels of protection where public authorities differ in technical expertise, audit capacity, procurement independence and access to effective remedies. It also argues that accountability must follow the chain of influence through which algorithmic systems shape administrative outcomes, rather than only the formal chain of decision-making. The article translates this framework into actor-specific recommendations concerning fundamental rights impact assessments, procurement, auditability, human oversight, transparency, contestability and post-deployment monitoring. It concludes that AI-enabled administration remains legitimate only where public authorities retain the capacity to understand, justify, correct, suspend and democratically control the systems they use.
A. Dragomir, Iulea Bulea, Lucian Tarnu· Frontiers in Political Scien...· 1 citation
The Ethico-Regulatory Governance (ERG) Framework is proposed, a conceptual model designed to bridge global ethics with local compliance, and offers a scalable, adaptable solution for universities navigating the complexities of GenAI.
Christian Roberto Cabezas Freire, Nayana Desai· Revista Hambatu Science· 0 citations
The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.
Asem S. Obied, Ahmed Raja Haj Ali· Frontiers in Education· 0 citations
Generative artificial intelligence (AI) complicates academic integrity by blurring the boundary between assistance and authorship, enabling cognitive delegation, and introducing algorithmic mediation into assessment and institutional decisions. This article presents a critical conceptual synthesis, not a systematic review. A purposive corpus of 64 academic and policy sources was examined through comparative coding, negative-case analysis, source-to-concept tracing, and normative interpretation. The synthesis distinguishes integrity of academic work, integrity of assessment, and institutional procedural integrity. It proposes an integrated governance framework connecting preventive governance, ethical AI literacy, governed verification, restorative accountability, and proportionate discipline. The framework argues that policy clarity must reach the assessed task; AI literacy supports judgement but cannot neutralize strategic misconduct; automated indicators require corroboration, competent human review, reasons, and appeal; and restorative processes are appropriate only when harm, affected parties, voluntary participation, responsibility, repair, and reintegration are substantively addressed. Its principal contribution is a case-to-governance feedback mechanism through which integrity cases generate institutional learning and trigger revision of policy, assessment design, literacy provision, and technology oversight. Seven testable conceptual propositions, a response-selection pathway, role-specific duties, staged implementation responsibilities, and evaluation indicators are provided. The framework remains a normative and empirically testable proposal rather than a validated intervention or universally effective solution.
Gabriel Bădescu, Mihai Susinski, Cristian Vasile et al.· Education sciences· 0 citations
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