Human-centred AI governance and sustainability capability development in higher education: the AIMS AI-Human Framework

AI-enabled governance in higher education: a systematic review of applications, outcomes, and emerging implications

This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.

Governance of inclusive decision-making in AI adoption

Artificial intelligence (AI) is revolutionizing higher education through personalized learning and data-driven administration. However, effective governance remains an essential issue for AI adoption, particularly in developing countries such as Morocco, where research is still limited. This study examines the role of governance structures, stakeholder engagement, and institutional preparedness in AI adoption within Moroccan universities through a systematic literature review of 37 peer-reviewed articles from Scopus (2020-2026), using PRISMA guidelines and thematic synthesis. Findings reveal a surge in AI studies since 2024, mostly conceptual and from Global North countries, highlighting multi-level, ethical, participatory, and anticipatory governance models. Stakeholder participation remains mostly top-down, with minimal faculty and student engagement, while readiness comprises infrastructure, literacy, leadership, and policy frameworks. Morocco has centralized control through Law 59.24 and Maroc AI 2030, but lacks context-specific studies. The study offers the Inclusive AI Governance-Readiness Framework, connecting institutional coordination, stakeholder engagement, readiness, and ethics for sustainable AI integration.

Journal of Perspectives in Management

Human-Centred AI in Education and Governance: Ethical Challenges of Digital Transformation

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.

Anticipatory governance and leadership for AI implementation in higher education: A scoping review

The ability of institutions to leverage opportunities to transform governance in higher education depends on adopting anticipatory governance models that emphasize foresight and stakeholder engagement, as well as adopting changes to the traditional role of both leaders and educators to become data literate, inclusive, collaborative, and forward-thinking.

Human-Centred Governance for Responsible AI Adoption: Enabling Sustainable Business Transformation and Societal Value

This study develops a six-phase human-centred governance framework for responsible AI adoption through an integrative synthesis of academic literature, international standards, and regulatory frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the European Union Artificial Intelligence Act.