Jun 2026· Journal of Ethics in Higher Education· 0 citations· 28 references
Abstract
The rapid integration of Artificial Intelligence (AI) across higher education institutions has generated substantial governance challenges concerning accountability, transparency, oversight, and responsible deployment. While existing Responsible AI governance frameworks provide normative principles for ethical AI practice, a persistent gap remains between institutional governance commitments and the governance capability required to operationalise those commitments effectively. This paper introduces Ethical Readiness as a governance-precondition framework designed specifically for higher education environments. Ethical Readiness is defined as the institutional governance condition in which accountability structures, governance ownership mechanisms, oversight capability, transparency preparedness, corrective governance capacity, governance adaptability, and governance proportionality are sufficiently developed to support Responsible AI deployment before operational implementation occurs. Adopting a conceptual-theoretical research design, the paper synthesises scholarship across Responsible AI governance, higher education governance, organisational readiness, governance maturity, and institutional legitimacy theory. The framework is operationalised through eight interdependent governance dimensions and a seven-stage governance lifecycle model. Institutional applicability is illustrated through comparative analysis of five AI deployment contexts common in higher education, supported by evidence from emerging university governance initiatives. The study contributes to Responsible AI governance scholarship by introducing governance preparedness as a distinct analytical construct, differentiating Ethical Readiness from governance maturity, organisational readiness, and Responsible AI frameworks, and providing a structured governance architecture capable of supporting institutional reflection and future empirical investigation.
Artificial Intelligence (AI) is transforming organisational operations and strategic decision-making, but its rapid adoption has outpaced the development of governance mechanisms capable of supporting sustainable growth and societal outcomes. Organisations face increasing challenges in translating ethical principles and regulatory requirements into actionable governance processes that align with business strategy and stakeholder expectations. 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. The proposed framework integrates strategic alignment, risk management, governance-by-design, organisational capability, continuous monitoring, and legal assurance into a structured and repeatable model. By positioning AI governance as a strategic capability rather than a compliance function, the framework provides a practical roadmap for organisations to embed responsible AI practices that enhance transparency, accountability, and trust. The study contributes to the field of human factors in business management by demonstrating how governance can enable sustainable organisational performance while supporting broader societal impact.
Artificial Intelligence (AI) governance has emerged as a critical organisational and board-level concern as AI systems become increasingly embedded in business operations and decision-making. Although existing AI maturity models assess technological capability and deployment readiness, they provide limited mechanisms for evaluating governance effectiveness, accountability, and board oversight. Consequently, organisations lack structured approaches for assessing whether AI governance practices are achieving their intended objectives.
This study addresses this gap through the development of an AI Governance Capability Maturity Model that reconceptualises AI governance as a measurable organisational capability. Using a qualitative integrative synthesis of regulatory frameworks, legal doctrine, governance standards, and academic literature, the study identifies key governance mechanisms and integrates them within a six-phase governance framework. These governance phases are subsequently transformed into a five-level maturity model supported by a multi-dimensional measurement architecture comprising input, process, output, and outcome metrics.
The analysis demonstrates that existing maturity models focus primarily on AI deployment capability, while governance-oriented frameworks emphasise operational controls but provide limited support for performance evaluation, strategic governance, and board-level accountability. To address these limitations, the proposed model links governance processes to measurable indicators and maturity levels, enabling organisations to assess governance effectiveness, identify capability gaps, and monitor continuous improvement.
The study contributes to theory by positioning AI governance as a dynamic organisational capability rather than a collection of compliance activities. It contributes to practice by providing a structured framework that supports governance assessment, performance monitoring, and board oversight. The model aligns with emerging governance expectations reflected in the NIST AI Risk Management Framework, ISO/IEC 42001, and the European Union Artificial Intelligence Act.
By integrating governance processes, capability development, maturity assessment, and performance measurement, the proposed framework provides a practical and theoretically grounded approach for advancing responsible AI adoption and strengthening board-level governance oversight.
P. Wong· International journal of res...· 0 citations
Artificial intelligence (AI) has moved from being a specialized technical tool used with computers to reshaping teaching, administration, and strategic decision-making in higher education. While a substantial body of research addresses the technical deployment of AI and its ethical implications separately, few studies integrate strategic leadership, governance, ethical practice, and institutional readiness into a single, actionable framework, which is the gap this review addresses. The present study aims to develop a value-based leadership framework for the ethical governance of AI in higher education settings. A systematic literature review was undertaken based on 50 peer-reviewed studies published between 2021 and 2025, in accordance with PRISMA 2020 guidelines. Thematic synthesis employed to analyze findings across three dimensions: strategic leadership, governance and ethical challenges, and evidence-based opportunities. The review identifies persistent governance challenges, including algorithmic bias, data privacy, and academic integrity risks, alongside leadership competencies and institutional readiness factors that critically determine whether AI adoption strengthens or diminishes institutional trust. A proposed five-phase framework based on the review findings consists of Vision and Foundation, Capacity Building, Governance in Action, Evaluation and Learning, and Refinement and Iteration, which integrates transformational, distributed, and ethical leadership theories, while offering a practical sequence institutions can use to move from reactive to proactive AI governance. 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
Purpose: The study aims to examine how Artificial Intelligence (AI) mechanisms, governance responses, and human and institutional outcomes are represented within the higher education literature and to explore their implications for sustainability capability development.
Design/Methodology/Approach: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided systematic review examined 91 peer-reviewed articles published between 2020 and 2025.
Findings: The review identifies transparency, explainability, human oversight, accountability, proportionality, co-design, and appeal mechanisms as core governance principles. These are integrated into the AI Mechanisms, Institutional and Human Outcomes, Moderating Governance Responses, and Sustainability Capability Development (AIMS) AI-Human Framework.
Originality/Value: The study connects AI governance, human judgement, institutional responsibility, and Sustainable Development Goals (SDGs)-aligned capability development in higher education.
Keywords: Artificial Intelligence; Higher Education; Governance; Human Judgement; Sustainability Capability Development; Systematic Review.
Citation: Anthuvan, T., Prabhuram, S and Rathod, C. (2026): Human-centred AI governance and sustainability capability development in higher education: the AIMS AI-Human Framework. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 5, pp. 461.
T. Anthuvan, Sunitha Prabhuram, Chirag B. Rathod· World Journal of Entrepreneu...· 0 citations
Artificial intelligence (AI) is transforming business, government, and society at a pace that exceeds the development of governance frameworks. This article presents an academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026, examining Indonesia's strategic position in the evolving global AI landscape. The presentation argued that Indonesia should become a rule shaper rather than a rule taker by developing governance frameworks that reflect national and regional priorities instead of relying solely on external regulatory models. Responsible AI requires more than ethical principles; it depends on capable institutions, effective implementation, qualified professionals, and credible regulatory mechanisms. Key challenges identified include fragmented governance, implementation gaps, and operational risks associated with large language models, including prompt injection, hallucination, and data leakage. The presentation further emphasised the importance of independent AI assurance, certification systems, and institutional readiness to support responsible AI adoption across strategic sectors. Building digital talent and governance capability was presented as the essential foundation for reducing implementation gaps and strengthening long-term competitiveness. The presentation concluded that developing national capability while adapting international best practices provides the most appropriate pathway for enabling Indonesia to contribute to the future development of AI governance.
Patrick Rudolf Dannacher Dannacher· Proceeding Jakarta Geopoliti...· 1 citation
Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by higher education institutions (HEIs).
Methodology: The study adopts a conceptual research approach grounded in institutional, human capital, and entrepreneurial ecosystem theories. Relevant literature on AI, entrepreneurship, higher education, governance, and innovation ecosystems is synthesised to construct an integrated conceptual framework.
Results: The framework conceptualises responsible AI entrepreneurship as the intersection of AI capability, entrepreneurial innovation, ethical responsibility, and institutional governance. It identifies four interrelated dimensions: AI capability, entrepreneurial innovation, ethical and governance capability, and institutional readiness, and explains how leadership, pedagogy, technological infrastructure, governance systems, and ecosystem collaboration shape universities’ capacity to foster sustainable AI-driven innovation. Particular attention is given to challenges facing developing economies.
Novelty and Contribution: The study advances higher education scholarship by integrating AI capability development, institutional readiness, entrepreneurship, and ecosystem thinking into a unified conceptual model, providing a foundation for future empirical research on responsible AI entrepreneurship ecosystems.
Practical and Social Implications: The framework offers guidance for policymakers and university leaders seeking to strengthen AI governance, institutional readiness, and ecosystem collaboration. It supports the development of ethical, inclusive, and innovation-oriented higher education systems capable of preparing graduates and entrepreneurs for AI-driven economies.
Oluwatosin Omosolape Omodewu, M. Shokunbi· Elicit Journal of Economics...· 0 citations