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.
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 provides a practical and theoretically grounded approach for advancing responsible AI adoption and strengthening board-level governance oversight and contributes to theory by positioning AI governance as a dynamic organisational capability rather than a collection of compliance activities.
P. Wong· International journal of res...· 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
The review identifies transparency, explainability, human oversight, accountability, proportionality, co-design, and appeal mechanisms as core governance principles in the AIMS AI-Human Framework.
T. Anthuvan, Sunitha Prabhuram, Chirag B. Rathod· World Journal of Entrepreneu...· 0 citations
An academic adaptation of Patrick Rudolf Dannacher's presentation at the 10th Jakarta Geopolitical Forum 2026 is presented, examining Indonesia's strategic position in the evolving global AI landscape.
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