Ethical Readiness for Responsible AI in Higher Education

A Pre-deployment Institutional Governance Framework

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.