Care Ethics for AI Governance in Higher Education

Higher education institutions have adopted artificial intelligence (AI) tools for proctoring, writing detection, student-success prediction, and student support at a pace that has outrun the ethical frameworks meant to govern them. Institutional AI policy has largely borrowed the fairness, accountability, and transparency vocabulary of commercial AI ethics, supplemented by an academic integrity frame focused on student misuse. Empirical studies document harms these frames are poorly equipped to register: automated proctoring that flags Black students and students with darker skin tones at higher rates, AI-text detectors that misclassify non-native English writing as machine-generated, and student-success models that are less accurate for racially minoritized students. This conceptual article introduces the Jones AI Ethics Framework for institutional use. The framework derives five components (Attentiveness, Responsibility, Competence, Responsiveness, and Trust) from Tronto's phases of care and operationalizes each through a corresponding domain of the Multicultural and Social Justice Counseling Competencies (MSJCC) and is positioned alongside UNESCO's (2021) Recommendation on the Ethics of Artificial Intelligence. The article describes how the framework was developed, specifies its contribution relative to existing care-ethics scholarship on AI, and applies it to two evidence-informed illustrative scenarios: algorithmic proctoring and AI-assisted writing detection. Five recommendations for faculty governance bodies, academic leaders, student affairs professionals, and faculty developers are each mapped to the framework's components. Limitations include the framework's conceptual status, its origin in counselor education, and the need for empirical validation.