Governing Artificial Intelligence for Academic Renewal in Nigerian Higher Education

Abstract Artificial intelligence has entered Nigerian higher education through the side door. It is already in students’ phones, lecturers’ drafts, postgraduate literature searches, coding exercises, translation work, slide preparation, plagiarism anxieties, and administrative shortcuts. Many universities are still discussing AI as if it were a future policy choice, but the real situation is less tidy: use has begun before most institutions have settled the academic rules, trained staff, protected student data, redesigned assessment, or decided where human judgment must remain final. This study examines that problem from the standpoint of university responsibility. Nigerian higher education does not need AI enthusiasm for its own sake. It needs a disciplined way to decide where AI can improve teaching, research, access, feedback, administration, and national skills development without weakening the degree, exposing students, deepening inequality, or turning academic work into machine-assisted imitation. The paper reads Nigerian policy and institutional evidence alongside international guidance on AI risk, education, data protection, and quality assurance. National AI ambition, digital learning policy, CCMAS curriculum reform, JAMB’s data-supported admissions system, NOUN’s distance-learning experience, TETFund’s TERAS platform, 3MTT, the Nigeria Data Protection Act, UNESCO guidance, NIST risk-management work, ISO/IEC 42001, and connectivity evidence are treated as signals of direction, not proof that campus practice is already mature. The paper proposes the AI Higher Education Readiness and Safeguards Score as a planning instrument for universities. Its purpose is not to rank institutions or produce false precision. It helps leaders examine eight areas that now decide whether AI use is responsible: governance authority, faculty preparation, data protection and infrastructure, assessment integrity, research capacity, equity and access, quality assurance evidence, and procurement control. The argument is direct. Nigerian universities should adopt AI where it strengthens learning and research. They should resist it where it replaces authorship, hides weak teaching, exploits student data, rewards privilege, or places academic authority in the hands of vendors. The future question is not whether AI belongs in the university. It is whether Nigerian universities can make AI serve the university’s academic mission rather than the other way around. Keywords: artificial intelligence; Nigerian higher education; AI governance; digital learning; academic integrity; data protection; research ethics; curriculum reform; faculty development; AI-HERS.