IMPLICATIONS OF GENERATIVE AI ON ACADEMIC INTEGRITY, RESEARCH QUALITY, AND PEDAGOGY IN NORTH-WEST NIGERIAN UNIVERSITIES

Abstract

The rapid integration of generative artificial intelligence (AI) tools has transformed higher education, offered efficiency gains while challenged academic integrity, research authenticity, and pedagogical practices. This Institutional-Based Research (IBR)-funded study examines the implications of AI-generated content for academic integrity, research quality, and pedagogy in universities across North-West Nigeria. Employing a convergent mixed-methods design, quantitative data were collected via structured questionnaires from 368 participants (students 63%, lecturers 25%, administrators 12%; 92% response rate) across 6 Universities, supplemented by 20 semi-structured interviews. Descriptive statistics revealed widespread adoption (65% active users overall; 70% among students), with 58% of student users primarily employing AI for writing assignments. Inferential analyses showed a moderate positive correlation between AI literacy and ethical usage (r = .46, p < .001), a negative correlation between AI dependency and perceived integrity (r = −.41, p < .001), and a multiple regression model where literacy and ethical awareness explained 53% of variance in integrity outcomes (R² = .53, f² = 1.13, large effect). Thematic analysis of qualitative data highlighted utility–integrity tensions, literacy as empowerment, institutional silence, and needs for pedagogical adaptation. Grounded in TAM, UTAUT2, TPACK, and Academic Integrity Theory, findings reject prohibition in favor of governance, mandatory literacy integration, assessment redesign, faculty development, and infrastructure investment to address digital divides. Limitations include the cross-sectional design and self-reported biases; future research should pursue longitudinal and intervention studies. These region-specific insights contribute to global discourse on equitable AI governance in Sub-Saharan African higher education.