Data Privacy Dominance: An Empirical Investigation into Nigerian Postgraduate Students' Prioritization of AI Ethical Concerns in Higher Education

Abstract – The integration of Artificial Intelligence (AI) into Higher Education Institutions (HEIs) promises significant pedagogical and administrative efficiencies, yet it concurrently introduces profound ethical dilemmas, particularly in data-rich environments. The study empirically investigates the prioritization of major AI ethical concerns—Data Privacy, Algorithmic Bias/Fairness, Transparency, and Accountability—among Nigerian postgraduate students. Utilizing a quantitative survey with 300 strategically selected postgraduate students across 2 federal universities and 2 state universities, data were collected using a structured questionnaire. The study, anchored in the Ethical Data Governance Framework (EDGF), addressed the question of what the major ethical concerns are and tested two null hypotheses on the significant differences in awareness and the prioritization of these concerns. Descriptive statistics, non-parametric Friedman Test, and Independent Samples t-test were employed for analyses. Although the findings revealed a high level of overall awareness regarding AI ethical implications (X̄ = 3.12), there is a significant difference in the awareness of ethical implications between students who have encountered AI applications (X̄ = 3.29) and those who have not (X̄ = 2.65). The Friedman Test and Independent Samples t-test unequivocally demonstrated a significant statistical difference in prioritization (Friedman, X² = 12.34, p-value < 0.05; t-value of 4.10, p-value < 0.05), leading to the rejection of H_O2. Data Privacy emerged as the overwhelmingly dominant ethical concern with a Weighted Mean Score (WMS) = 3.55, followed by Transparency (WMS = 3.38), reflecting a deep-seated trust deficit in institutional data stewardship and a strong student demand for Explainable AI (XAI). Students who have encountered AI applications demonstrated significantly higher awareness (X̄ = 3.29) and significantly higher overall prioritization (X̄ = 3.33) of ethical concerns compared to those who have not. This paper recommends that Nigerian HEIs must urgently adopt the EDGF principles by implementing stringent data privacy and transparency protocols with robust procedural tools to address data protection and enforce algorithmic accountability. This should be coupled with experiential AI training to foster trust and ensure the responsible adoption of AI in Nigerian higher education.

Artificial Intelligence, AI, Data Privacy, Higher Education, Ethical Concerns, EDGF, Postgraduate Students, Nigeria, Bias, Transparency, Accountability, Prioritization

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