ORIGINAL RESEARCH article
Front. Educ.
Sec. Higher Education
This article is part of the Research TopicArtificial Intelligence in Educational Technology: Innovations, Impacts, and Future DirectionsView all 11 articles
Strategic Leadership in Higher Education: Navigating the Impact of Artificial Intelligence on University Governance, Teaching, and Research
Provisionally accepted
- University of Delta, Agbor, Nigeria
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This study investigates the transformative impact of artificial intelligence (AI) on higher education, focusing on its potential to enhance instructional delivery, automate administrative tasks, and expand research opportunities. An analytical descriptive survey design was employed, targeting academic, non-academic, and managerial staff from 21 public universities in Nigeria's South-South Geopolitical Zone. A sample of 800 participants was selected through a stratified random sampling method, including 400 academic staff, 350 non-academic staff, and 50 management staff. The study was guided by three research questions and hypotheses, utilizing the "Artificial Intelligence in University Leadership Questionnaire (AIULQ)", a vetted 20-item instrument with a reliability coefficient of 0.81 obtained via Cronbach's alpha. Data analysis involved calculating mean and standard deviation for research questions, while hypotheses were tested at the 0.05 alpha level using One-Way Analysis of Variance (ANOVA). Findings reveal that AI enhances data-informed decision-making, supports virtual academic advising, and promotes adaptive learning platforms. The study also addresses ethical issues, including algorithmic bias, data privacy, academic integrity, and digital divide. It recommends that universities formulate clear guidelines for the application of AI in research, academia, and administration. The insights from this research contribute valuable information for stakeholders, educators, university leaders, and policymakers in educational reform.
Keywords: Artificial, Intelligence, University, Leadership, Managing
Received: 29 Sep 2025; Accepted: 28 Nov 2025.
Copyright: © 2025 Ossai, Okokoyo and Asabor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Anthony Ossai
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