Strategic leadership for ethical AI integration in higher education: a systematic review of challenges and opportunities
TL;DR
The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.
Abstract
Artificial intelligence (AI) has moved from being a specialized technical tool used with computers to reshaping teaching, administration, and strategic decision-making in higher education. While a substantial body of research addresses the technical deployment of AI and its ethical implications separately, few studies integrate strategic leadership, governance, ethical practice, and institutional readiness into a single, actionable framework, which is the gap this review addresses. The present study aims to develop a value-based leadership framework for the ethical governance of AI in higher education settings. A systematic literature review was undertaken based on 50 peer-reviewed studies published between 2021 and 2025, in accordance with PRISMA 2020 guidelines. Thematic synthesis employed to analyze findings across three dimensions: strategic leadership, governance and ethical challenges, and evidence-based opportunities. The review identifies persistent governance challenges, including algorithmic bias, data privacy, and academic integrity risks, alongside leadership competencies and institutional readiness factors that critically determine whether AI adoption strengthens or diminishes institutional trust. A proposed five-phase framework based on the review findings consists of Vision and Foundation, Capacity Building, Governance in Action, Evaluation and Learning, and Refinement and Iteration, which integrates transformational, distributed, and ethical leadership theories, while offering a practical sequence institutions can use to move from reactive to proactive AI governance. The framework demonstrates that the sustainable value derived from AI in higher education depends less on the level of the technology adopted than on the ethical bases and consistency of the leadership responsibility for its integration, offering higher education leaders and policymakers a structured path toward responsible AI governance and sustainable institutional transformation.