Aug 2026· Frontiers in Education· 0 citations· 45 references
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.
The ability of institutions to leverage opportunities to transform governance in higher education depends on adopting anticipatory governance models that emphasize foresight and stakeholder engagement, as well as adopting changes to the traditional role of both leaders and educators to become data literate, inclusive, collaborative, and forward-thinking.
S. Baroudi· International Journal of Edu...· 1 citation
The integration of Artificial Intelligence (AI) in higher education is reshaping institutional decision-making, governance, policy development, and educational innovation. Effective leadership in AI-enabled environments requires more than technical competence, extending to strategic awareness, ethical judgement, and the ability to critically evaluate the broader implications of AI technologies. Drawing on survey and interview data from 52 academic leaders across UK higher education institutions, this study examines current levels of AI literacy and explores how AI capability relates to institutional readiness and leadership practice. The findings reveal variation in participants’ self-reported AI literacy and engagement, identify technical, strategic, ethical, and organisational capability gaps, and highlight structural barriers including limited time, fragmented professional development, and insufficient institutional support for institution-wide AI adoption. In response, the paper presents the AI Literacy Leadership Framework (AILLF), which conceptualises AI literacy as a multidimensional leadership capability comprising technical, strategic, ethical, and applied dimensions that support four interconnected leadership domains: innovation, decision-making, ethical governance, and policy development. Informed by leadership theory, international AI governance frameworks, and the study’s exploratory empirical findings, the AILLF is accompanied by a proposed capability progression model and role-differentiated leadership competency guide to provide implementation guidance for higher education institutions. The study contributes an empirically informed conceptual framework that integrates AI literacy, leadership theory, and AI governance, providing a foundation for future research and institutional approaches to AI leadership within higher education.
Alaa Mohasseb, Ronel Beukman, Andreas Kanavos· Applied Informatics· 0 citations
The integration of AI in education is transforming schools and the work of educational leaders. However, AI adoption also raises ethical concerns and presents substantial challenges that school leaders should consider. This systematic review examines research on the use of AI in K-12 school leadership, focusing on four key areas: (1) ethical considerations, (2) challenges to AI adoption, (3) perceived benefits, and (4) practical applications in leadership practices. We followed a systematic narrative review approach, analyzing peer-reviewed literature from WoS, Scopus, ERIC, and Google Scholar. 26 articles met the inclusion criteria and were included in the review. The results revealed that while AI offers promising benefits for educational leaders, such as enhancing decision-making, efficiency, and dealing with time-consuming administrative tasks, its adoption remains limited due to a range of challenges, including a lack of AI literacy, inadequate professional development, data privacy and fairness concerns, misinformation, the lack of capacity for emotional judgment, as well as access disparities. Despite these obstacles, evidence suggests that some leaders are incorporating AI into administrative tasks, yet often without clear guidelines. Implications for policy, practice, and future research have been discussed.
M. Bellibaş, Figen Karaferye· Improving Schools· 0 citations
This conceptual paper argues that traditional technology governance models are insufficient and that leaders must develop an augmented moral compass to navigate algorithmic decision-making, data governance, and equity and proposes an original Moral Compass Model.
This study synthesizes fragmented research on artificial intelligence (AI) in higher education governance and identifies key gaps for future research and policy and provides a useful lens for interpreting institutional adaptation.
Xinyi Jiang, Zuraidah Abdullah· Frontiers in Education· 2 citations
Generative Artificial Intelligence (GenAI) is reconfiguring authority, accountability, and legitimacy in organizational leadership. This mixed-methods study integrates survey data from 542 leaders across health care, finance, government, education, and nonprofits with twenty-two interviews to examine how digital literacy and ethical infrastructure shape trust in AI-mediated decision systems. Findings show that 77% of respondents now incorporate GenAI into leadership decisions, reflecting a shift from hierarchical control to distributed, participatory orchestration. Ethics policies (β = .48, p < .001) and digital literacy (β = .42, p < .001) significantly predict trust in GenAI governance. Interview evidence demonstrates that confidence relies less on technical accuracy than on the institutionalization of ethics, transparent oversight, clear decision rights, bias audits, and mechanisms for contestation and redress. Sectoral contrasts illustrate how power shapes these safeguards, determining who is protected, who bears risk, and who can influence AI-enabled decisions. The study reframes leadership in the algorithmic age as the design of accountable, adaptive, and equitable decision architectures. Leaders who pair ethical governance with digital fluency are best positioned to sustain legitimacy and justice as GenAI becomes embedded in organizational life.
Ajmal Aminee· Organizational Cultures An I...· 0 citations