Aug 2026· Applied Informatics· Vol 7, pp. 328· 0 citations· 47 references
TL;DR
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.
Abstract
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.
The increasing use of artificial intelligence (AI) and generative artificial intelligence (GenAI) is reshaping teaching, assessment, curriculum development, research, academic administration and professional learning within higher education. Yet existing approaches to AI literacy, competence, self-efficacy, adoption and ethics remain conceptually dispersed, limiting a coherent understanding of faculty preparedness for responsible AI-mediated professional practice. This paper addresses this gap by conceptualising Responsible AI Readiness (RAIR) as a higher-order professional capability that integrates AI knowledge and competence, self-efficacy, ethical judgement, pedagogical preparedness, human agency, privacy and fairness within enabling institutional conditions. Drawing on a critical integrative review of conceptual, empirical, measurement, policy and competency-framework literature, the paper develops a multilevel architecture comprising seven faculty-level dimensions: AI Knowledge and Literacy, AI Self-Efficacy, Responsible and Ethical AI Awareness, Human Agency and Professional Autonomy, AI Pedagogical Readiness, Data Privacy and Security Awareness and Bias and Fairness Awareness. These capabilities are situated within two contextual conditions- Institutional AI Support and Institutional AI Governance Readiness, thereby connecting individual capability with the organisational environments within which AI-mediated professional decisions occur. The framework advances a measurement-oriented conceptualisation of RAIR as a potentially testable higher-order construct and proposes a pathway towards scale development, psychometric validation, multimethod assessment, measurement invariance and subsequent multilevel modelling. Its principal contribution is to move the discourse beyond AI literacy and technology adoption towards integrated competence, responsible professional judgement and institutionally enabled readiness. The framework further provides an India-contextualised perspective connecting faculty AI capability with responsible innovation, human-capital development and the Viksit Bharat@2047 vision while offering a transferable conceptual basis for research on human-centred and sustainable AI transformation in higher education.
Unknown authors· International Journal For Mu...· 0 citations
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.
Asem S. Obied, Ahmed Raja Haj Ali· Frontiers in Education· 0 citations
This article examines strategic leadership in the age of artificial intelligence by emphasizing the need to balance machine intelligence with human insight. Drawing from Halima Idris's conference material and supported by contemporary literature on digital leadership, AI governance, ethics, and organizational transformation, the article argues that AI strengthens leadership through evidence-based decision-making, forecasting, fraud detection, educational innovation, healthcare support, and improved governance. However, AI cannot replace human qualities such as ethical judgment, empathy, accountability, cultural understanding, integrity, and wisdom. The study employs a conceptual literature-based method to synthesize key ideas on AI-enabled leadership and responsible decision-making. The discussion shows that effective leaders should use AI as a decision-support instrument while retaining responsibility for final decisions and social consequences. The article recommends that governments develop responsible AI policies, universities integrate AI literacy with ethics and critical thinking, and organizations build transparent AI practices while reskilling their workforce. The article concludes that sustainable development in the AI era requires hybrid leaders who combine technological competence with human values.
Halima Idris· IC-BESTS: International Conf...· 0 citations
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...· 2 citations
Artificial intelligence (AI) is reshaping decision-making and organizational life, yet leadership development in higher education has not evolved at the same pace. To address this gap, the article proposes the concept of the "augmented leader" and develops a four-competency framework for AI-integrated leadership: algorithmic literacy, strategic use of AI, ethical leadership and governance, and culture and adoption. Based on an integrative literature review and expert-informed thematic synthesis, the framework offers a structured perspective on leadership in AI-mediated environments. The article argues that higher education must embed AI as a core leadership capability, preparing students to lead effectively and responsibly in human-AI systems.
David López-López, Patricia Rodriguez Garcia, Miguel Saiz Garcia et al.· New Directions for Student L...· 0 citations
Purpose: This paper develops a theory-informed conceptual framework for responsible AI entrepreneurship ecosystems in higher education. It addresses the limited integration of AI capabilities, entrepreneurship, institutional readiness, and governance in existing scholarship, particularly amid increasing AI adoption by higher education institutions (HEIs).
Methodology: The study adopts a conceptual research approach grounded in institutional, human capital, and entrepreneurial ecosystem theories. Relevant literature on AI, entrepreneurship, higher education, governance, and innovation ecosystems is synthesised to construct an integrated conceptual framework.
Results: The framework conceptualises responsible AI entrepreneurship as the intersection of AI capability, entrepreneurial innovation, ethical responsibility, and institutional governance. It identifies four interrelated dimensions: AI capability, entrepreneurial innovation, ethical and governance capability, and institutional readiness, and explains how leadership, pedagogy, technological infrastructure, governance systems, and ecosystem collaboration shape universities’ capacity to foster sustainable AI-driven innovation. Particular attention is given to challenges facing developing economies.
Novelty and Contribution: The study advances higher education scholarship by integrating AI capability development, institutional readiness, entrepreneurship, and ecosystem thinking into a unified conceptual model, providing a foundation for future empirical research on responsible AI entrepreneurship ecosystems.
Practical and Social Implications: The framework offers guidance for policymakers and university leaders seeking to strengthen AI governance, institutional readiness, and ecosystem collaboration. It supports the development of ethical, inclusive, and innovation-oriented higher education systems capable of preparing graduates and entrepreneurs for AI-driven economies.
Oluwatosin Omosolape Omodewu, M. Shokunbi· Elicit Journal of Economics...· 0 citations
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