Artificial intelligence in educational leadership: a comprehensive taxonomy and future directions

Abstract

Abstract Educational institutions worldwide face mounting challenges in effectively integrating artificial intelligence (AI) technologies into their operations, largely due to the absence of comprehensive frameworks for evaluation and implementation. This systemic gap has led to fragmented adoption practices, missed opportunities for innovation, and potential risks in deployment of AI solutions. The rapid proliferation of AI technologies in higher education has created significant challenges for institutional leaders who must balance technological advancement with educational outcomes, ethical considerations, and resource constraints. This study addresses these critical challenges by developing a comprehensive taxonomy of AI applications in higher education leadership. Through a systematic literature review and inductive analysis of publications from 2017 to 2024, the research synthesizes diverse AI applications into ten distinct domains: Administrative Efficiency, Personalized Learning, Enhancing Teaching Practices, Decision-Making and Policy Formulation, Student Support Services, Organizational Leadership and Strategic Planning, Governance and Compliance, Community Engagement and Communication, Ethical AI Leadership, and Diversity, Equity, and Inclusion Initiatives. The resulting taxonomy, validated across various higher education contexts, provides educational leaders with a structured framework for understanding, evaluating, and implementing AI solutions in their institutions. This study contributes to the field by offering a common language and conceptual framework for researchers, policymakers, and practitioners, while also identifying critical areas for future research. The findings underscore the transformative potential of AI in higher education and the need for a balanced approach that leverages technological advancements while addressing ethical considerations and equity issues.