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

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Synthesis: Sposato (2025) develops a ten-domain taxonomy of AI applications specifically tailored to educational leadership, filling a gap between AI research and how institutional leaders understand, evaluate, and implement AI. Built from a systematic literature review and inductive analysis of 314 publications (2017–2024), the taxonomy spans 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. The framework gives leaders a common language for strategic AI integration while foregrounding ethical and equity concerns. It is primarily a conceptual/organizing contribution rather than an empirical test of outcomes.

Core Finding

Educational leaders lack a comprehensive, structured framework for categorizing, evaluating, and implementing AI in their institutions, and this study supplies one: a validated ten-domain taxonomy of AI applications in educational leadership. Because AI adoption in higher education is fragmented and poorly understood at the leadership level, leaders need a conceptual map that spans operational automation through ethical governance. The taxonomy is grounded in three theoretical frameworks — transformative leadership theory, the adaptive organizational framework, and an ethical AI implementation framework — and is designed to apply across K 12, Higher Ed, vocational, and continuing education contexts. The study explicitly argues for a balanced approach that leverages technological advances while actively managing Ethics and equity concerns.

The Ten-Domain Taxonomy

The taxonomy organizes the full spectrum of AI in educational leadership into ten interrelated domains, each with key components and practical examples:

Administrative Efficiency — automated scheduling, data-driven decision support, HR/enrollment analytics, budget forecasting, dropout-risk prediction. AI scheduling has been reported to cut administrative workload by up to 40%, and enrollment systems to predict patterns with >85% accuracy.
Personalized Learning — Adaptive Learning platforms, Intelligent Tutoring systems, Learning Analytics, virtual tutors that adjust content difficulty in real time.
Enhancing Teaching Practices — AI in Curriculum Design, teacher professional development, intelligent classroom management, and real-time Feedback on classroom dynamics.
Decision-Making and Policy Formulation — predictive analytics, sentiment analysis, ethical/equity decision support, and bias detection, supporting Educational Policy AI.
Student Support Services — AI-based career counseling, mental health analytics, early warning systems, and 24/7 AI chatbots.
Organizational Leadership and Strategic Planning — strategic resource allocation, trend forecasting, and risk/crisis management.
Governance and Compliance — regulatory compliance monitoring, fraud detection, and data integrity, tied to Governance.
Community Engagement and Communication — communication tools, feedback analytics, and social media monitoring.
Ethical AI Leadership and Governance — bias mitigation, Privacy/data security, and transparent AI-use policies.
Diversity, Equity, and Inclusion (DEI) Initiatives — AI-driven equity audits, inclusive curriculum design, and Special Education support.

Methodological Approach

The study uses a general inductive approach to synthesize literature from IEEE Xplore, ACM Digital Library, ERIC, and Scopus (1,247 papers screened down to 314 for detailed review). Two independent researchers coded publications in NVivo, and the emerging categories were consolidated and validated through three rounds of refinement. The author acknowledges limitations: reliance on published literature may miss the newest developments, and restricting to English-language publications may exclude relevant cross-cultural insights.

Relevance to the Knowledge Base

This article is directly relevant to the knowledge base's coverage of how AI Education is governed and led at the institutional level. It connects several concept clusters that otherwise appear in scattered Generative AI and Personalized Learning articles: Governance, Educational Policy AI, Educational Development, and Administrator roles. For leaders, it reframes AI not as a classroom-only concern but as an institution-wide strategic and ethical matter spanning teaching, research, and service. It also complements empirical adoption studies by providing the organizational Scaffolding those studies implicitly assume.

Connected Concepts

Connected Articles

Citation

Sposato, M. (2025). Artificial intelligence in educational leadership: a comprehensive taxonomy and future directions. International Journal of Educational Technology in Higher Education.

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