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Leadership for artificial intelligence use in schools: A six-domain framework for ethical, equitable, and effective integration

David E. DeMatthews et al.

Educational Management Administration & Leadership2026https://doi.org/10.1177/17411432261418940article
AJG 2ABDC B
Weight
0.37

Abstract

This article explores the evolving role of artificial intelligence (AI) in K–12 educational leadership. We argue that AI is not merely a technological innovation but now a leadership imperative that can help schools personalize learning, streamline administrative tasks, and support data analysis and continuous improvement. AI tools will become increasingly embedded in both daily life and school systems, so school leaders must develop new leadership competencies to guide their ethical and effective use. The article introduces a framework of six AI Leadership Domains aligned with the Professional Standards for Educational Leaders, encompassing strategic vision, instructional innovation, equity, and stakeholder engagement. Developed through conceptual synthesis and practitioner validation, the framework draws on theories and research in leadership and technology adoption to provide actionable guidance. The article concludes with implications for policy, leadership preparation, and future research, emphasizing the need for human-centered, equity-driven approaches to AI in education.

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https://doi.org/https://doi.org/10.1177/17411432261418940

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@article{david2026,
  title        = {{Leadership for artificial intelligence use in schools: A six-domain framework for ethical, equitable, and effective integration}},
  author       = {David E. DeMatthews et al.},
  journal      = {Educational Management Administration & Leadership},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/17411432261418940},
}

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Leadership for artificial intelligence use in schools: A six-domain framework for ethical, equitable, and effective integration

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.