Incorporate AI literacy into educational-leadership preparation curricula¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article argues that schools of education and teacher training programs should embed AI literacy in leadership preparation, so future leaders can both use AI tools and critically evaluate their effectiveness, ethical implications, and impacts on teaching and learning. It states future educational leaders "will need to understand how to use AI tools and how to critically evaluate their effectiveness, ethical implications, and potential impacts on teaching and learning," and proposes the taxonomy as a framework for developing courses or modules on AI in educational leadership.

Design Implications¶

Context¶

Requirements¶

• Training programs should cover the full spectrum of AI applications in education as organized by the taxonomy

Constraints¶

Target Learners¶

• aspiring educational leaders
• students in leadership preparation programs

Target Learning Goals¶

• AI literacy for educational leadership
• critical evaluation of AI tools and their ethical implications

Affordances¶

• Ten Domain Taxonomy Ai Educational Leadership

Related Strategies¶

• Embed AI literacy across disciplines by identifying intersections with existing content and using case studies, interdisciplinary modules, and collaborative projects
• Embed GenAI literacy and ethical use directly in nursing curricula rather than prohibiting it
• Integrate AI literacy into health sciences curricula and tailor career counselling to programme type
• Build ethical AI integration on four coordinated levels: faculty professional development, ethics curricula, clear institutional guidelines, and interdisciplinary updating
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Higher education leaders should develop more inclusive and future-oriented GenAI policies integrating social equity, interdisciplinary experimentation, and sustainability considerations
• Embed anticipatory governance in institutional strategy via Chief AI Officers, AI task forces, faculty AI-literacy development, and student co-design mechanisms
• Foster communities of practice for AI governance instead of rigid top-down policies
• Use the taxonomy as a self-assessment and strategic-planning roadmap for institutional AI adoption

Examples¶

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Key Sources¶

• Martin Sposato. (2025). Artificial intelligence in educational leadership: a comprehensive taxonomy and future directions. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-025-00517-1