AI Literacy Frameworks for Educators: An Umbrella Review

As artificial intelligence transforms educational environments, educators require specialized literacy frameworks for effective technology integration. This umbrella review synthesizes 28 prior studies (26 systematic/scoping reviews and 2 design/resource-mapping studies) on AI literacy frameworks for educators. Our analysis examines conceptual models, ethical principles, treatment of explainability as a subdomain of ethics, implementation challenges and enablers, and assessment strategies. The results show fragmented approaches with constructivist approaches being the predominant theoretical foundation, with TPACK and AI4K12 also commonly adopted, while attention to ethics varies widely across frameworks. Key challenges include insufficient teacher preparation, resource limitations, and technological complexity, while enablers encompass structured professional development, project-based learning, and institutional support. The review identifies significant gaps in assessment methodologies, particularly the lack of standardized teacher-specific evaluation tools, and notes that explainability, despite its importance for educator trust, is explicitly addressed in only one study. This analysis informs development of robust educator-centered AI literacy frameworks balancing technical and ethical knowledge.