The rapid integration of artificial intelligence in education necessitates comprehensive teacher AI literacy extending beyond traditional digital competence. However, systematic frameworks identifying essential competencies remain fragmented across contexts. This systematic review establishes an evidence-based hierarchical competency framework for teacher AI literacy development. Following PRISMA 2020 guidelines, we searched four databases for peer-reviewed studies (2019-2024). After screening 1,851 records, 37 high-quality studies from 24 countries representing 15,520 participants were analyzed. We identified 17 distinct competencies classified into three evidence-based tiers: Essential competencies (≥70% prevalence) led by AI-Enhanced Teaching Methods (94.6%) and AI Conceptual Knowledge (91.9%); Important competencies (40-69%) including Professional Development (73.0%); and Developing competencies (<40%) such as Cross-disciplinary Applications (35.1%). This internationally-derived framework reveals a critical gap where equity awareness (24.3%) emerges late despite fundamental ethical importance, informing teacher education policy and curriculum design. This review was not prospectively registered. This registration is retrospective and deposits the protocol, data-extraction sheet, coding book, PRISMA 2020 checklist, and quality-appraisal records of a completed review to support transparency and reproducibility.
Sarakan et al. (2026) studied this question.
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