School leaders and AI-driven education: a comparative study of readiness, perceptions and implementation strategies

Purpose The rapid integration of artificial intelligence (AI) in education necessitates that school leaders and educators develop the knowledge and readiness to implement AI-driven innovations both effectively and ethically. Guided by the technology acceptance model and distributed leadership theory, this study examines disparities in AI familiarity, perceived relevance of AI-related skills, and institutional readiness between K-12 school leaders and teachers. Design/methodology/approach Using survey data from 147 US based K-12 educators, the findings reveal that school leaders report higher levels of AI familiarity and optimism, while teachers express more varied perspectives and lower confidence in implementation. Qualitative responses further highlight concerns about relevance, training gaps and ethical implications such as data privacy and algorithmic bias. Findings These insights emphasize the need for targeted, job embedded professional development, collaborative leadership models and policy frameworks that are inclusive, equity focused and responsive to instructional realities. Originality/value The study contributes to global conversations on school leadership by framing AI adoption not just as a technical issue, but as a sociotechnical and policy challenge requiring intentional, cross-sector collaboration.