Factors influencing educators' AI adoption: A grounded meta-analysis review

Artificial Intelligence (AI) is rapidly reshaping educational practices, yet educators' adoption of AI varies. This paper utilized a grounded meta-analysis framework of 45 peer-reviewed articles published between 2020 and 2024, including qualitative, quantitative, mixed-method, and social media (X) studies, to examine factors influencing educators' AI adoption. Four primary categories emerged from coding the papers: Individual Factors (demographics, AI literacy, beliefs, and self-efficacy), Infrastructure (institutional support, resource availability, social influence, and media narratives), Tools (perceived usefulness, ease of use, compatibility, transparency, bias, and reliability), and Impacts (concerns about overdependence, job security, and potential misuse). X-data paper findings also indicated that educators generally view AI positively but express notable concerns regarding trust, transparency, and ethical implications, highlighting the necessity for improved AI literacy. This study moreover revealed that although common technology adoption frameworks (e.g., TAM, UTAUT) frequently informed the analyses, these models inadequately address the unique ethical, pedagogical, institutional, and technical complexities specific to AI. The findings offer valuable insights for educators, educational institutions, and AI developers by pinpointing these critical factors. Key recommendations include providing robust institutional support, establishing transparent AI usage policies, and offering targeted professional development opportunities. Implementing these strategies will enhance educators' confidence and ensure the responsible and ethical integration of AI into educational settings, ultimately maximizing AI's potential to positively transform education.