The present study aimed to design and validate a comprehensive model for artificial intelligence (AI) innovation adoption in education oriented organizations. Despite the growing capacity of AI to enhance instructional and managerial processes, existing research lacks a multilevel and context specific framework tailored to the complexities of educational settings. To address this gap, the study employed an applied mixed methods approach consisting of qualitative exploration followed by quantitative validation. In the qualitative phase, a systematic literature review and semi structured interviews with experts were carried out, resulting in the identification of thirteen key components influencing AI innovation adoption. These components were subsequently classified into five analytical levels: individual, technological, organizational, environmental, and social. In the quantitative phase, data were collected from 360 questionnaires distributed among faculty members of Farhangian University, educational specialists, and administrative managers in 2025. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS SEM) in SmartPLS V4. The results demonstrated adequate construct reliability and convergent validity (CR > 0.70; AVE > 0.50), and all major structural relationships were confirmed at a significance level of p < 0.001. The coefficients of determination also indicated strong explanatory power for the proposed model. Findings further showed that technology interaction protocols, organizational alignment mechanisms, and technological trust and transparency had the greatest impact on AI innovation adoption. Consequently, the proposed model provides a practical analytical framework that can guide policymakers and educational leaders in designing and implementing effective strategies for the development and deployment of intelligent technologies in education oriented organizations.