Aim/Purpose: This study aims to identify and explain the key factors influencing the artificial intelligence (AI) readiness of in-service secondary Vietnamese teachers, addressing the lack of empirical evidence on how teachers progress from initial acceptance to active instructional integration of AI. Background: While AI is increasingly promoted in education policy and practice, teachers’ readiness to adopt and integrate AI remains uneven and underexplored in developing countries. Existing studies often conceptualize AI readiness as a single construct, overlooking its developmental nature. Methodology: The study employed a quantitative approach and collected survey data from 1,145 in-service secondary school teachers in Vietnam. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study developed and validated a theoretical model incorporating organizational factors (policy and organization support, professional development, collegial support), individual factors (confidence, perceived relevance), and social factors (subjective norms) as predictors of readiness stages (Accepting, Introducing, Teaching). Contribution: This research contributes to the literature by conceptualizing teachers’ AI readiness as a progressive, multi-stage construct and empirically validating a comprehensive model that integrates organizational, professional, social, and individual factors in a developing-country context. Findings: The model demonstrated strong explanatory power and predictive relevance, with thirteen of fourteen hypotheses supported. Results indicate that AI confidence and perceived AI relevance significantly predict all stages of AI readiness. Subjective norms influence early stages of readiness but do not directly affect advanced teaching integration. Acceptance strongly predicts introduction, and introduction, in turn, is a powerful predictor of AI integration in teaching. Collegial support emerged as the strongest organizational predictor of subjective norms. Multi-group analysis revealed gender and regional differences, with professional development effects stronger for female teachers and perceived relevance influencing male teachers’ integration intentions more substantially. Recommendations for Practitioners: Educational leaders should design staged professional development programs that build teachers’ confidence, highlight the pedagogical relevance of AI, foster supportive collegial cultures, and provide clear institutional policies. Recommendation for Researchers: Future studies should adopt longitudinal designs to examine changes in AI readiness over time and extend the model to other subject areas and educational levels to enhance generalizability. Impact on Society: By clarifying how teachers develop readiness to integrate AI into teaching, this study supports more effective implementation of AI-driven educational reforms, contributing to improved teaching quality and equitable digital transformation in education. Future Research: Further research should explore causal mechanisms through mixed-methods approaches and investigate how student outcomes and ethical considerations interact with teachers’ AI readiness across diverse educational contexts.
Dũng et al. (Thu,) studied this question.