Teacher Readiness for AI Integration: A Professional Development Framework

Artificial intelligence (AI) technologies are increasingly embedded in classroompractice, yet teachers' readiness to adopt them varies widely and is shapedbyaninteracting set of psychological, institutional, and demographic factors. This studyproposes and empirically illustrates a Professional Development (PD) FrameworkforAI integration by applying supervised and unsupervised machine learning techniquesto survey-based indicators of teacher readiness (N = 428). Four classifiers were trainedto predict three-level readiness (Low, Moderate, High) from digital literacy, self- efficacy, school ICT support, attitude toward AI, prior technology training, professional development hours, experience, and demographic covariates. Logisticregression achieved the strongest generalization (accuracy = .61, macro F1 =.54, 5-fold cross-validated F1 = .56), while random forest attained the highest rawaccuracy(.63). Feature-importance analysis identified digital literacy, self-efficacy, attitudetoward AI, and school ICT support as the four strongest predictors of readinessclassification. K-means clustering on principal components identified three latent
teacher profiles — cautious adopters, confident-but-under-supported adopters, andexperience-anchored moderates — each requiring differentiated PDstrategies. Theresults inform a tiered, profile-responsive PD framework that aligns institutional
support, coaching intensity, and AI-pedagogy content with teachers' actual readinessprofiles rather than a one-size-fits-all training mode