Technical Proficiency as the Strongest Predictor of AI Self-Efficacy: A Decision Tree Analysis of ChatGPT Literacy Among Romanian Educators

Abstract

The growing integration of generative artificial intelligence (AI) in education requires educators to develop not only operational competencies but also confidence in using AI tools effectively. The present study examined the predictive relationship between the multidimensional construct of ChatGPT literacy and AI self-efficacy among educators working in Romanian educational settings. A sample of 393 educators from Western Romania completed the ChatGPT Literacy Scale (and the AI Self-Efficacy subscale of the Meta AI Literacy Scale. Reliability analyses indicated good to excellent internal consistency across all literacy dimensions (α = .80–.95) and AI self-efficacy (α = .89). To model nonlinear and hierarchical relationships among predictors, a Decision Tree Regression approach was implemented in JASP. The five ChatGPT literacy dimensions, technical proficiency, critical evaluation, communication proficiency, ethical competence, and creative application, were entered as predictors of AI self-efficacy. The model explained 53.2% of the variance in AI self-efficacy (R² = .532), demonstrating moderate predictive performance (MSE = 0.517; RMSE = 0.719). Feature importance analysis revealed that technical proficiency was the strongest predictor (40.07%), followed by ethical competence (18.12%), critical evaluation (14.99%), communication proficiency (14.01%), and creative application (12.80%). The first and most informative split occurred on technical proficiency, highlighting its importance in shaping educators’ perceived AI capability. These findings indicate that technical proficiency emerged as the strongest predictor of AI self-efficacy among educators. The results have implications for AI-focused professional development programmes, emphasising the importance of structured technical training alongside ethical and critical competencies.