NONE 20260417085936529 naun/naun content-registration-form+ja@crossref.org content-registration-form International Journal of Education and Information Technologies 2074-1316 Judit Szűcs Department of Information Technology and its Applications, University of Pannonia, Hungary Krisztián Németh Department of Information Technology and its Applications, University of Pannonia, Hungary Szilárd Jambrits Department of Information Technology and its Applications, University of Pannonia, Hungary Tibor Guzsvinecz Department of Information Technology and its Applications, University of Pannonia, Hungary Psychological analysis of drawings has long been a subject of interest in clinical and research settings, offering insights into an individual's personality, emotional state, and cognitive processes. Traditional methods rely on subjective interpretation, often leading to bias and inconsistency. This study presents a novel approach to psychological drawing analysis by integrating artificial intelligence and computer vision techniques. A Python-based application has been developed, utilizing machine learning algorithms for automated recognition of geometric shapes, color patterns, and symmetry within drawings. The system employs convolutional neural networks for shape detection and a structured color recognition model based on the RGB model. The extracted visual features are analyzed using a generative artificial intelligence model to infer psychological traits and emotional states. While the application does not replace clinical diagnosis, it offers a valuable complementary tool for therapists by providing objective and data-driven insights. The research was designed as a proof-of-concept study, aiming to demonstrate the technical feasibility and potential clinical relevance of AI-assisted drawing analysis. 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