PROSPECTS OF USING AI TO PERSONALIZE TEACHING IN 3D MODELING AND PRINTING IN STEM EDUCATION

Abstract

The relevance of the study is due to the low level of implementation of 3D printers in educational institutions of Ukraine, the lack of adaptive teaching methods and the difficulty of mastering 3D technologies without individual support. Given the rapid development of AI and the growing role of 3D printing in engineering education, there is a need to create effective intellectual tools that can improve students' mastery of technical skills. The purpose of the study is to develop and experimentally test a model of AI-personalization of 3D modelling and 3D printing training using Vernerfab 3D printers in STEM courses. The methodology is based on comparing the results of the control and experimental groups, where the experimental group interacted with an AI module that analyzed STL files, generated personalized prompts, recommended 3D printing parameters and adapted the complexity of tasks. The results showed significant benefits of AI support: a 67% reduction in topological errors, a 37% increase in the proportion of models that are printable the first time, an increase in printing accuracy, and a reduction in project execution time by approximately 30%. The experimental group demonstrated a significantly higher increase in complex modelling skills and model preparation for printing. AI also had a positive impact on motivation, a sense of support, and satisfaction with learning. The conclusions emphasize that integrating AI into 3D modelling and 3D printing education is an effective way to personalize STEM education and improve the quality of student training. The applied model can be scaled for use in educational institutions of various levels. Prospects for further research include expanding the functionality of the AI system, integrating multi-component adaptive learning models, and studying the long-term impact of AI on student success.