Exploring STEM Education Models and Technologically Enhanced Pathways Based on Artificial Intelligence

Abstract

Against the backdrop of intensifying global technological competition and the digital transformation of education, STEM education, as a core path for cultivating innovative scientific and technological talent, faces challenges in its traditional model, including uneven resource allocation, insufficient personalized instruction, and weak practical links. Artificial intelligence (AI), leveraging core capabilities such as data analysis, adaptive learning, and virtual simulation, offers new possibilities for model innovation and quality improvement in STEM education. This paper systematically explores the logical connections between AI and STEM education through literature research and case analysis, exploring typical AI-based STEM education models. Drawing on domestic case studies, it analyzes the technical bottlenecks and practical challenges faced in AI-enhanced STEM education. Furthermore, it proposes specific pathways for AI-enhanced STEM education from four perspectives: technological optimization, resource balancing, teacher training, and institutional support. This provides practical insights for promoting the high-quality development of STEM education. Research shows that AI empowerment is not simply a matter of adding new technologies, but rather deeply implementing the core goals of STEM education: "personalization, practicality, and interdisciplinaryness" by restructuring teaching processes, optimizing learning experiences, and expanding practical scenarios. Future policy guidance and technological innovation will be needed to address the challenges of "technical adaptability" and "educational equity," ensuring that AI truly becomes a core support for improving the quality and efficiency of STEM education.