Abstract: This paper investigates innovative approaches to organizing chemistry instruction through the application of artificial intelligence (AI) technology, utilizing specific case studies to demonstrate how AI can transform traditional teaching methods. To address the shortcomings of conventional chemistry education, we propose three primary innovation pathways based on AI technology: adaptive learning systems, intelligent experimental platforms, virtual laboratories, and collaborative learning enhanced by intelligent coordination. Adaptive learning systems leverage personalized data analysis to dynamically tailor learning pathways for students, effectively addressing the limitations of “one-size-fits-all” teaching models. Virtual laboratories and intelligent experimental platforms transcend the constraints of physical experiments by providing students with a safe and flexible environment for conducting experiments. Additionally, intelligent collaboration tools optimize group dynamics in cooperative learning settings, enhancing learning efficiency through real-time feedback. Nevertheless, the implementation of AI technology in chemical education faces challenges, including the uneven distribution of educational resources and inadequate technological proficiency among educators. This paper recommends addressing these challenges by optimizing resource allocation, strengthening teacher training, and integrating virtual and traditional teaching methods to enhance the effectiveness of AI applications in chemistry education. Looking ahead, AI technology is poised to continue driving innovation in chemical education, facilitating the intelligent transformation of instructional methods, and playing a crucial role in promoting educational equity and improving teaching quality.

Key words: Artificial intelligence, Adaptive learning system, Intelligent experimental platform, Virtual laboratory platform, Intelligent collaboration