Abstract: Under the dual impetus of educational digital transformation and the “Emerging Engineering Education” initiative, the integration of artificial intelligence (AI) technology with higher education has emerged as a pivotal direction for pedagogical reform. This study examines the Physical Chemistry Experiment course as a case study to explore AI-enhanced experimental teaching methodologies. Key initiatives include: (1) developing smart courses on the Chinese MOOC platform, (2) employing AI to drive innovative experimental design and equipment modernization, (3) utilizing AI-assisted ideological education in curriculum development, and (4) constructing a multidimensional comprehensive evaluation system through large language models. The implementation involves creating knowledge graphs, deploying AI teaching assistants, and developing an intelligent experimental report grading system, enabling precise resource allocation, intelligent optimization of experimental processes, personalized ideological education, and efficient evaluation feedback. By leveraging smart course resources and AI technologies, this teaching model effectively bridges pre-class and post-class learning—traditionally weaker components—with inclass instruction, forming a complete pedagogical cycle. Practical results demonstrate significant improvements in teaching efficiency and assessment quality, along with enhanced student autonomy in inquiry-based learning and innovative thinking. This approach provides a replicable model for intelligent reform in STEM experimental courses.

Key words: Physical chemistry experiments, Artificial intelligence, Smart courses, Knowledge graphs, AI teaching assistants, Intelligent experimental report scoring system