This work explores the reform of the Physical Chemistry course in regional universities under the background of artificial intelligence (AI). Taking Guilin University of Technology in China as a case study, the course is redesigned to address issues such as weak local relevance, insufficient interdisciplinary integration, low digitalization, and limited practical application. Teaching content is reconstructed using knowledge graphs to incorporate regional pillar industries such as new energy vehicles and non-ferrous metals. A discipline–technology–industry matrix is established, integrating AI tools including machine learning and intelligent simulation software. A smart teaching platform is developed to support personalized learning and competency-based assessment. Furthermore, an industry–education data-sharing mechanism is introduced to transform theoretical knowledge into engineering capabilities. These reforms enhance students’ professional competencies and contribute to the development of high-quality engineering professionals aligned with regional economic needs.
Zhang et al. (Tue,) studied this question.