Study on Generative AI-Enabled Internship Teaching for Polymer Materials Majors
SHE Yan1, ZUO Pei-Yuan1, ZHUANG Qi-Xin1, NIU De-Chao1, GU Jin-Lou1, XU Qi-Jie2**, TENG Xin1**
1. School of Materials Science and Engineering,East China University of Science and Technology,Shanghai 200237,China; 2. Information Technology Office,Shanghai Jian Qiao University,Shanghai 201306,China
Abstract Internship teaching in materials science is a core component in cultivating engineering-oriented professionals and a crucial stage for students’ transition toward professional engineering roles.Traditional internship instruction is often limited by safety regulations and production complexity,resulting in low student engagement and the dilemma of “visible but untouchable” skill development.Taking polymer materials internships as a case study,this paper explores the construction of an AI-enabled internship assistant that integrates enterprise production data,operating standards,and other resources to provide scenario-based and precise guidance.By incorporating virtual simulation experiments,an immersive training environment is created.An intelligent Q&A system is also developed to support real-time interaction for complex tasks such as process parameter adjustment and anomaly handling.Teaching evaluations show an 8.15% improvement in theoretical scores and a 17.23% enhancement in practical abilities,marking a shift from passive observation to active practice.This research offers a replicable model for the digital-intelligent transformation of engineering education.
SHE Yan, ZUO Pei-Yuan, ZHUANG Qi-Xin, NIU De-Chao, GU Jin-Lou, XU Qi-Jie, TENG Xin. Study on Generative AI-Enabled Internship Teaching for Polymer Materials Majors[J]. Chinese Journal of Chemical Education, 2025, 46(24): 80-87.
