超星AI教学在《大学化学》大班教学中的创新应用

Innovative Application of AI Teaching in Large-Class Instruction of “University Chemistry” Based on Superstar Learning Platform

摘要: 针对百人规模《大学化学》大班教学中存在的概念理解困难、教学效率低下及专业联结薄弱等核心痛点，基于超星学习通平台构建了AI赋能的新型教学模式。通过知识图谱技术重构热力学、电化学等核心模块，将抽象概念与专业案例深度融合；采用动态分组和个性化路径实现跨专业自适应教学；利用智能批改和学情预警系统将反馈周期从72小时缩短至实时干预。该模式成功突破了传统大班教学规模化与个性化的矛盾，通过AI技术实现了从“统一灌输”到“精准滴灌”的教育范式转型，使《大学化学》从基础理论课程转变为连接机械、材料、汽车等工科专业的化学思维训练枢纽，为理工科基础课程教学改革提供了可复制的创新范式。

Abstract: This paper addresses core challenges in large-scale “College Chemistry” classes of 100 students, including difficulties in conceptual understanding, low teaching efficiency, and weak disciplinary connections. Based on the Superstar Learning Platform, an AI-powered teaching model has been developed. By reconstructing core modules such as thermodynamics and electrochemistry using knowledge graph technology, abstract concepts are deeply integrated with disciplinary case studies. Dynamic grouping and personalized pathways enable adaptive cross-disciplinary teaching, while intelligent grading and learning early warning systems reduce feedback cycles from 72 hours to real-time intervention. This model successfully resolves the conflict between scale and personalization in traditional large-class teaching, transforming the educational paradigm from “uniform instruction” to “targeted delivery” through AI technology. It shifts “College Chemistry” from a foundational theoretical course to a hub for chemical thinking that connects engineering disciplines such as mechanical engineering, materials science, and automotive engineering, providing a replicable innovative paradigm for teaching reform in fundamental science and engineering courses.