Citation: Xiaowei Song, Min Lin, Qiuyan Cao. AI辅助的“核心知识↔学科前沿”双向映射在物理化学课程教学中的研究[J]. University Chemistry, ;2026, 41(10): 73-80. doi: 10.12461/PKU.DXHX202510074 shu

AI辅助的“核心知识↔学科前沿”双向映射在物理化学课程教学中的研究

School of Chemistry and Chemical Engineering, Anqing Normal University, Anqing 246133, Anhui Province, China

Corresponding author: Xiaowei Song, songxw@aqnu.edu.cn
Received Date: 20 October 2025
Accepted Date: 10 December 2025

针对物理化学教学中前沿内容引入进程缓慢、与核心知识体系衔接不足的问题，我们提出一种基于人工智能(AI)辅助的“核心知识↔学科前沿”双向映射教学策略。这一策略旨在通过AI构建课程核心知识与化学学科前沿研究成果之间的联系，实现教学内容与科研进展的有机融合。我们借助AI识别并匹配核心知识点所对应的前沿研究成果，并将筛选成果教学化、结构化，转化为可直接应用于课堂的“前沿案例”和“教学活动”，以促进学生对核心知识的深度理解与迁移应用。研究设计了包含任务设定、前沿成果挖掘、内容提炼与人工核验的案例开发流程，并以“Joule-Thomson效应”为例展示AI辅助双向映射的构建过程。在此基础上，提出“讲授环-研讨环-考核环”的“三环”融合方法，推动学生从知识掌握到能力养成的递进发展。本文希望为学科前沿成果融入物理化学课程教学提供初步的思路与方法。
- Physical chemistry teaching,
- Disciplinary frontiers,
- frontier cases,
- Bidirectional mapping,
- three-phase integration
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