大学化学 >> 2026, Vol. 41 >> Issue (10): 47-55.doi: 10.12461/PKU.DXHX202510050

教学研究与改革 上一篇    

基于生成式人工智能的有机化学教学创新：学生角色、教师职责与学习迁移

朱荣秀   

  1. 山东大学化学与化工学院, 山东 济南 250100
  • 收稿日期:2025-10-15 录用日期:2025-11-17 发布日期:2026-10-13
  • 通讯作者: 朱荣秀 E-mail:rxzhu@sdu.edu.cn Rongxiu Zhu
  • 基金资助:
    山东省自然科学基金(ZR2023MB072)；山东省本科教学改革研究项目(M2024026)；山东大学教育教学改革研究项目(2023Y106, 2024Z07和2024Y123)

Innovating organic chemistry education with generative artificial intelligence: redefining student roles, teacher responsibilities, and learning transfer

Rongxiu Zhu   

  1. School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, Shandong Province, China
  • Received:2025-10-15 Accepted:2025-11-17 Published:2026-10-13
  • Contact: Rongxiu Zhu E-mail:rxzhu@sdu.edu.cn

PDF

320

赞

补充材料

1

摘要： 生成式人工智能(Generative Artificial Intelligence，GenAI)的迅速发展正重塑高等教育的教学形态。作为实验性强、概念抽象且知识体系庞大的核心学科，有机化学教学正面临来自AI的机遇与挑战。一方面，ChatGPT、DeepSeek等模型为学生提供了即时的知识生成、反应机理与核心概念的理解以及写作辅助功能，激发学习兴趣，促进自我解释与知识迁移；另一方面，缺乏引导的使用可能引发依赖性、削弱批判性思维，并带来学术诚信风险。本文以“学生角色重构-教师职责转变-学习迁移促进”为主线，在“批判性乐观主义”的教育哲学视角下，分析AI驱动学习生态中师生关系的再定义。进一步提出以AI素养、提示词设计与迁移性任务为核心的教学框架，并结合课堂实践阐明其实施要点与评价路径，旨在为高校有机化学课程的教学创新与教育评价提供可推广的参考。

关键词: 生成式人工智能, 有机化学教学, 学习迁移, 学生角色, 教师职责, 教学创新

Abstract: The rapid advancement of generative artificial intelligence (GenAI) is reshaping the landscape of higher education. As a discipline characterized by strong experimental rigor, conceptual abstraction, and a highly structured knowledge system, organic chemistry faces both opportunities and challenges in the era of AI integration. On the one hand, models such as ChatGPT and DeepSeek provide students with instant knowledge generation, support for understanding reaction mechanisms and core concepts, and assistance with academic writing—thereby enhancing learning motivation, fostering self-explanation, and promoting knowledge transfer. On the other hand, unguided or uncritical use may lead to overreliance, weaken students’ critical thinking, and create risks to academic integrity. Guided by an educational philosophy of critical optimism, this paper examines how GenAI redefines teacher-student dynamics within AI-mediated learning environments, focusing on the reconstruction of student roles, the transformation of teacher responsibilities, and the enhancement of transferability in learning. Building on this analysis, we propose a pedagogical framework centered on AI literacy, prompt design, and transfer-oriented learning tasks, and elaborate its implementation and evaluation through classroom practice. The framework offers a scalable reference model for instructional innovation and educational assessment in undergraduate organic chemistry.

Key words: Generative artificial intelligence, Organic chemistry education, Learning transfer, Student roles, Teacher responsibilities, Teaching innovation