Exploration and Research on the Application of Generative AI in Supporting Materials Science Experimental Courses in the Era of Digital Intelligence

SHE Yan; ZHUANG Qi-xin; ZHANG Hao-ran; ZUO Pei-yuan; GU Jin-lou; TENG Xin

doi:10.14028/j.cnki.1003-3726.2025.24.366

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Exploration and Research on the Application of Generative AI in Supporting Materials Science Experimental Courses in the Era of Digital Intelligence

更新时间：2025-05-23

- Exploration and Research on the Application of Generative AI in Supporting Materials Science Experimental Courses in the Era of Digital Intelligence
- Polymer Bulletin Vol. 38, Issue 6, Pages: 958-966(2025)
- 作者机构：
  
  1.华东理工大学材料科学与工程学院，上海 200237
  2.华东理工大学教务处，上海 200237
- 作者简介：
- 基金信息：
- DOI：10.14028/j.cnki.1003-3726.2025.24.366
  CLC：
- Received：02 December 2024，
  
  Accepted：27 January 2025，
  
  Published Online：25 March 2025，
  
  Published：20 June 2025
- 稿件说明：
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佘砚, 庄启昕, 张浩然, 左沛元, 顾金楼, 滕鑫. 数智化时代生成式AI助力材料专业实验课程探索研究. 高分子通报, 2025, 38(6), 958-966.

She, Y.; Zhuang, Q. X.; Zhang, H. R.; Zuo, P. Y.; Gu, J. L.; Teng, X. Exploration and research on the application of generative AI in supporting materials science experimental courses in the era of digital intelligence. Polym. Bull. (in Chinese), 2025, 38(6), 958-966.
佘砚, 庄启昕, 张浩然, 左沛元, 顾金楼, 滕鑫. 数智化时代生成式AI助力材料专业实验课程探索研究. 高分子通报, 2025, 38(6), 958-966. DOI： 10.14028/j.cnki.1003-3726.2025.24.366.

She, Y.; Zhuang, Q. X.; Zhang, H. R.; Zuo, P. Y.; Gu, J. L.; Teng, X. Exploration and research on the application of generative AI in supporting materials science experimental courses in the era of digital intelligence. Polym. Bull. (in Chinese), 2025, 38(6), 958-966. DOI： 10.14028/j.cnki.1003-3726.2025.24.366.

摘要

在数智化时代，生成式人工智能（generative AI，简称“生成式AI”）技术凭借其在数据分析和智能反馈等领域的优势，为高校实验课程的建设带来了全新的视角和解决方案。本文以华东理工大学开发的高分子化学实验AI助手为例，探索了基于大语言模型和检索增强生成技术的智能教学解决方案，该AI助手为实验教学创设因材施教的教学新模式、打造沉浸式学习新形态，并推动了智能化教学管理新变革，有效解决了传统实验教学中资源受限、指导不足等难题。通过实验教学效果评价，“使用AI助手组”的学生绝大多数认为AI助手能显著提升学生的理论知识掌握和实验操作能力，教学模式效果良好，验证了生成式AI助力材料专业实验课程的可行性与优势，为推动高等教育数智化转型提供了创新范例，也为生成式AI推广应用到更多课程教学提供了有益参考。

Abstract

In the era of digital intelligence

the advantages of generative AI technology in data analysis

intelligent feedback

and other areas provide a new perspectives and solutions for the development of experimental courses in higher education. This paper takes the polymer chemistry experiment AI assistant developed by the East China University of Science and Technology as an example to explore an intelligent teaching solution based on LLM+RAG technology. The AI assistant creates a personalized teaching model

builds an immersive learning experience

and promotes a new paradigm in intelligent teaching management

addressing the challenges of resource limitations and inadequate guidance in traditional experimental teaching. Through the evaluation of experimental teaching outcomes

the majority of students in the “AI-assisted group” believed that the AI assistant significantly enhanced their theoretical knowledge and experimental skills

and the teaching model showed positive results. This study validates the feasibility and advantages of Generative AI in supporting experimental courses in materials science

providing an innovative example for the digital transformation of higher education

and offering valuable insights for the broader application of Generative AI in course teaching.

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references

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