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清华大学教育研究[ISSN:1001-4519/CN:11-1610/G4]

卷:

期数:

2024年05期

页码:

89-98

栏目:

教育改革与发展

出版日期:

2024-10-20

文章信息/Info

Title:

Empowering Higher Education Curriculum with Generative Artificial Intelligence: A Case Study of the “Chemical Thermodynamics” Course

作者:

卢滇楠¹ 党 漾² 王宏宁³ 黎叙锐¹

1.清华大学 化学工程系;2.清华大学 教育研究院;3.清华大学 计算机科学与技术系

Author(s):

LU Dian-nan¹ DANG Yang² WANG Hong-yu³ LI Xu-rui¹

1.Department of Chemical Engineering, Tsinghua University;2.Institute of Education, Tsinghua University;3.Department of Computer Science and Technology, Tsinghua University

关键词:

生成式人工智能; 高等教育; 课程教学; 化工热力学

Keywords:

generative artificial intelligence;  higher education;  course teaching;  Chemical Thermodynamics

分类号:

G642

文献标志码:

A

摘要:

人工智能的快速发展持续驱动着教育教学模式的革新。当前生成式人工智能在高等教育领域的应用缺乏针对特定学科的垂直模型训练与系统化的课堂实践。本研究以清华大学“化工热力学”课程人工智能助教的建设与应用为例,介绍了生成式人工智能赋能特定学科课程教学的垂直模型训练与校准的方法,并使用课堂案例验证了所开发模型赋能教师教学和学生学习的有效性。研究发现,课程人工智能助教能够显著提高教师的备课效率与教学灵活性,并支持学生及时补齐知识短板、提出复杂问题的创造性解决方案。

Abstract:

The rapid development of artificial intelligence continuously drives the revolution of teaching models and practices in higher education. However, the discipline-specific model training and systematic curriculum integration of generative AI in higher education still lack scientific research. Taking the “Chemical Thermodynamics” course at Tsinghua University as an example, this study introduces the construction and application of a discipline-specific AI teaching assistant, including methods for vertical model training and calibration and actual curriculum practice. The case study validates the effectiveness of the developed AI teaching assistant in enhancing both teaching and learning. The results show that the AI teaching assistant significantly improves teaching preparation efficiency and instructional flexibility and supports students in addressing knowledge gaps and providing creative solutions to complex problems.

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更新日期/Last Update: 2024-10-20