人工智能时代工程材料学教学改革的几点思考及实施途径
Several Thoughts and Implementation Approaches on the Teaching Reform of Engineering Materials in the Artificial Intelligence Era
摘要: 随着人工智能技术的快速迭代与革新,其已成为赋能新材料研发跨越式发展的核心引擎,推动工程材料领域从传统经验驱动模式向数据智能创新模式转变。工程材料学作为工科类专业核心基础课程,承担着培养学生材料专业知识与实践创新能力的重要使命。人工智能时代下,如何实现二者教学有机融合、破解传统教学瓶颈,顺应行业趋势、培养与时俱进的工程材料领域专业型人才,进而提升教学效果与人才培养质量,是高校工程材料学教学改革的重要课题。文章结合人工智能在材料领域的应用现状,分析当前工程材料学教学现存问题,提出教学改革的几点思考,并探索切实可行的实施途径,为高校工程材料学教学改革提供参考与借鉴。
Abstract: With the rapid iteration and innovation of artificial intelligence technology, it has become the core engine empowering the leapfrog development of new material research and development, driving the field of engineering materials to transform from a traditional experience-driven model to a data-driven intelligent innovation model. As a core basic course for engineering majors, Engineering Materials undertakes the important mission of cultivating students’ professional knowledge of materials and practical innovation capabilities. In the era of artificial intelligence, how to realize the organic integration of the two in teaching, break through the bottlenecks of traditional teaching, adapt to industry trends, cultivate professional talents in the field of engineering materials who keep pace with the times, and further improve teaching effectiveness and the quality of professional and technical talent training has become an important issue in the teaching reform of Engineering Materials in colleges and universities. Combining the application status of artificial intelligence in the field of materials, this paper analyzes the existing problems in the current teaching of Engineering Materials, puts forward several thoughts on teaching reform, and explores practical implementation approaches, so as to provide reference for the teaching reform of Engineering Materials in colleges and universities.
参考文献
|
[1]
|
周杨理理, 汪卫华, 赵紫威. 人工智能驱动的材料科学: 演进、框架、困境与破局[J]. 中国科学院院刊, 2026, 41(2): 393-405.
|
|
[2]
|
齐民. 人工智能时代背景下《工程材料》课程教育教学的思考[J]. 教育教学论坛, 2020(35): 268-269.
|
|
[3]
|
中国教师生成式人工智能应用报告(2026) [EB/OL]. https://m.gmw.cn/2026-05/13/content_38762738.htm, 2026-05-12.
|
|
[4]
|
王查娜. 设AI专业高校数量占比超50% [N]. 中国高新技术产业导报, 2025-6-16(A13).
|
|
[5]
|
杨天野. 人工智能赋能工程教育课程改革: 逻辑缘起、维度重构与实践路径[J]. 职业技术教育, 2025, 46(34): 75-80.
|
|
[6]
|
祝刚, 吴淑婧. 人工智能课程教什么怎么教——对全球高校人工智能课程体系的观察[N]. 中国教育报, 2025-03-20(09).
|
|
[7]
|
上海大学材料学院举办院级平台课程《材料AI设计与实践》首次集体备课会——打造AI驱动的新工科教育样本[EB/OL]. https://mat.shu.edu.cn/info/1012/21958.htm, 2025-04-21.
|
|
[8]
|
袭晟堃, 李嘉慧, 陶秋伶, 等. 基于人工智能的航空航天高温合金设计进展与展望[J]. 空间科学与试验学报, 2025, 2(5): 37-61.
|
|
[9]
|
曹志增, 王桂吉, 罗斌强. 基于机器学习的金属材料力学性能智能预测研究现状与展望[J]. 力学进展, 2026, 56(1): 123-168.
|
|
[10]
|
陈名毅, 胡俊伟, 余耀辰, 牛海洋. 机器学习分子动力学辅助材料凝固形核研究进展[J]. 金属学报, 2024, 60(10): 1329-1344.
|
|
[11]
|
王哲, 李长久, 陈泽霖. 智能化教学工具在工程材料学科教学中的创新应用[J]. 化工管理, 2025(6): 81-84+88.
|