人工智能赋能《材料表面工程基础》教学改革研究
Research on the Teaching Reform of “Fundamentals of Materials Surface Engineering” Empowered by Artificial Intelligence
摘要: 本论文聚焦人工智能技术在材料表面工程课程教学中的应用与创新，提出以培养综合能力、创新精神和实践能力为核心的课程目标。通过理论课程、实验课程和实践课程的系统设计，结合启发式、互动式和案例式教学方法，构建智能化教学体系。研究强调人工智能在表面处理技术、工程装备及教学评价中的深度融合，旨在推动材料表面工程课程向智能化、实践化和创新化方向发展，培养适应新时代需求的高素质工程人才。
Abstract: This paper focuses on the application and innovation of artificial intelligence technology in the teaching of the materials surface engineering course, proposing a course goal centered on cultivating comprehensive capabilities, innovative spirit, and practical skills. Through the systematic design of theoretical, experimental, and practical courses, combined with heuristic, interactive, and case-based teaching methods, an intelligent teaching system is constructed. This research emphasizes the deep integration of artificial intelligence in surface treatment technology, engineering equipment, and teaching evaluation, aiming to promote the development of the materials surface engineering course towards intelligence, practicality, and innovation, and to cultivate high-quality engineering talents that meet the needs of the new era.
文章引用：谭伯川, 邓洪达, 兰伟, 曹献龙, 孙建春. 人工智能赋能《材料表面工程基础》教学改革研究[J]. 创新教育研究, 2025, 13(11): 172-182. https://doi.org/10.12677/ces.2025.1311854

参考文献

[1] 迟光芳, 王杰, 王怡静, 等. 新工科背景下的材料表面工程技术课程教学改革探索与实践[J]. 化工设计通讯, 2025, 51(5): 52-54.
[2] 张吉阜, 施斌卿, 陈东初. 电化学“微弧氧化”技术引入《金属材料表面工程》实验教学课程建设[J]. 广东化工, 2023, 50(10): 236-239.
[3] 蔡会生, 郭锋, 白朴存, 等. 材料表面工程课程教学改革探索与实践[J]. 创新创业理论研究与实践, 2022, 5(18): 21-23.
[4] 韦莉莉, 黄宏锋, 亓海全, 等. 面向金属材料工程专业的《材料表面工程》课程教学思考与改革[J]. 科技风, 2021(28): 46-48.
[5] 崔龙辰. 基于“以学生为中心”理念的材料表面工程课程教学改革探索[J]. 广东化工, 2020, 47(8): 184-192.
[6] 强新发, 巴志新. 以“材料表面工程”教学为例浅谈科研对教学的促进作用[J]. 教育现代化, 2018, 5(45): 225-226.
[7] 李丽波, 张桂玲, 王飞. “互联网+”背景下的《材料表面与界面》工程认证体系课程建设[J]. 创新创业理论研究与实践, 2018, 1(17): 47-48.
[8] 袁明月. 互动式教学法在“材料表面工程”课程中的应用[J]. 课程教育研究, 2017(36): 103-104.
[9] 郭云霞, 卢向雨, 郭平义.《材料表面工程技术》教学改革探索[J]. 产业与科技论坛, 2017, 16(11): 147-148.
[10] 田立辉, 卢向雨.《材料表面工程技术》本科教学改革探索[J]. 产业与科技论坛, 2017, 16(3): 217-218.
[11] Kulik, J.A. and Fletcher, J.D. (2016) Effectiveness of Intelligent Tutoring Systems. Review of Educational Research, 86, 42-78. [Google Scholar] [CrossRef] 
[12] Ma, W., Adesope, O.O., Nesbit, J.C. and Liu, Q. (2014) Intelligent Tutoring Systems and Learning Outcomes: A Meta-Analysis. Journal of Educational Psychology, 106, 901-918. [Google Scholar] [CrossRef] 
[13] Gervet, T., Koedinger, K., Schneider, J. and Mitchell, T. (2020) When Is Deep Learning the Best Approach to Knowledge Tracing? Journal of Educational Data Mining, 12, 31-54.
[14] Levin, N.A. (2021) Process Mining Combined with Expert Feature Engineering to Predict Efficient Use of Time on High-Stakes Assessments. Journal of Educational Data Mining, 13, 1-15.
[15] Radianti, J., Majchrzak, T.A., Fromm, J. and Wohlgenannt, I. (2020) A Systematic Review of Immersive Virtual Reality Applications for Higher Education: Design Elements, Lessons Learned, and Research Agenda. Computers & Education, 147, Article 103778. [Google Scholar] [CrossRef] 
[16] Suhail, N., Bahroun, Z. and Ahmed, V. (2024) Augmented Reality in Engineering Education: Enhancing Learning and Application. Frontiers in Virtual Reality, 5, Article 1461145. [Google Scholar] [CrossRef] 

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