人工智能背景下“油矿地质学”课程教学内容改革与探索
Reform and Exploration of Teaching Content for “Oilfield Geology” in the Era of Artificial Intelligence
DOI: 10.12677/ae.2026.161037, PDF,    科研立项经费支持
作者: 罗 超*, 尹楠鑫, 李胜玉：重庆科技大学石油与天然气工程学院，重庆；李 明, 王 涛：重庆科技大学重庆非常规油气开发研究院，重庆
关键词: 油矿地质学；人工智能；课程教学内容；考核方式；Oilfield Geology； Artificial Intelligence； Course Teaching Content； Assessment Methods
摘要: 课程教学内容改革是课程建设的关键环节，在重庆科技大学“油矿地质学”课程建设过程中，深度引入人工智能方法，系统优化了课程建设目标，在常规内容的基础上，增加了“人工智能+”融合点，按照8个模块系统实施，可激发学习兴趣、提升学习成效、增强实践能力。
Abstract: Teaching content reform is a key component of curriculum development. In the process of constructing the “Oilfield Geology” course at Chongqing University of Science and Technology, artificial intelligence methods were deeply integrated to systematically optimize the course objectives. On the basis of conventional content, several “AI-enhanced” elements were added and implemented through eight structured modules. This reform stimulates students’ interest, improves learning outcomes, and strengthens practical abilities.
文章引用：罗超, 李明, 尹楠鑫, 王涛, 李胜玉. 人工智能背景下“油矿地质学”课程教学内容改革与探索[J]. 教育进展, 2026, 16(1): 269-275. https://doi.org/10.12677/ae.2026.161037

参考文献

[1] 王雪, 张生, 黄翠, 等. 生成式人工智能支持日常教学中情境试题评价与改进的实证研究[J]. 上海教育科研, 2025(10): 23-30.
[2] 洪源, 武文彬. 人工智能时代高校教学的技术依赖隐忧与破局之路[J]. 信息系统工程, 2025(10): 129-132.
[3] 周震, 张婷婷, 刘艳丽, 等. 人工智能在我国高校教育教学中的应用研究综述[J]. 中国现代教育装备, 2025(19): 20-23.
[4] 赵斌, 陈荣. 人工智能赋能教研教学融合推动课堂教学的理论与实践研究[J]. 昌吉学院学报, 2025(4): 119-123.
[5] 韩冰. 人工智能赋能智慧课堂的智慧生成机制与教学模式创新研究[J]. 计算机时代, 2025(10): 100-103+108.
[6] 刘钰铭, 吴胜和, 岳大力, 等. 面向工程教育专业认证的油矿地质学课程改革[J]. 教育现代化, 2018, 5(40): 93-95.
[7] 李海燕, 徐朝晖, 刘钰铭. 研究型教学模式在油矿地质学大作业教学中的应用[J]. 教育教学论坛, 2018(27): 66-68.
[8] Araya-Polo, M., Jennings, J., Adler, A. and Dahlke, T. (2018) Deep-Learning Tomography. The Leading Edge, 37, 58-66. [Google Scholar] [CrossRef] 
[9] Zhao, T. (2018) Seismic Facies Classification Using Different Deep Convolutional Neural Networks. SEG Technical Program Expanded Abstracts 2018, Anaheim, 14-19 October 2018, 2046-2050. [Google Scholar] [CrossRef] 
[10] Petrelli, M. (2024) Machine Learning in Petrology: State-of-the-Art and Future Perspectives. Journal of Petrology, 65, egae036. [Google Scholar] [CrossRef] 
[11] Wing, J.M. (2006) Computational Thinking. Communications of the ACM, 49, 33-35. [Google Scholar] [CrossRef] 
[12] Lye, S.Y. and Koh, J.H.L. (2014) Review on Teaching and Learning of Computational Thinking through Programming: What Is Next for K-12? Computers in Human Behavior, 41, 51-61. [Google Scholar] [CrossRef] 
[13] Zawacki-Richter, O., Marín, V.I., Bond, M. and Gouverneur, F. (2019) Systematic Review of Research on Artificial Intelligence Applications in Higher Education—Where Are the Educators? International Journal of Educational Technology in Higher Education, 16, Article No. 39. [Google Scholar] [CrossRef] 
[14] Holmes, W., Bialik, M. and Fadel, C. (2019) Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

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