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AI驱动工科专业课程教与学范式转变
The Transformation of Teaching and Learning Paradigms in Engineering Courses Driven by AI

DOI: 10.12677/ass.2025.147666, PP. 714-719

Keywords: AI,工科专业,教学,范式转变
AI
, Engineering Majors, Teaching, Paradigm Shift

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Abstract:

随着科技的发展,AI赋能教育领域,它通过变革教学模式实现线上线下混合式教学、创新教学内容引入前沿案例与虚拟仿真实验、转换教学角色使教师成为引导者和学生成为主动探索者。本论文聚焦于AI驱动下工科专业课程教与学范式的转变。阐述了传统工科专业教与学以课堂讲授为主、学生被动接收知识、教学资源单一和评价方式传统等问题。在此背景下,AI驱动工科专业课程教与学范式转变,具体体现为重塑教学模式提供个性化方案并创建虚拟实验室以及实现远程协作学习、丰富教学资源整合知识图谱且生成直观内容并筛选网络数据、优化评价方式综合学习过程数据且智能批改文本并评价实践能力、促进教学角色转换让教师成为设计者与引导者和学生成为主动学习者与创造者,培养高素质工科人才。
With the development of technology, AI has empowered the field of education. It has transformed teaching models to enable online-offline hybrid teaching, innovated teaching content by introducing cutting-edge cases and virtual simulation experiments, and shifted teaching roles, making teachers into guides and students into active explorers. This paper focuses on the transformation of teaching and learning paradigms in engineering courses driven by AI. It elaborates on the problems in traditional engineering teaching and learning, including the dominance of classroom lectures, students passively receiving knowledge, limited teaching resources, and traditional evaluation methods. Against this backdrop, AI is driving the transformation of teaching and learning paradigms in engineering courses. Specifically, it reshapes the teaching model by providing personalized learning plans, creating virtual laboratories, and facilitating remote collaborative learning. It enriches teaching resources by integrating knowledge graphs, generating intuitive content, and screening online data. It optimizes the evaluation method by comprehensively analyzing learning process data, intelligently grading written work, and assessing practical skills. Moreover, it promotes the transformation of teaching roles, with teachers becoming designers and guides and students becoming active learners and creators, thus cultivating high-quality engineering talents.

References

[1]  朱志萍. 智能释放: 人工智能2.0时代教育的冲击与改变——兼论人工智能赋能高等职业教育[J]. 中国职业技术教育, 2021(1): 51-58.
[2]  令玉林, 周建红. 新工科背景下人工智能赋能课程思政教学方式探究[J]. 化工设计通讯, 2025, 51(2): 95-97.
[3]  Xu, H.M. (2025) AI-Empowered Instructional Design: Current Implementations, Systemic Challenges, and Emerging Paradigms. Education Journal, 8, 249-257.
[4]  赵宇衡. 人工智能赋能背景下汽车专业课程教学质量提升策略研究[J]. 现代职业教育, 2025(16): 141-144.
[5]  吴华锋. 基于3E教学模式的“人工智能”课程教学模式优化[J]. 信息系统工程, 2025(5): 152-155.
[6]  杨业凯. “智能机器人及控制”课程教学改革[J]. 纺织服装教育, 2025, 40(1): 46-50+69.
[7]  Nan, G.F. (2025) Exploration and Thinking on the New Paradigm of AI Empowering Talent Cultivation. Advances in Vocational and Technical Education, 7, 7-11.
[8]  边媛. 人工智能背景下高中思政课教师角色定位分析[J]. 中学政治教学参考, 2023(29): 4-6.
[9]  祝智庭, 戴岭, 胡姣. 高意识生成式学习: AIGC技术赋能的学习范式创新[J]. 电化教育研究, 2023, 44(6): 5-14.
[10]  叶甘露, 高乔. 浅谈人工智能时代高职工程制图课程教学改革研究[J]. 时代汽车, 2025(12): 79-81.
[11]  刘芳平, 程龙飞, 郭远臣, 强跃, 闫磊. 面向“新工科范式”的传统工科专业改造升级路径研究——以土木工程类专业为例[J]. 三峡高教研究, 2020(1): 38-43.
[12]  邸臻炜, 谭洁霞, 刘树先. 人工智能时代工科应用型课程人机协同教学模式的构建与实践[J]. 梧州学院学报, 2024, 34(6): 88-96.
[13]  汤晨琦, 邵川华, 龚瑞晴. 技术赋能: 课堂分析与教学的范式转换——第二十届上海国际课程论坛述评[J]. 广东第二师范学院学报, 2023, 43(2): 61-72.

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