人工智能赋能工科教学创新研究

Artificial Intelligence Empowers Innovation in Engineering Education

摘要: 随着人工智能技术的迅速发展，其在高等教育领域的深度融合正在推动教学理念、教学组织与教学模式的全方位革新。文章以本科工科教学为研究对象，构建了“课前–课中–课后”的AI赋能全过程教学框架，讨论了AI技术在教学资源生成、课堂实施、学习支持与教学评价等环节的创新应用路径。研究发现：在课前，AI可提升教学资源开发效率并优化内容呈现；在课中，虚实融合与智能分析技术可增强课堂互动与学习体验；在课后，智能诊断与个性化推荐支持学生持续学习，促进学习闭环形成。同时，文章以典型工科课程案例为例，展示了AI工具在作业批改、实验仿真、学习反馈分析等方面的应用成效。研究指出，AI在工科教学中的深度应用仍面临教师AI素养不足与教学数据安全等问题。针对以上问题，文章提出相应对策建议，研究成果可为AI赋能高等教育改革提供实践参考。

Abstract: The rapid advancement of artificial intelligence (AI) is driving comprehensive transformation in higher education, revolutionizing teaching philosophies, instructional organization, and pedagogical models. This paper develops an AI-enabled instructional framework for undergraduate engineering education that spans three interconnected phases: pre-class, in-class, and post-class. Within this framework, we systematically examine innovative applications of AI technology across key educational dimensions—including resource generation, classroom implementation, learning support, and evaluation. Our findings demonstrate that AI significantly enhances resource development efficiency and content presentation during pre-class preparation; improves classroom interaction and learning experience through virtual-real integration and intelligent analytics during instruction; and facilitates continuous learning through intelligent diagnostics and personalized recommendations after class. Furthermore, through case studies of representative engineering courses, we validate the effectiveness of AI tools in automated assignment grading, experimental simulation, and learning feedback analysis. The study also identifies persistent challenges in deep implementation, particularly insufficient AI literacy among educators and data security concerns. Corresponding strategic recommendations are proposed to address these issues. This research provides valuable practical insights for AI-enabled reform in engineering education and offers reference for broader higher education innovation.