AI赋能机械原理课程"数据驱动+场景融合"教学改革实践范式

Teaching Reform in Mechanism and Machine Theory Course: A Data-Driven and Scenario Integration Approach Empowered by AI

摘要:

机械原理课程是机械工程专业核心基础课程,具有理论抽象程度高与工程实践性强的双重特点。传统教学以讲授和静态演示为主,难以满足新工科人才培养对学生创新、实践及综合素养的需求。针对上述问题,依托智能化教学平台,确立了"数据驱动精准教学、场景融合深化实践"的改革思路。通过构建基于生成式AI的动态模拟场景解决认知壁垒,利用机器学习算法实施个性化路径推荐,借助大语言模型实现全过程智能评估。以平面四杆机构运动分析和凸轮设计为例,证实了该模式在破解"理解难"与"应用弱"等痛点上的有效性。实践显示,AI显著增强了课堂互动、学习效率与教学质量,推动学生从被动接受转向主动探究,有效提升了学生解决复杂工程问题的能力。量化数据与机制探析表明,教学改革成效显著,为新工科人才培养提供可推广的智能化教学范式。

Abstract:

The Mechanism and Machine Theory course is a core foundational course for mechanical engineering majors, characterized by highly abstract theory and strong engineering practicability. Traditional teaching, dominated by lecturing and static demonstrations, struggles to meet the demands of new engineering education for cultivating students’ innovation, practical abilities, and comprehensive qualities. To address these issues, supported by an intelligent teaching platform, this study establishes a reform approach of "data-driven precise teaching and scenario integration for deepened practice." It constructs generative AI-based dynamic simulation scenarios to resolve cognitive barriers, utilizes machine learning algorithms for personalized path recommendations, and leverages large language models for full-process intelligent assessment. Taking the motion analysis of planar four-bar mechanisms and cam design as examples, the study verifies the model’s effectiveness in tackling two persistent challenges in traditional pedagogy, namely students conceptual gaps and their inability to apply knowledge to practice. This study also shows that AI markedly enhances classroom interactivity, learning efficiency, and overall teaching quality, shifting students from passive reception to active inquiry and effectively developing their ability to solve complex engineering problems. Besides, Quantitative data and mechanism analysis indicate that the teaching reform has achieved remarkable results, providing a scalable and replicable intelligent teaching paradigm for cultivating new engineering talent.