“新工科”背景下AI赋能工程案例教学的探索与实践——以地下水科学与工程专业为例

Exploration and Practice of AI-Enhanced Engineering Case Teaching in the Context of the “Emerging Engineering Education” Initiative—A Case Study of the Groundwater Science and Engineering Major

摘要: 本文立足“新工科”建设需求，聚焦地下水科学与工程专业案例教学，探索人工智能(AI)技术在工程案例教学中的应用路径与实践模式。传统案例教学存在内容陈旧、方法单一及资源有限等问题，结合地下水系统隐蔽性、数据获取难、污染物迁移过程复杂等专业特殊性，提出AI赋能解决方案。结合“地下水污染智能分析”与“智能地下水监测系统”两个典型案例，明确AI在污染源识别、扩散预测及系统优化中的具体应用，同时指出其在技术稳定性、数据安全、教师AI素养及教学评价等方面仍面临挑战。最后，从技术融合、教学实施与教育观念方面提出优化策略，并展望AI将进一步推动“AI + 专业”课程建设、智能自适应教学模式和跨学科融合教育的发展，为地下水领域高质量人才培养提供核心支持。

Abstract: This paper grounded on the demands of “new engineering” education by focusing on case-based teaching in groundwater science and engineering. It explores application pathways and practical models for integrating artificial intelligence (AI) technology into engineering case studies. Traditional case teaching suffers from outdated content, limited methodologies, and scarce resources. Considering the unique characteristics of groundwater systems—including their hidden nature, challenging data acquisition, and complex pollutant migration processes—this study proposes AI-empowered solutions. The study illustrates specific applications of AI in “Intelligent analysis of groundwater contamination" and “Intelligent groundwater monitoring systems” through two representative cases, focusing on pollution source identification, diffusion prediction, and system optimization. Nevertheless, challenges remain in terms of technical stability, data security, teachers’ AI proficiency, and teaching evaluation. Finally, optimization strategies are proposed from three perspectives: technological integration, teaching implementation, and educational philosophy. The study further envisions that AI will drive the development of “AI + Major” curriculum construction, intelligent adaptive teaching models, and interdisciplinary education, thereby providing core support for high-quality talent cultivation in the field of groundwater science and engineering.