人工智能赋能工程地质课程的教学创新——以“石头记”学习系统为例

Teaching Innovation in Engineering Geology Courses Enabled by Artificial Intelligence—A Case Study of the “Stone Record” Learning System

摘要: 随着人工智能技术的迅猛发展，高等工程教育面临着创新教学模式、提升课程质量和落实“三全育人”目标的多重挑战。工程地质作为土木、道桥等工程类专业的核心课程，长期存在内容繁杂、标本受限、学习兴趣不足、思政育人难以有效融合等痛点。文章以“石头记”三大岩石学习系统为例，系统阐述了人工智能大模型赋能工程地质课程的创新设计与深度融合路径。论文详细分析了课程教学的现实困境与改革需求，介绍了“石头记”系统基于国产DeepSeek大模型开发技术架构、双循环学习模式、模块化功能分区以及课程思政的有机嵌入。通过在实际教学过程中的应用与成效评估，结果显示系统显著提升了学生的学习兴趣、理论与实践能力以及价值观认同，教师教学效率和课堂互动性亦得到明显增强。研究表明，人工智能赋能下的工程地质课程教学创新，为高等工程教育的高质量发展和专业人才培养提供了案例。

Abstract: With the rapid advancement of artificial intelligence (AI) technologies, higher engineering education is facing the dual challenges of innovating teaching models, enhancing course quality, and realizing the “all-round education” objectives. As a core course for majors such as Civil Engineering and Bridge Engineering, Engineering Geology has long suffered from issues including complex content, limited access to physical specimens, lack of student engagement, and difficulties in effectively integrating value-based education. This paper takes the “Stone Record” learning system for the three major rock types as an example, systematically explaining the innovative design and in-depth integration path of AI large language models driving Engineering Geology course reform. The study analyzes the practical challenges and reform needs of course teaching, introduces the technical architecture of the “Stone Record” system based on the domestic DeepSeek large model, its dual-cycle learning approach, modular functional structure, and the organic embedding of value-based education and aesthetic education. Through practical teaching applications and outcome evaluations, results show that the system significantly increases student interest, theoretical mastery, practical skills, and value identification, while also enhancing teaching efficiency and classroom interactivity for instructors. Finally, the paper discusses future directions for the ongoing integration of AI and Engineering Geology courses, advocating for the development of intelligent teaching scenarios, improvement of personalized learning support, and the coordinated advancement of curriculum education and intelligent technology. The research demonstrates that AI-driven innovation in Engineering Geology course teaching provides an effective paradigm and a sustainable path for high-quality development in higher engineering education and professional talent cultivation.