AI 中文总结
本研究针对工程力学课程设计了含九次结构化AI演示的方法论框架，结合调查工具分析学生LLM使用情况，为工程教育应对LLM融入提供实证研究基础。
AI 中文摘要
大型语言模型（LLM）快速融入本科教育，给工程类教师带来了紧迫挑战。尽管学生广泛采用LLM，但仍缺乏指导教学政策与课堂干预的领域实证证据。本文呈现2026年春季开展的本科工程力学课程的描述性研究设计与初步发现。我们详述了可重复使用的调查工具，用于捕捉学生的AI使用模式、态度及验证实践，并将其与学业表现指标关联；此外，我们记录了一套可部署的九次结构化、教师主导的AI演示序列，旨在示范战略性LLM委托与评估。初步数据显示学生行为正在转变，且AI依赖与课程成果间存在复杂关系，本工作的主要贡献是提供了一个开放获取的方法论框架。通过公开完整的研究设计、调查工具及演示材料，我们呼吁其他工程教育工作者收集并分享类似实证数据。应对这一前所未有的技术转变，需采用协作、循证的方法以充分理解其对学生学习的长期影响。
英文摘要
The rapid integration of large language models (LLMs) into undergraduate education presents an urgent challenge for engineering instructors. Despite widespread student adoption, there remains a critical lack of domain-specific empirical evidence to guide pedagogical policies and classroom interventions. This manuscript presents a descriptive study design and preliminary findings from an undergraduate engineering mechanics course conducted in Spring 2026. We detail a reproducible survey instrument used to capture student AI usage patterns, attitudes, and verification practices, which are subsequently linked to academic performance metrics. Additionally, we document a deployable sequence of nine structured, instructor-led AI demonstrations designed to model strategic LLM delegation and evaluation. While our preliminary data highlight shifting student behaviors and complex relationships between AI reliance and course outcomes, the primary contribution of this work is the provision of an open-access methodological framework. By making our complete study design, survey tools, and demonstration materials publicly available, we urge other engineering educators to collect and share similar empirical data. Navigating this unprecedented technological shift will require a collaborative, evidence-based approach to fully understand its long-term impacts on student learning.
Comments47 pages, 10 figures, 9 tables
