工程教育中用AI要平衡效率与风险，关键在明确目的。
Using AI in engineering education: a balancing act, driven by clear purpose
- 用问卷和文献分析学生用大模型的四种场景
- 发现学生依赖AI但担忧错误与学术诚信问题
- 适合关注AI教学伦理与批判性思维培养的人
基于100名高校工程类学生问卷调查及近期文献的批判性回顾，本章探讨学生在工程教育中使用和感知大语言模型（LLMs）的情况。学生主要将LLMs用于写作辅助、概念澄清、编程帮助和头脑风暴，同时对准确性、偏见、过度依赖、学术诚信及验证负担表示担忧。通过分析‘预言者’和‘导师’两种主导隐喻，本章指出这些系统往往引发对权威性、专业性和个性化学习的过高期待，而实际能力有限。研究认为，学生对效率与个性化支持的依恋体现了一种‘残酷的乐观’：其收益往往依赖于学生尚未完全掌握的技能、警惕性和专业知识。因此，本章主张在工程教育中采取以目标为导向、情境敏感的AI整合策略，强调批判性AI素养、反思性评估设计、教学谨慎性以及对更广泛伦理与环境影响的考量。
原文摘要 · Abstract (English)
Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Models (LLMs) in engineering education. Students primarily value LLMs for writing support, conceptual clarification, coding assistance, and brainstorming, while simultaneously expressing concerns about inaccuracies, bias, overreliance, academic integrity, and the burden of verification. Through an analysis of two dominant metaphors, namely LLMs as an "oracle" and as a "tutor," the chapter shows how these systems cultivate expectations of authority, expertise, and personalized learning that often exceed their actual capabilities. The chapter further argues that students' attachment to the promises of efficiency and personalized support reflects a form of "cruel optimism," where the perceived benefits of LLMs often depend on the very skills, vigilance, and expertise that students are still developing. Overall, the chapter argues for a purpose-driven and context-sensitive approach to AI integration in engineering education, emphasizing critical AI literacy, reflective assessment design, pedagogical caution, and consideration of broader ethical and environmental impacts.
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