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.

翻译：基于对100名高等教育学生（主要来自工程相关领域）的问卷调查以及对近期文献的批判性评述，本章考察了学生在工程教育中如何使用和感知大型语言模型（LLMs）。学生主要看重LLMs在写作支持、概念澄清、编程辅助和头脑风暴方面的能力，同时也表达了对不准确性、偏见、过度依赖、学术诚信以及验证负担的担忧。通过分析两个主要隐喻——即将LLMs视为“神谕”和“导师”，本章展示了这些系统如何培养出往往超出其实际能力的权威性、专业性和个性化学习期望。本章进一步论证，学生对效率和个性化支持承诺的依恋反映了一种“残酷的乐观主义”，即LLMs所带来的感知益处，往往有赖于学生自身仍在发展的那些技能、警觉性和专业知识。总体而言，本章主张在工程教育中采用一种目标驱动且情境敏感的方法来整合人工智能，强调了批判性AI素养、反思性评估设计、教学审慎以及对更广泛的伦理和环境影响的考量。