教学生如何批判性、负责任地使用大模型，提升技术理解与反思能力。
Demystify, Use, Reflect: Preparing students to be informed LLM-users
- 通过讲解原理、工具使用与伦理问题，构建系统化AI学习路径
- 学生对LLM的认知更技术化，使用更审慎且注重验证与协作
- 适合高校计算机通识课或希望培养AI素养的教育者参考
我们重构了面向非专业学生的后CS1课程，将大型语言模型（LLMs）以结构化、批判性和实践性方式融入教学，旨在帮助学生掌握与AI有效、负责任互动的能力。课程包含对LLM工作原理的明确讲解、当前工具的体验、伦理议题讨论，以及鼓励学生反思个人使用习惯和AI辅助编程的发展趋势。课堂中，教师演示输出验证方法，指导学生将LLM作为解决问题循环中的一个环节，并要求披露和承认使用LLM的程度。全课程贯穿对各计算机科学子领域中LLM风险与收益的探讨。在首轮实施中，通过前后测数据收集分析发现，学生对LLM的理解趋于技术化，其输出验证与使用方式变得更加审慎且具有协作性。这些策略可推广至其他课程，助力学生迎接人工智能融合的未来。
原文摘要 · Abstract (English)
We transitioned our post-CS1 course that introduces various subfields of computer science so that it integrates Large Language Models (LLMs) in a structured, critical, and practical manner. It aims to help students develop the skills needed to engage meaningfully and responsibly with AI. The course now includes explicit instruction on how LLMs work, exposure to current tools, ethical issues, and activities that encourage student reflection on personal use of LLMs as well as the larger evolving landscape of AI-assisted programming. In class, we demonstrate the use and verification of LLM outputs, guide students in the use of LLMs as an ingredient in a larger problem-solving loop, and require students to disclose and acknowledge the nature and extent of LLM assistance. Throughout the course, we discuss risks and benefits of LLMs across CS subfields. In our first iteration of the course, we collected and analyzed data from students pre and post surveys. Student understanding of how LLMs work became more technical, and their verification and use of LLMs shifted to be more discerning and collaborative. These strategies can be used in other courses to prepare students for the AI-integrated future.
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