arXiv:2510.19342cs.CYcs.AI2025-10

引导学生反思AI使用角色,将效率转化为创新设计能力。

To Use or to Refuse? Re-Centering Student Agency with Generative AI in Engineering Design Education

  • 以工具、伙伴、拒绝三重角色引导学生思考AI使用方式。
  • 学生学会主动拒绝错误输出,用节省时间深化用户研究。
  • 适合关注AI教育实践与设计思维培养的师生参考。

本试点研究跟踪了新加坡科技设计大学超过500名大一工程与建筑专业学生在为期13周的基础设计课程中对生成式AI的使用反思。课程为融合AI的设计课,设置了多项干预措施以培养学生基于AI的设计技能。学生需反思技术是作为工具(辅助)、伙伴(协作)或拒绝使用(有意回避)。通过这一三重视角,学生不仅提升创新而非仅自动化能力,更聚焦于自主性、伦理与情境,而不仅是提示词优化。证据来自13份结构化反思表、8份图文简报及教师与研究者笔记。定性编码显示,引入生成式AI后涌现出共享实践:加速原型制作、快速技能获取、迭代提示优化、用户研究阶段有意识关闭AI、以及识别幻觉的临时应对机制。意外发现:学生不仅利用AI提速,还(在三重角色框架支持下)学会拒绝其输出,自创幻觉应对演练,并将省下的时间投入更深入的用户研究,从而将效率转化为创新。该方法启示:可将AI应用转化为可评估的设计习惯;奖励选择性不用能培养幻觉敏感的工作流;实际操作上,通过工具访问、反思训练、角色标记和竞赛表彰等协同策略,可在不牺牲问责的前提下实现教育中基于AI的创新规模化。

原文摘要 · Abstract (English)

This pilot study traces students' reflections on the use of AI in a 13-week foundational design course enrolling over 500 first-year engineering and architecture students at the Singapore University of Technology and Design. The course was an AI-enhanced design course, with several interventions to equip students with AI based design skills. Students were required to reflect on whether the technology was used as a tool (instrumental assistant), a teammate (collaborative partner), or neither (deliberate non-use). By foregrounding this three-way lens, students learned to use AI for innovation rather than just automation and to reflect on agency, ethics, and context rather than on prompt crafting alone. Evidence stems from coursework artefacts: thirteen structured reflection spreadsheets and eight illustrated briefs submitted, combined with notes of teachers and researchers. Qualitative coding of these materials reveals shared practices brought about through the inclusion of Gen-AI, including accelerated prototyping, rapid skill acquisition, iterative prompt refinement, purposeful "switch-offs" during user research, and emergent routines for recognizing hallucinations. Unexpectedly, students not only harnessed Gen-AI for speed but (enabled by the tool-teammate-neither triage) also learned to reject its outputs, invent their own hallucination fire-drills, and divert the reclaimed hours into deeper user research, thereby transforming efficiency into innovation. The implications of the approach we explore shows that: we can transform AI uptake into an assessable design habit; that rewarding selective non-use cultivates hallucination-aware workflows; and, practically, that a coordinated bundle of tool access, reflection, role tagging, and public recognition through competition awards allows AI based innovation in education to scale without compromising accountability.

AI教育设计思维生成式AI

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