学生用大模型辅助设计反思，提升批判性思维与表达能力。
Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio
- 将大模型作为思维伙伴，帮助梳理想法、克服创作瓶颈。
- 在同伴互评中降低社交压力，提供中立反馈支持。
- 在导师评审后用于整合意见，实现从抽象讨论到具体改进的转化。
本研究探讨大型语言模型（LLMs）在建筑学设计工作室反馈机制中的应用，推动从生成性创作向反思性教学的转变。通过对新加坡科技设计大学22名建筑专业学生的问卷调查与半结构化访谈，分析学生在自我反思、同伴评议和教师主导评审三个反馈场景中的感知。结果表明，学生并未将大模型视为权威指导者，而是将其作为协作型“认知镜像”，辅助批判性思考。在自主学习中，大模型帮助组织思路、应对“空白页困境”，但受限于缺乏情境细节；在同伴互评中，其作为中立中介缓解社交焦虑与“得罪他人”的恐惧；在高风险教师评审环节，学生主要将其用作评审后的综合工具，以缓解认知负荷，并将抽象学术话语转化为可操作的设计调整。
原文摘要 · Abstract (English)
This study investigates the integration of Large Language Models (LLMs) into the feedback mechanisms of the architectural design studio, shifting the focus from generative production to reflective pedagogy. Employing a mixed-methods approach with surveys and semi structured interviews with 22 architecture students at the Singapore University of Technology and De-sign, the research analyzes student perceptions across three distinct feed-back domains: self-reflection, peer critique, and professor-led reviews. The findings reveal that students engage with LLMs not as authoritative in-structors, but as collaborative "cognitive mirrors" that scaffold critical thinking. In self-directed learning, LLMs help structure thoughts and over-come the "blank page" problem, though they are limited by a lack of contex-tual nuance. In peer critiques, the technology serves as a neutral mediator, mitigating social anxiety and the "fear of offending". Furthermore, in high-stakes professor-led juries, students utilize LLMs primarily as post-critique synthesis engines to manage cognitive overload and translate ab-stract academic discourse into actionable design iterations.
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