学生对在建筑工作室中使用大型语言模型进行自我反思和设计批评的看法
Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio
AI总结：
本研究探讨了大型语言模型在建筑工作室自我反思与设计批评中的应用，发现学生将其视为协作工具，帮助构建批判性思维并提升设计迭代效率。
摘要：
本研究探讨了将大型语言模型（LLMs）整合到建筑设计工作室反馈机制中的整合，将焦点从生成性生产转向反思性教学。采用混合方法，对22名新加坡技术与设计大学建筑学生的调查和半结构化访谈，分析学生在三个不同的反馈领域中的看法：自我反思、同伴批评和由教授主导的审查。研究发现，学生与LLMs互动并非作为权威教师，而是作为协作的'认知镜像'，帮助构建批判性思维。在自主学习中，LLMs帮助组织思路并克服'空白页面'问题，尽管它们受限于缺乏上下文细微差别。在同伴批评中，该技术作为中立调解者，缓解了社交焦虑和'冒犯恐惧'。此外，在高风险教授主导的审查中，学生主要将LLMs用作批判后综合引擎，以管理认知超载并将抽象学术话语转化为可行的设计迭代。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
