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.

翻译：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实践，将关注点从生成式产出转向反思性教学法。研究采用混合方法，通过对新加坡科技设计大学22名建筑学学生进行问卷调查和半结构化访谈，分析了学生在三个不同反馈领域中的认知：自我反思、同伴评图和教授主导的评图。研究结果表明，学生并非将LLMs视为权威指导者，而是将其作为支撑批判性思维的协作式“认知镜像”。在自主学习中，LLMs有助于结构化思维并克服“空白页”困境，但其局限性在于缺乏情境细微差异的理解。在同伴评图中，该技术充当了中立调解者，缓解了社交焦虑和“冒犯恐惧”。此外，在由教授主导的高风险评图环节中，学生主要将LLMs用作评图后的综合处理引擎，以管理认知负荷并将抽象的学术论述转化为可操作的设计迭代方案。