Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio

Abstract: This study investigates the integration of 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 architecture students at the Singapore Uni-versity of Technology and Design, the research analyzes student percep-tions across three distinct feedback domains: self-reflection, peer critique, and professor-led reviews. The findings reveal that students engage with LLMs not as authoritative instructors, but as collaborative "cognitive mir-rors" that scaffold critical thinking. In self-directed learning, LLMs help structure thoughts and overcome the "blank page" problem, though they are limited by a lack of contextual nuance. In peer critiques, the technology serves as a neutral mediator, mitigating social anxiety and the "fear of of-fending". Furthermore, in high-stakes professor-led juries, students utilize LLMs primarily as post-critique synthesis engines to manage cognitive overload and translate abstract academic discourse into actionable design iterations.