Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912870528188416 |
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| author | Rodriguez, Juan David Salazar Joyce, Sam Conrad Sockalingam, Nachamma Tat, Khoo Eng Julfendi |
| author_facet | Rodriguez, Juan David Salazar Joyce, Sam Conrad Sockalingam, Nachamma Tat, Khoo Eng Julfendi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_00041 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio Rodriguez, Juan David Salazar Joyce, Sam Conrad Sockalingam, Nachamma Tat, Khoo Eng Julfendi Computers and Society Artificial Intelligence Human-Computer Interaction 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. |
| title | Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.00041 |