Title: Large Language Models in Architecture Studio: A Framework for Learning Outcomes Show Chinese title

Title: 大型语言模型在建筑工作室中的应用：学习成果的框架

Abstract: The study explores the role of large language models (LLMs) in the context of the architectural design studio, understood as the pedagogical core of architectural education. Traditionally, the studio has functioned as an experiential learning space where students tackle design problems through reflective practice, peer critique, and faculty guidance. However, the integration of artificial intelligence (AI) in this environment has been largely focused on form generation, automation, and representation-al efficiency, neglecting its potential as a pedagogical tool to strengthen student autonomy, collaboration, and self-reflection. The objectives of this research were: (1) to identify pedagogical challenges in self-directed, peer-to-peer, and teacher-guided learning processes in architecture studies; (2) to propose AI interventions, particularly through LLM, that contribute to overcoming these challenges; and (3) to align these interventions with measurable learning outcomes using Bloom's taxonomy. The findings show that the main challenges include managing student autonomy, tensions in peer feedback, and the difficulty of balancing the transmission of technical knowledge with the stimulation of creativity in teaching. In response to this, LLMs are emerging as complementary agents capable of generating personalized feedback, organizing collaborative interactions, and offering adaptive cognitive scaffolding. Furthermore, their implementation can be linked to the cognitive levels of Bloom's taxonomy: facilitating the recall and understanding of architectural concepts, supporting application and analysis through interactive case studies, and encouraging synthesis and evaluation through hypothetical design scenarios. Show Chinese abstract

Abstract: 该研究探讨了大型语言模型（LLMs）在建筑设计工作室环境中的作用，该环境被视为建筑教育的教学核心。 传统上，工作室作为一个体验式学习空间，学生通过反思实践、同伴批评和教师指导来解决设计问题。 然而，在这一环境中人工智能（AI）的整合主要集中在形式生成、自动化和表现-效率方面，忽视了其作为教学工具的潜力，以增强学生的自主性、协作能力和自我反思。 本研究的目标是：(1) 识别建筑学习过程中自主学习、同伴间学习和教师指导学习过程中的教学挑战；(2) 提出通过LLM实现的AI干预措施，以克服这些挑战；以及(3) 使用布鲁姆分类法将这些干预措施与可衡量的学习成果对齐。 研究结果表明，主要挑战包括管理学生自主性、同伴反馈中的紧张关系，以及在教学中平衡技术知识的传递与创造力的激发的难度。 针对这些问题，LLMs 正逐渐成为能够生成个性化反馈、组织协作互动并提供适应性认知支架的补充代理。 此外，它们的实施可以与布鲁姆分类法的认知层次相联系：促进建筑概念的回忆和理解，通过互动案例研究支持应用和分析，并通过假设设计场景鼓励综合和评估。