Teaching with Artificial Intelligence in Architecture: Embedding Technical Skills and Ethical Reflection in a Core Design Studio
Abstract
1. Introduction
1.1. AI in Architectural Practice
1.2. AI in Architectural Education
1.3. Educational Feedback and Pedagogical Challenges
1.4. Summary
- To develop a pedagogical framework for systematically embedding AI content into undergraduate core design studios while maintaining disciplinary coherence.
- To investigate how AI tools are applied across different stages of the design process and how student engagement varies accordingly.
- To evaluate students’ perceptions of AI in supporting creative development, managing expression, and fostering ethical awareness, based on feedback and in-studio observations.
- To identify key practical challenges and opportunities associated with AI integration.
2. Course Design and Pedagogical Framework
2.1. Context of Curriculum Reform
2.2. Overview of Architectural Design III/IV Studio
- The integration of AI technologies into architectural pedagogy remains in its nascent stage, with few established instructional models or empirically grounded precedents to guide curriculum development.
- Students predominantly rely on conventional design thinking and procedural habits, which limits their ability to engage with AI tools in a conceptually coherent and methodologically structured manner.
- Students’ understanding of AI is often fragmented and disproportionately focused on visual outputs, highlighting the urgent need for a more structured integration throughout the full design process.
2.3. Modular Integration of AI Skills and AI Ethics Components
2.4. Survey Design for Student Feedback
3. Studio Implementation and Results
3.1. AI-Integrated Design Development
3.2. AI-Integrated Visual Representation
3.3. AI-Integrated Pre-Design Research Stage
3.4. Survey Results
4. Discussions
4.1. Modular Integration Enables Scalable and Responsive Curriculum Reform
4.2. Student Engagement and the Challenge of Deep AI Integration
4.3. Embedding Ethical Thinking Through AI-Integrated Design Practice
5. Conclusions
- Modular integration is both practical and scalable: Embedding a 20 h AI skills module alongside in-class ethics discussions proved effective without altering the original studio structure, offering a replicable model for AI-integrated design education.
- A dedicated technical instructor is essential: The rapid evolution of AI tools requires real-time updates and hands-on support beyond the capacity of traditional studio instructors. Assigning a technical lead enabled the consistent, up-to-date delivery of AI instruction throughout the course cycle.
- Student engagement is phase-dependent and tool-sensitive: Survey responses indicated higher engagement during the pre-design research and design development phases. Students demonstrated relatively consistent familiarity with large language models, while their perceptions of more complex systems such as image generation tools and custom workflows varied significantly, reflecting differences in technical confidence and cognitive readiness.
- AI integration promotes both cognitive growth and ethical orientation: The pedagogical value of AI lies in strengthening students’ strategic thinking, digital adaptability, and critical design judgment while also encouraging ethical reflection on issues such as authorship, bias, accountability, and the broader implications of human–AI collaboration. Rather than treating AI as a black box generator, students are positioned to engage with it as a co-author requiring intentional guidance and responsibility.
- Supportive infrastructure underpins successful implementation: Robust institutional systems, such as e-learning platforms, cloud computing resources, and access to research-based tools including pretrained models and custom workflows, were essential to enabling meaningful experimentation and sustained technical engagement.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial Intelligence |
AIGC | Artificial Intelligence-Generated Content |
LLM | Large Language Model |
LoRA | Low-Rank Adaptation |
CLIP | Collaborative Layout Integration Platform |
Appendix A
1. Demographical Background | ||||
1.1 Your name: | ||||
1.2 Your student no.: | ||||
1.3 Your gender: | ||||
1.4 Have you participated in the AI modules embedded in the Architectural Design Studio at Zhejiang University? | ||||
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1.5 Please rate the extent to which this AI module benefited your design learning: | ||||
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2. Assessment of AI tool effectiveness | ||||
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2.1: AI tools demonstrate strong information synthesis capabilities (e.g., organizing large volumes of input documents). | ||||
2.2: AI tools demonstrate strong analytical reasoning capabilities (e.g., deriving insights from input materials). | ||||
2.3: AI tools demonstrate strong creative thinking capabilities (e.g., generating unconventional ideas). | ||||
2.4: AI tools demonstrate strong visual representation capabilities (e.g., generating qualified images). | ||||
3. Descriptive feedback of AI-integrated teaching | ||||
3.1 To what extent have you used AI tools in architectural design studios? | ||||
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3.2 In which phases of architectural design studios have you used AI tools? (Multiple choices) | ||||
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3.3 Rank the practicality of the following AIGC tools (1–4): (Respondents were only presented with this ranking task if they had selected the corresponding phase in question 3.2.) | ||||
Phase | AI Tools | |||
3.3.1 Research |
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3.3.2 Design |
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3.3.3 Representation |
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3.4 Will you consider using AI tools in future design projects? | ||||
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3.5 Do you think formal teaching and classroom discussions on AI ethics are necessary? | ||||
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Week | Class-Hours 1 | Design Task | AI Ethics Module | AI Skills Module 2 |
---|---|---|---|---|
Fall 1–8 | 64 | Architecture & Re-build | Ethic Ⅰ: Data Privacy and Copyright | Skill Ⅰ (2 class-hours): Foundations of AI |
Skill Ⅱ (4 class-hours): Prompt Engineering and Image Generation | ||||
Fall 9–16 | 64 | System & Synthesis | Ethic Ⅱ: Accountability in Human–AI Collaboration | Skill Ⅲ (4 class-hours): LoRA Model Training and Fine-tuning |
Skill Ⅳ-1 (3 class-hours): ComfyUI Workflow—Basics | ||||
Summer 1–4 | 32 | X & Hypothesis | Ethic Ⅲ: Stylistic Authorship and Cultural Bias | Skill Ⅳ-2 (3 class-hours): ComfyUI Workflow—Advanced |
Summer 5–16 | 96 | Urban & Renewal | Ethic Ⅳ: Model Bias | Skill Ⅴ (4 class-hours): Multimodal Integration |
Category | Option | n | Percentage (%) |
---|---|---|---|
Gender | Male | 38 | 54.3% |
Female | 32 | 45.7% | |
Participation in AI-integrated Coursework | Attended lectures only | 17 | 24.3% |
Attended lectures and took exercises | 32 | 45.7% | |
Practiced exercises and applied AI tools in design studio | 21 | 30.0% | |
Perceived Benefit | 1: Not beneficial at all | 1 | 1.4% |
2: Slightly beneficial | 2 | 2.9% | |
3: Moderately beneficial | 25 | 35.7% | |
4: Very beneficial | 25 | 35.7% | |
5: Extremely beneficial | 17 | 24.3% | |
Overall AI Tool Usage in Architectural Design Studios | 1: Never used | 14 | 13.7% |
2: Briefly experimented | 52 | 51.0% | |
3: Used multiple times for process discussions | 30 | 29.4% | |
4: Frequently used for multiple tasks | 6 | 5.9% |
Question | Mean (M) | Standard Deviation (SD) | Percentage Agree (4–5) |
---|---|---|---|
AI tools demonstrate strong information synthesis capabilities | 4.28 | 0.84 | 84.1% |
AI tools demonstrate strong analytical reasoning capabilities | 3.86 | 0.94 | 67.1% |
AI tools demonstrate strong creative thinking capabilities | 3.46 | 1.02 | 44.3% |
AI tools demonstrate strong visual representation capabilities | 3.47 | 1.03 | 44.3% |
Design Stage | n | Percentage (%) |
---|---|---|
Pre-design Research | 39 | 55.7 |
Design Development | 44 | 62.9 |
Final Representation | 22 | 31.4 |
Tool Type | Mean Rank (Research) | Top-1 Count (Research) | Mean Rank (Design) | Top-1 Count (Design) | Mean Rank (Representation) | Top-1 Count (Representation) |
---|---|---|---|---|---|---|
Large Language Models | 1.35 | 25 | 2.07 | 15 | 2.47 | 4 |
Multimodal Models | 1.74 | 14 | 2.16 | 14 | 2.64 | 3 |
AIGC Image Tools | 3.04 | 0 | 2.41 | 9 | 1.80 | 10 |
Custom AI Workflows | 3.59 | 0 | 2.82 | 6 | 2.14 | 5 |
Category | Option | n | Percentage (%) |
---|---|---|---|
Future Use of AI Tools | Yes, I am very interested and plan to increase my use | 18 | 25.7% |
Yes, but I believe current tools are not yet mature and I will observe how they evolve | 40 | 57.1% | |
Not for now, but I will stay informed | 10 | 14.3% | |
No, I do not plan to use them due to high technical barriers | 1 | 1.4% | |
No, I do not find AI tools relevant or useful | 1 | 1.4% | |
AI Ethics Teaching Attitude | Yes, formal teaching and structured classroom discussions are necessary | 20 | 28.6% |
Somewhat necessary, but informal discussion or self-reflection may be sufficient | 46 | 65.7% | |
Not necessary | 4 | 5.7% | |
No opinion or not sure | 0 | 0 |
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Share and Cite
Wang, J.; Shi, Y.; Chen, X.; Lan, Y.; Liu, S. Teaching with Artificial Intelligence in Architecture: Embedding Technical Skills and Ethical Reflection in a Core Design Studio. Buildings 2025, 15, 3069. https://doi.org/10.3390/buildings15173069
Wang J, Shi Y, Chen X, Lan Y, Liu S. Teaching with Artificial Intelligence in Architecture: Embedding Technical Skills and Ethical Reflection in a Core Design Studio. Buildings. 2025; 15(17):3069. https://doi.org/10.3390/buildings15173069
Chicago/Turabian StyleWang, Jiaqi, Yu Shi, Xiang Chen, Yi Lan, and Shuying Liu. 2025. "Teaching with Artificial Intelligence in Architecture: Embedding Technical Skills and Ethical Reflection in a Core Design Studio" Buildings 15, no. 17: 3069. https://doi.org/10.3390/buildings15173069
APA StyleWang, J., Shi, Y., Chen, X., Lan, Y., & Liu, S. (2025). Teaching with Artificial Intelligence in Architecture: Embedding Technical Skills and Ethical Reflection in a Core Design Studio. Buildings, 15(17), 3069. https://doi.org/10.3390/buildings15173069