Date
Publisher
arXiv
This study investigates AI integration in architectural education through a teaching experiment in Zhejiang University's 2024-25 grade three undergraduate design studio. Adopting a dual-module framework (20-hour AI training + embedded ethics discussions), the course introduced deep learning models, LLMs, AIGC, LoRA, and ComfyUI while maintaining the original curriculum structure, supported by dedicated technical instructors. Findings demonstrate the effectiveness of phased guidance, balanced technical-ethical approaches, and institutional support. The model improved students' digital skills and strategic cognition while addressing AI ethics, providing a replicable approach combining technical and critical learning in design education.
Study at a glance
- Question: How can modular artificial intelligence training and ethical discussions be integrated into an undergraduate architecture design course?
- Setting and data: Zhejiang University, third-year undergraduate architecture design course, 2024–2025 academic year, 70 student survey respondents.
- Method: Mixed methods case study with student surveys, semi-structured interviews, and student project reviews.
Why it matters
University training in artificial intelligence tools often lags behind architectural industry practices. Integrating technical skills alongside critical ethical awareness into design curricula requires tested teaching experiments.
Key findings
- Overall, 95.7% of surveyed students reported benefiting from the course, with 82.8% expressing positive attitudes toward future artificial intelligence use.
- Students gave artificial intelligence the highest effectiveness ratings for organizing information (84.1%) and logical analysis (67.1%), but lower ratings for innovative thinking and design presentation (both 44.3%).
- Artificial intelligence tool use was highest during detailed design (62.9%) and site research (55.7%), but lowest during final design presentation (31.4%).
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
