TY - GEN
T1 - An Adoption of AI in Architectural Education through an Analysis of Stable Diffusion Practice-Based Lectures
AU - Hong, Soon Min
AU - Park, Jungmin
AU - Park, Gaeun
AU - Gu, Hyeongmo
AU - Heo, Minji
AU - Chin, Sangyoon
AU - Choo, Seungyeon
N1 - Publisher Copyright:
© 2025, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
PY - 2025
Y1 - 2025
N2 - This study investigates the pedagogical potential of integrating Stable Diffusion, a generative AI model, into architectural design education. Over two consecutive years, a practical design course incorporating AI-based tools was conducted with undergraduate architecture students. In Course 2023, students explored image generation using Stable Diffusion primarily as a novel visualization tool. In Course 2024, improvements in both the learning environment and curriculum enabled students to engage more creatively and strategically, integrating Stable Diffusion with existing design tools such as Rhino and Photoshop. Comparative analysis of student outcomes and course evaluations indicates that generative AI can enhance creative thinking, design expression, and engagement in the architectural studio. However, the study also highlights the importance of educational strategies in maximizing AI’s value as a design partner. The findings suggest that effective implementation of generative AI in architectural education requires not only technological access but also curriculum frameworks that foster exploratory and reflective learning.
AB - This study investigates the pedagogical potential of integrating Stable Diffusion, a generative AI model, into architectural design education. Over two consecutive years, a practical design course incorporating AI-based tools was conducted with undergraduate architecture students. In Course 2023, students explored image generation using Stable Diffusion primarily as a novel visualization tool. In Course 2024, improvements in both the learning environment and curriculum enabled students to engage more creatively and strategically, integrating Stable Diffusion with existing design tools such as Rhino and Photoshop. Comparative analysis of student outcomes and course evaluations indicates that generative AI can enhance creative thinking, design expression, and engagement in the architectural studio. However, the study also highlights the importance of educational strategies in maximizing AI’s value as a design partner. The findings suggest that effective implementation of generative AI in architectural education requires not only technological access but also curriculum frameworks that foster exploratory and reflective learning.
KW - AI in Architecture
KW - Architectural Education
KW - Architectural Image Generation
KW - Stable Diffusion
UR - https://www.scopus.com/pages/publications/105026172061
U2 - 10.52842/conf.ecaade.2025.1.855
DO - 10.52842/conf.ecaade.2025.1.855
M3 - Conference contribution
AN - SCOPUS:105026172061
SN - 9789491207396
T3 - Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
SP - 855
EP - 864
BT - Proceedings of the 43rd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2025
A2 - Sorguç, Arzu Gönenç
A2 - Yemişcioğlu, Müge Kruşa
A2 - Erol, Serda Buket
A2 - Bük, Mustafa Eren
A2 - Güney, Dilara
A2 - Sulayıcı, Betül Aktaş
A2 - Akol, Mert
PB - Education and research in Computer Aided Architectural Design in Europe
T2 - 43rd Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2025
Y2 - 1 September 2025 through 5 September 2025
ER -