AI in Architectural Education: Rethinking Studio Culture
Open accessAbstract
This article examines the pedagogical transformations emerging in architectural education through a conceptual and critical perspective focused on human–AI co-creativity. Co-creativity specifically refers to collaborations between human designers and artificial intelligence, in contrast to broader notions of collaborative creativity. The paper argues that AI functions not merely as a technical instrument, but as a co-creative partner that reshapes studio culture, authorship, and creative work. Drawing on selected studio-based cases, the study explores how AI-supported workflows influence ideation, representation, critique culture, prompt literacy, and ethical reasoning. Thematically, it engages with concepts such as cognitive augmentation and conceptual ambiguity to demonstrate how design pedagogy is evolving in response to intelligent systems. Rather than viewing AI as a generative tool alone, the article positions it as an epistemic and ethical agent that prompts a rethinking of studio environments as cultural and pedagogical spaces. Methodologically, the study adopts a case-based approach, analysing selected 16 design studios in which AI was integrated into early-stage ideation, feedback sessions, and conceptual development. These cases extent strategies from prompt-driven speculation to hybrid critique practices, revealing a dynamic landscape of experimentation and adaptation. The findings suggest that AI can foster deeper conceptual inquiry, student reflection, and new modalities of authorship and collaboration. Eventually, the study underscores the need for reflexive pedagogical frameworks that integrate AI meaningfully enhancing, rather than displacing, human creativity.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Creativity development in illustration design depends on both exploratory generation and reflective evaluation.
“Illustration design is a studio discipline in which students translate a brief into visual narratives through cycles of ideation, selection, critique, and refinement, so creativity development depends on both exploratory generation and reflective evaluation (Karadağ 2025).”
From A Study on the Role of Midjourney, A Generative Artificial Intelligence Tool, in Enhancing Student Creativity in Illustration Design Classes (2026) · cites this paper as Karadağ 2025
Cites
Clark, A., & Chalmers, D. (1998) · Analysis · doi:10.1093/analys/58.1.7 · Cited by 5,457 · see citing works
Boden, M. A. (2004) · doi:10.4324/9780203508527 · Cited by 2,775 · see citing works
Creativity in the design process: co-evolution of problem–solution
Dorst, K., & Cross, N. (2001) · Design Studies · doi:10.1016/s0142-694x(01)00009-6 · Cited by 2,357 · see citing works
The core of ‘design thinking’ and its application
Dorst, K. (2011) · Design Studies · doi:10.1016/j.destud.2011.07.006 · Cited by 1,897 · see citing works
Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthy
Shneiderman, B. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1741118 · Cited by 1,743 · see citing works
Creativity and artificial intelligence
Boden, M. A. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00055-1 · Cited by 846 · see citing works
Cross, N. (2011) · Berg eBooks · doi:10.5040/9781474293884 · Cited by 831 · see citing works
Sarkar, P., & Chakrabarti, A. (2011) · Design Studies · doi:10.1016/j.destud.2011.01.002 · Cited by 452 · see citing works
Can artificial intelligence support creativity in early design processes?
Chandrasekera, T., Hosseini, Z., & Perera, U. (2025) · International Journal of Architectural Computing · doi:10.1177/14780771241254637 · Cited by 140 · see citing works
Jin, S., Tu, H., Li, J., Fang, Y., Zhang, Q., Xu, F., Liu, K., & Lin, Y. (2024) · Buildings · doi:10.3390/buildings14061613 · Cited by 81 · see citing works
Sadek, M. (2023) · MSA Engineering Journal · doi:10.21608/msaeng.2023.291867 · Cited by 38 · see citing works
A new frontier in design studio: AI and human collaboration in conceptual design
Karadağ, D., & Ozar, B. (2025) · Frontiers of Architectural Research · doi:10.1016/j.foar.2025.01.010 · Cited by 37 · see citing works
Self-citationÖzorhon, G., Gelirli, D. N., Lekesiz, G., & Müezzinoğlu, C. (2025) · International Journal of Technology and Design Education · doi:10.1007/s10798-025-09975-0 · Cited by 37 · see citing works
An Exploration of the Relations between Functionality, Aesthetics and Creativity in Design
Han, J., Forbes, H., & Schaefer, D. (2019) · Proceedings of the ... International Conference on Engineering Design · doi:10.1017/dsi.2019.29 · Cited by 27 · see citing works
Can AI Function As A New Mode of Sketching
Tong, H., Türel, A., Şenkal, H., Ergün, S. F. Y., Güzelci, O. Z., & Alaçam, S. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i18.42603 · Cited by 26 · see citing works
Think AI-side the Box! Exploring the Usability of text-to-image generators for architecture students
Dortheimer, J., Schubert, G., Dalach, A., Brenner, L. J., & Martelaro, N. (2023) · eCAADe proceedings · doi:10.52842/conf.ecaade.2023.2.567 · Cited by 21 · see citing works
Enhancing the use of artificial intelligence in architectural education – case study Saudi Arabia
Alshahrani, A., & Mostafa, A. M. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1610709 · Cited by 20 · see citing works
Xu, C. Q., & Huang, Y. (2024) · Buildings · doi:10.3390/buildings14103288 · Cited by 19 · see citing works
Agkathidis, A., Song, Y., & Symeonidou, I. (2024) · eCAADe proceedings · doi:10.52842/conf.ecaade.2024.2.619 · Cited by 17 · see citing works
Redefining Architectural Pedagogy: Navigating the Integration of Midjourney AI in Design Education
Mansour, N. (2024) · doi:10.35483/acsa.am.112.24 · Cited by 9 · see citing works
Shaping Tomorrow: The Impact of AI on Architectural History and Interior Design Education
Hafiz, D. (2024) · Evolutionary Studies in Imaginative Culture · doi:10.70082/esiculture.vi.1052 · Cited by 5 · see citing works
BULDAÇ, M. (2024) · Sanat ve Tasarım Dergisi · doi:10.20488/sanattasarim.1602366 · Cited by 5 · see citing works
D’souza, N., & Dastmalchi, M. R. (2024) · Springer eBooks · doi:10.1007/978-3-031-71922-6_15 · Cited by 4 · see citing works
Integrating AI in the Creative Process: A Case Study in Interior Design Education
Fares, G. (2025) · Art and Design Review · doi:10.4236/adr.2025.132010 · Cited by 3 · see citing works
YARATICI SENARYO YAZIMINDA YAPAY ZEKÂNIN ROLÜ: İÇ MİMARLIK EĞİTİMİNDE YENİLİKÇİ BİR YAKLAŞIM
İşbilir, A., & Bölükbaşı, M. (2025) · İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.47769/izufbed.1633624 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Yaşar, D., & Küçükali, U. F. (2026) · Buildings · doi:10.3390/buildings16050888 · Cited by 4 · see citing works
More Talk Than Touch: Chinese Art Educators and the Overwhelming Discourse of AI in Higher Education
Tadesse, E., Fu, S., & Diankun, J. (2026) · European Journal of Education · doi:10.1111/ejed.70408 · Cited by 1 · see citing works
Kevseroğlu, Ö., & Kıdık, A. (2026) · Black Sea Journal of Engineering and Science · doi:10.34248/bsengineering.1878241 · Cited by 1 · see citing works
He, S. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70305
Tasarım Stüdyosu Pedagojisinde Yapay Zeka Uygulamalarının Sistematik Analizi
Yıldırım, İ. (2026) · Journal of Computational Design · doi:10.53710/jcode.1837071
Reframing Architectural Pedagogy Within AI Supported Personalized Learning Experiences
Bala, H. A., & Göktepe, F. (2026) · Beykent üniversitesi fen ve mühendislik bilimleri dergisi · doi:10.20854/bujse.1938609
A Mixed-Initiative Procedural Workflow for Fulldome Scene Assembly from Layered 2D Illustrations
Yentür, H. (2026) · PLANARCH - Design and Planning Research · doi:10.54864/planarch.1940932
Şen, E., & Yılmaz, B. (2026) · PLANARCH - Design and Planning Research · doi:10.54864/planarch.1959890
Görsel Düşünmede Metin ve Yapay Zeka: İç Mekan Elemanlarının Temsili Üzerine Bir Atölye Çalışması
Duygun, E., Kanlı, D., & Nane, P. (2026) · GRID - Architecture Planning and Design Journal · doi:10.37246/grid.1836717
Yirmibeş, A., & Çahantimur, A. I. (2026) · Nexus Network Journal · doi:10.1007/s00004-026-00921-8
- Authors
- Karadağ, D.
- Affiliations
- Işık University (TR)
- Corresponding author
- Karadağ, Derya
- ORCID
- Karadağ, Derya
- Venue
- PLANARCH - Design and Planning Research
- Publisher
- Ataturk Universitesi
- Published
- 2025-09-20
- Volume
- 9
- Issue
- 2
- Pages
- 243-253
- ISSN
- 2822-4507 (ISSN-L)
- OpenAlex
- W4414231930
- Cited by
- 10
- References
- 25
- Citations by year
- 2026: 10
- FWCI
- 52.91
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc
- Locations
- PLANARCH - Design and Planning Research (publishedVersion, cc-by-nc)
- DergiPark (Istanbul University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Architecture › Architecture and Computational Design
- Physical Sciences › Engineering › Mechanical Engineering › Design Education and Practice
- Social Sciences › Arts and Humanities › Visual Arts and Performing Arts › Architecture, Art, Education
- Keywords
- architectural design studio, human-AI co-creation, design pedagogy, authorship, prompt literacy, idea generation, cognitive enhancement, conceptual ambiguity, moral reasoning, collaborative creativity, reflexive pedagogy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review