Tasarım Stüdyosu Erken Aşamalarında Üretken Yapay Zeka ile İşbirliği: Bir Model Önerisi

Year 2025, Volume: 10 Issue: 1, 434 - 450, 28.07.2025

Tayibe Seyman Güray , Betül Uyan

https://doi.org/10.30785/mbud.1649820

Abstract

Bu çalışma, tasarım stüdyosu eğitiminde öğrencilerin fikir üretimi, senaryo oluşturma ve kavramsal geliştirme gibi becerilerle başa çıktığı erken aşamalarda Üretken Yapay Zekâ (GAI) entegrasyonunu araştırmaktadır. GAI destekli insan-merkezli bir iş birliği modeli, metinden-metine (ChatGPT) ve metinden-görüntüye (Bing Image Creator) platformlarıyla geliştirilmiş ve ikinci sınıf iç mimarlık stüdyosunda 15 öğrenciyle uygulanmıştır. Model, öğrencilerin kullanıcı senaryolarını ifade etmelerini, mekânsal atmosferleri görselleştirmelerini ve ilk tasarım kavramlarını analog kolajlar aracılığıyla şekillendirmelerini desteklemeyi amaçlamıştır. GAI deneyimi olan iç mimar jüri üyeleri, öğrencilerin çıktıları üzerinde önceden tanımlanmış değerlendirme ölçütlerine göre uzman değerlendirmesi gerçekleştirmiştir. Nicel analizler, GAI destekli süreçlerin öğrencilerin kavramsal netliğini, temsil becerilerini ve stüdyo verimliliğini artırdığını göstermiştir. Öğrenci çalışmalarına ait görsel belgeler de bu bulguları desteklemiştir. Bu araştırma, AI destekli stüdyo eğitimine dair tekrar edilebilir ve insan merkezli bir entegrasyon modeli önererek, yapay zekâ temelli tasarım pedagojisi literatürüne anlamlı bir katkı sunmaktadır.

Keywords

Üretken yapay zeka , konsept tasarım , tasarım stüdyosu eğitimi , metinden metine , metinden görsele

Collaboration with Generative Artificial Intelligence in the Early Stages of Design Studio: A Model Proposal

Abstract

This study investigates the integration of Generative Artificial Intelligence (GAI) into early-stage design studio education, where students navigate ill-defined problems requiring ideation, scenario-building, and conceptual development. A structured collaboration model was developed using text-to-text (ChatGPT) and text-to-image (Bing Image Creator) platforms and implemented in a second-year interior design studio with 15 students. The model guided students in creating user-based scenarios, generating atmospheric visualizations, and framing initial design concepts through analog collages. To assess the impact, expert jury members—practicing interior architects with GAI experience—evaluated student outputs based on predefined criteria. Quantitative analysis revealed that GAI-supported workflows improved conceptual clarity, representational skills, and productivity. Visual documentation of student work further illustrated these outcomes. This research contributes to the growing discourse on AI-enhanced pedagogy by proposing a replicable, human-centered GAI integration model. It offers practical insights into how generative tools can support creativity, foster reflective thinking, and enhance communication in early design education.

Keywords

Generative artificial intelligence , conceptual design , design studio education , text to text , text to image

References

• AboWardah, E. S. (2020). Bridging the gap between research and schematic design phases in teaching architectural graduation projects. Frontiers of Architectural Research, 9(1), 82-105. Abrishami, S., Goulding, J. & Rahimian, F. (2021). Generative BIM workspace for AEC conceptual design automation: prototype development. Engineering, Construction and Architectural Management, 28(2), 482-509.
• Adeshola, I. & Adepoju, A. P. (2023). The opportunities and challenges of ChatGPT in education. Interactive Learning Environments, 1-14.
• Akin, O. (2008). Creativity in Design. Performance Improvement Quarterly, 7(3), 9–21. doi:10.1111/j.1937- 8327.1994.tb00633.x
• Albaghajati, Z. M., Bettaieb, D. M., & Malek, R. B. (2023). Exploring text-to-image application in architectural design: Insights and implications. Architecture, Structures and Construction, 3(4), 475-497.
• As, I., Pal, S. & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4), 306-327.
• Bandi, A., Adapa, P.V.S.R. & Kuchi, Y.E.V. P. K. (2023). The Power of Generative AI: A Review of Requirements, Models, Input–Output Formats, Evaluation Metrics, and Challenges. In Future Internet (Vol. 15, Issue 8). doi: https://doi.org/ 10.3390/fi15080260
• Brown, T. (2009). Change by Design. HarperCollins e-books.
• Bueno, E. & Turkienicz, B. (2014). Supporting tools for early stages of architectural design. International Journal of Architectural Computing, 12(4), 495-512.
• BuHamdan, S., Alwisy, A. & Bouferguene, A. (2021). Generative systems in the architecture, engineering and construction industry: A systematic review and analysis. International Journal of Architectural Computing, 19(3), 226-249.
• Carrol, J. M. (1999, January). Five reasons for scenario-based design. In Proceedings of the 32nd annual hawaii international conference on systems sciences. 1999. hicss-32. abstracts and cd-rom of full papers (pp. 11-pp). IEEE.
• Casakin, H., van Timmeren, A. & Badke-Schaub, P. (2016). Approaches in design education: The role of patterns and scenarios in the design studio. Problems of Education in the 21st Century, 69, 6.
• Cheung, L. H. & Dall’Asta, J. C. (2024). Human-computer interaction (HCI) approach to artificial ıntelligence in education (AIEd) in Architectural Design. Eídos, 17(23), 109-131.
• Cho, J. Y. (2017). An investigation of design studio performance in relation to creativity, spatial ability, and visual cognitive style. Thinking Skills and Creativity, 23, 67-78.
• Choi, H. H. & Kim, M. J. (2018). Using the digital context to overcome design fixation: a strategy to expand students' design thinking. Archnet-IJAR, 12(1), 228-240.
• Cubuk, G. (2023). Spatial ıntegrity through sequences: Contemporary scenario planning techniques for architectural design. Kocaeli Üniversitesi Mimarlık ve Yaşam Dergisi, 8(2), 239–255. doi:10.26835/my.1250738
• Dai, S., Li, Y., Grace, K. & Globa, A. (2023, July). Towards Human-AI Collaborative Architectural Concept Design via Semantic AI. In International Conference on Computer-Aided Architectural Design Futures (pp. 68-82). Cham: Springer Nature Switzerland.
• Eilouti, B. (2018). Scenario-based design: New applications in metamorphic architecture. Frontiers of Architectural Research, 7(4), 530-543.
• Enjellina, Beyan, E.V.P. and Anastasya Gisela Cinintya Rossy. (2023). Review of aı ımage generator: Influences, challenges, and future prospects for architectural field. Journal of Artificial Intelligence in Architecture, 2(1). doi: https://doi.org/10.24002/jarina.v2i1.6662
• EPC. (2013). Emerging Professionals Companion, Programming 1A. Access Address (02.01.2025) https://content.aia.org/sites/default/files/2017-03/EPC_Programming_1A.pdf
• Ergül, D. B., Özgünler, S. A. & Arpacıoğlu, Ü. (2024). A proposal for a method to calculate the adaptive reuse potentials of structures by using artificial neural networks. A| Z ITU Journal of The Faculty of Archıtecture, 21(2), 291-304.
• Gero, J. S., & Mc Neill, T. (1998). An approach to the analysis of design protocols. Design studies, 19(1), 21-61.
• Goel, V. (1992). Ill-Structured Representations “for ill-Structured Problems” Presented at the Fourteenth Annual Conference of the Cognitive Science Society.
• Goldschmidt, G. (2004). Design Representation: Private Process, Public Image. In G. Goldschmidt & W. L. Porter (Eds.), Design Representation (pp. 203–217). Springer London.
• Goldschmidt, G. (2014). Design Creativity. In Linkography. doi:10.7551/mitpress/9455.003.0008
• Goldschmidt, G. (2019). Disciplinary Knowledge and the Design Space. Edited by Naz Börekçi, Dalsu Koçyıldırım, Fatma Korkut, and Derek Jones. METU Department of Industrial Design.
• Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023). ChatGPT is not all you need. A State of the Art Review of large Generative AI models. arXiv preprint arXiv:2301.04655.
• He, Z., Li, X., Fan, L. & Wang, H. J. (2023, July). Revamping Interior Design Workflow Through Generative Artificial Intelligence. In International Conference on Human-Computer Interaction (pp. 607-613). Cham: Springer Nature Switzerland
• Hettithanthri, U., Hansen, P., & Munasinghe, H. (2023). Exploring the architectural design process assisted in conventional design studio: a systematic literature review. International Journal of Technology and Design Education, 33(5), 1835-1859.
• Idi, D. B. & Khaidzir, K. A. B. M. (2015). Concept of creativity and innovation in architectural design process. International Journal of Innovation, Management and Technology, 6(1), 16.
• Islam, Z. (2019). Constructivist digital design studio with extended reality for effective design pedagogy. Design and Technology Education: an International Journal, 24(3), 52-76.
• Kawabata, R., Kasahara, T., & Itoh, K. (2007). Systems analysis for collaborative system by use case diagram. Journal of Integrated Design and Process Science, 11(1), 13-27.
• Leon, M. & Laing, R. (2013, December). Cloud and computer mediated collaboration in the early architectural design stages: A study of early design stage collaboration related to BIM and the cloud. In 2013 IEEE 5th International Conference on Cloud Computing Technology and Science (Vol. 2, pp. 94-99). IEEE.
• Liao, W., Lu, X., Fei, Y., Gu, Y. & Huang, Y. (2024). Generative AI Design for Building Structures. Automation in Construction, 157, 105187.
• Mankins, P. D. (2014). Design Phases. In R. L. Hayes (Ed.), The Architect’s Handbook of Professional Practice (15th ed., pp. 654–667). Hoboken: John Wiley & Sons, Inc.
• Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., ... & Siemens, G. (2022). Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?. Computers and Education: Artificial Intelligence, 3, 100056.
• Mazhari, M. E., Davoodeh, H. & Vasiq, B. (2022). The effect of teaching through the scenario planning method on creativity in architectural student’s design. Journal of Positive School Psychology, 6(7), 3405-3418.
• Nazidizaji, S., Tome, A., Regateiro, F. & Ghalati, A. K. (2015). Narrative ways of architecture education: A case study. Procedia-Social and Behavioral Sciences, 197, 1640-1646.
• Park, E. J. & Lee, S. (2022). Creative thinking in the architecture design studio: Bibliometric analysis and literature review. Buildings, 12(6), 828.
• Pena, M. L. C., Carballal, A., Rodríguez-Fernández, N., Santos, I. & Romero, J. (2021). Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 124, 103550.
• Prawata, A. G. (2017, November). Rethinking the architectural design concept in the digital culture (in architecture’s practice perspective). In AIP Conference Proceedings (Vol. 1903, No. 1). AIP Publishing.
• Rane, N., Choudhary, S. & Rane, J. (2023). Integrating ChatGPT, Bard, and leading-edge generative artificial intelligence in architectural design and engineering: applications, framework, and challenges. SSRN Electronic Journal. doi:10.2139/ssrn.4645595
• Raza, S., Venaik, A. & Khalil, S. N. (2023). Unveiling the ımpact of AI and ChatGPT on architectural and ınterior design studies: A comprehensive exploration. Tuijin Jishu/Journal of Propulsion Technology, 44(3), 580-591.
• Rafsanjani, H. N. & Nabizadeh, A. H. (2023). Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry. Computers in Human Behavior Reports, 100319.
• Sainani, E. S. (2023). Enhancing Interior Design With AI-enhanced Prompts and Stable Diffusion.
• Schubert, G., Artinger, E., Petzold, F. & Klinker, G. (2011). A (Collaborative) Design Platform for early design stages. eCAADe 2011, 187.
• Somer, P. M. (2015). Metinden görsele mimaride ekfrasis.
• Tvedebrink, T. D. O. & Jelic, A. (2018). Getting under the (ir) skin: Applying personas and scenarios with body- environment research for improved understanding of users' perspective in architectural design. Persona Studies, 4(2), 5-24.
• Uzun, T., & Çakır, H. S. (2022). BIM as a learning tool in design studio. International Journal of Digital Innovation in the Built Environment (IJDIBE), 11(1), 1-14.
• Zhang, Z., Fort, J. M. & Mateu, L. G. (2023). Exploring the potential of artificial intelligence as a tool for architectural design: A perception study using gaudí’sworks. Buildings, 13(7), 1863.