Integrative analysis of Text-to-Image AI systems in architectural design education: pedagogical innovations and creative design implications
Abstract
This study explores the potential of Text-to-Image (T2I) AI systems in architectural design education, particularly during the conceptual design phase. Through a structured two-stage workshop, architecture students used T2I AI to conceptualize a public building project, focusing on the bird’s eye and interior perspectives. These AI-assisted designs were subsequently refined to align with specific site conditions and programmatic requirements. The study reveals T2I AI’s ability to expand creative possibilities in architectural design while highlighting its limitations and biases. The findings emphasize the necessity for a critical and informed approach when integrating AI into architectural education and practice, addressing ethical considerations. Future research directions are proposed to optimize T2I AI applications in architectural design, address inherent biases in AI systems, and enhance the discourse on AI’s role in shaping the future of architectural practices.
Keyword : Text-to-Image AI, architectural design education, creative process in architecture, AI in design education
How to Cite
Montenegro, N. (2024). Integrative analysis of Text-to-Image AI systems in architectural design education: pedagogical innovations and creative design implications. Journal of Architecture and Urbanism, 48(2), 109–124. https://doi.org/10.3846/jau.2024.20870
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Agnese, J., Herrera, J., Tao, H., & Zhu, X. (2020). A survey and taxonomy of adversarial neural networks for text‐to‐image synthesis. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(4), Article 1345. https://doi.org/10.1002/widm.1345
Albukhari, I. N. (2021). Assessment of architectural design studio: A review. American Journal of Civil Engineering and Architecture, 9(3), 88–94.
Al-Saggaf, A., Nasir, H., & Hegazy, T. (2020). An Analytical Hierarchy Process-based system to evaluate the life-cycle performance of buildings at early design stage. Journal of Building Engineering, 31, Article 101364. https://doi.org/10.1016/j.jobe.2020.101364
Ashkan, M. (2016). The phenomenological evaluation of teaching professionalism in the architecture design studio culture: A case at the University of Kansas. ArchNet-IJAR: International Journal of Architectural Research, 10(1), 41–61. https://www.proquest.com/scholarly-journals/phenomenological-evaluation-teaching/docview/1805463495/se-2
Bynum, T. W. (2020). The historical roots of information and computer ethics. In The ethics of information technologies (pp. 43–61). Routledge. https://doi.org/10.4324/9781003075011-4
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 1–21. https://doi.org/10.1007/S10639-020-10159-7
Cheung, F. K. T., Kuen, J. L. F., & Skitmore, M. (2002). Multi-criteria evaluation model for the selection of architectural consultants. Construction Management and Economics, 20(7), 569–580. https://doi.org/10.1080/01446190210159818
Cuff, D. (1991). Architecture: The story of practice. MIT Press.
Dehouche, N., & Dehouche, K. (2023). What is in a Text-to-Image prompt: The potential of stable diffusion in visual arts education. ArXiv. https://doi.org/10.48550/arXiv.2301.01902
Duarte, J. P., Beirao, J. N., Montenegro, N., & Gil, J. (2012). City induction: A model for formulating, generating, and evaluating urban designs. In S. M. Arisona, G. Aschwanden, J. Halatsch, & P. Wonka (Eds.), Communications in computer and information science: Vol. 242. Digital urban modeling and simulation (pp. 73–98). Springer. https://doi.org/10.1007/978-3-642-29758-8
Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI education for K-12 students to enhance their learning of AI literacy through culturally responsive approaches. KI - Künstliche Intelligenz, 35(2), 153–161. https://doi.org/10.1007/s13218-021-00737-3
Enjellina, Beyan, E. V. P., & Rossy, A. G. C. (2023). Review of AI image generator: Influences, challenges, and future prospects for architectural field. Journal of Artificial Intelligence in Architecture, 2(1), 53–65. https://doi.org/10.24002/jarina.v2i1.6662
Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. (2021). Adversarial text-to-image synthesis: A review. Neural Networks, 144, 187–209. https://doi.org/10.1016/j.neunet.2021.07.019
Hajirasouli, A., Banihashemi, S., Sanders, P., & Rahimian, F. (2023). BIM-enabled virtual reality (VR)-based pedagogical framework in architectural design studios. Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-07-2022-0149
Harputlugil, T. (2018). Analytic Hierarchy Process (AHP) as an assessment approach for architectural design: Case study of architectural design studio. ICONARP International Journal of Architecture and Planning, 6(2), 217–245. https://doi.org/10.15320/ICONARP.2018.53
Huang, Y. C., Wang, S. Y., Liong, S. T., Huang, C. E., Hsieh, Y. C., Wang, H. Y., Lin, W. H., & Gan, Y. S. (2020). Who is the designer? ARC-100 database and benchmark on architecture classification. International Journal of Computational Intelligence Systems, 13(1), 1305–1314. https://doi.org/10.2991/ijcis.d.200824.001
Ismail, A. F. M. F., Sam, M. F. M., Bakar, K. A., Ahamat, A., Adam, S., & Qureshi, M. I. (2022). Artificial intelligence in healthcare business ecosystem. International Journal of Online and Biomedical Engineering (IJOE), 18(9), 100–114. https://doi.org/10.3991/ijoe.v18i09.32251
Kathuria, N. (2017). Role of internet in contemporary architectural pedagogy. International Journal of Advance Research, Ideas and Innovations in Technology, 3(6), 550–557. https://www.ijariit.com/manuscript/role-of-internet-in-contemporary-architectural-pedagogy/
Li, J., & Chen, S. (2009). Evaluating the architectural design services by using fuzzy AHP. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (pp. 287–291), Tianjin, China. https://doi.org/10.1109/FSKD.2009.360
Li, S. (2022). Potential of using computer vision to predict graphics for learning-by-doing. In 2022 IEEE 5th International Conference on Knowledge Innovation and Invention (pp. 169–172), Hualien, Taiwan. https://doi.org/10.1109/ICKII55100.2022.9983585
Liang, J., Pei, W., & Lu, F. (2020). CPGAN: Content-Parsing Generative Adversarial Networks for text-to-image synthesis. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Lecture notes in computer science: Vol. 12349. Computer Vision – ECCV 2020 (pp. 491–508). Springer. https://doi.org/10.1007/978-3-030-58548-8_29
Lim, J. Y., & Baboo, S. B. (2022). Perception of creative arts practitioners on the use of digital technologies for creative projects among creative arts students in institutions of higher learning. Journal of Language and Communication, 9(1), 102–115. https://doi.org/10.47836/jlc.9.1.07
Lin, R. Y., & Alvarez, J. B. (2020). Industry perspectives and commercial opportunities of artificial intelligence in medicine. In Artificial intelligence in medicine: Technical basis and clinical applications (pp. 479–502). Elsevier. https://doi.org/10.1016/B978-0-12-821259-2.00024-7
Liu, V., Vermeulen, J., Fitzmaurice, G., & Matejka, J. (2022). 3DALL-E: Integrating Text-to-Image AI in 3D design workflows. ArXiv. https://doi.org/10.48550/ArXiv.2210.11603
McManus, M. (2018). An in-depth look at design students as they embark on teaching architecture to children. ARCC Conference Repository. https://arcc-journal.org/index.php/repository/article/view/545
Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design Studies, 52, 4–39. https://doi.org/10.1016/j.destud.2017.06.001
Ploennigs, J., & Berger, M. (2022). AI art in architecture. ArXiv. https://doi.org/10.48550/arXiv.2212.09399
Purcell, A. T., & Gero, J. S. (1998). Drawings and the design process: A review of protocol studies in design and other disciplines and related research in cognitive psychology. Design Studies, 19(4), 389–430. https://doi.org/10.1016/S0142-694X(98)00015-5
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2022). DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. ArXiv. https://doi.org/10.48550/arXiv.2208.12242
Salama, A. M. (1995). New trends in architectural education: Designing the design studio (1st ed.). Tailored Text and Unlimited Potential Publishers. https://researchportal.northumbria.ac.uk/en/publications/new-trends-in-architectural-education-designing-the-design-studio
Schön, D. A. (2017). The reflective practitioner: How professionals think in action. Routledge. https://doi.org/10.4324/9781315237473
Shalem, Y., & De Clercq, F. (2022). Randomized control trials in education (RCTs): What is their contribution to education theory about teaching? Journal of Education, 89, 3–22. https://doi.org/10.17159/2520-9868/i89a01
Tepavčević, B. (2017). Design thinking models for architectural education. The Journal of Public Space, 2(3), 67–72. https://doi.org/10.5204/jps.v2i3.115
van Berkel, B. (2020). Architecture and the impact of the fourth industrial revolution. Architectural Design, 90(5), 126–133. https://doi.org/10.1002/ad.2619
Vermisso, E. (2022). Fragmented layers of design thinking: Limitations and opportunities of neural language model-assisted processes for design creativity. Design Computation Input/Output. https://doi.org/10.47330/DCIO.2022.MMLW2640
Vimpari, V., Kultima, A., Hämäläinen, P., & Guckelsberger, C. (2023). “An adapt-or-die type of situation”: Perception, adoption, and use of text-to-image-generation AI by game industry professionals. ArXiv. https://doi.org/10.48550/arXiv.2302.12601
Wei, A. (2020). The application of “Artificial Intelligence +” in the construction of architectural majors in higher vocational colleges. In 2020 International Conference on Educational Science (pp. 579–584). https://doi.org/10.38007/Proceedings.0000343
Wei, L. (2019). AI-Design: Architectural intelligent design approaches based on AI. DEStech Transactions on Engineering and Technology Research. https://doi.org/10.12783/dtetr/icaen201/28985
Yadav, A. (2021). How artificial intelligence is transforming the banking industry: Data analysis review. International Journal of Advanced Research in Science, Communication and Technology, 7(2), 76–81. https://doi.org/10.48175/IJARSCT-1707
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, H., Bai, X., & Ma, Z. (2022). Consumer reactions to AI design: Exploring consumer willingness to pay for AI‐designed products. Psychology & Marketing, 39(11), 2171–2183. https://doi.org/10.1002/mar.21721
Albukhari, I. N. (2021). Assessment of architectural design studio: A review. American Journal of Civil Engineering and Architecture, 9(3), 88–94.
Al-Saggaf, A., Nasir, H., & Hegazy, T. (2020). An Analytical Hierarchy Process-based system to evaluate the life-cycle performance of buildings at early design stage. Journal of Building Engineering, 31, Article 101364. https://doi.org/10.1016/j.jobe.2020.101364
Ashkan, M. (2016). The phenomenological evaluation of teaching professionalism in the architecture design studio culture: A case at the University of Kansas. ArchNet-IJAR: International Journal of Architectural Research, 10(1), 41–61. https://www.proquest.com/scholarly-journals/phenomenological-evaluation-teaching/docview/1805463495/se-2
Bynum, T. W. (2020). The historical roots of information and computer ethics. In The ethics of information technologies (pp. 43–61). Routledge. https://doi.org/10.4324/9781003075011-4
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 1–21. https://doi.org/10.1007/S10639-020-10159-7
Cheung, F. K. T., Kuen, J. L. F., & Skitmore, M. (2002). Multi-criteria evaluation model for the selection of architectural consultants. Construction Management and Economics, 20(7), 569–580. https://doi.org/10.1080/01446190210159818
Cuff, D. (1991). Architecture: The story of practice. MIT Press.
Dehouche, N., & Dehouche, K. (2023). What is in a Text-to-Image prompt: The potential of stable diffusion in visual arts education. ArXiv. https://doi.org/10.48550/arXiv.2301.01902
Duarte, J. P., Beirao, J. N., Montenegro, N., & Gil, J. (2012). City induction: A model for formulating, generating, and evaluating urban designs. In S. M. Arisona, G. Aschwanden, J. Halatsch, & P. Wonka (Eds.), Communications in computer and information science: Vol. 242. Digital urban modeling and simulation (pp. 73–98). Springer. https://doi.org/10.1007/978-3-642-29758-8
Eguchi, A., Okada, H., & Muto, Y. (2021). Contextualizing AI education for K-12 students to enhance their learning of AI literacy through culturally responsive approaches. KI - Künstliche Intelligenz, 35(2), 153–161. https://doi.org/10.1007/s13218-021-00737-3
Enjellina, Beyan, E. V. P., & Rossy, A. G. C. (2023). Review of AI image generator: Influences, challenges, and future prospects for architectural field. Journal of Artificial Intelligence in Architecture, 2(1), 53–65. https://doi.org/10.24002/jarina.v2i1.6662
Frolov, S., Hinz, T., Raue, F., Hees, J., & Dengel, A. (2021). Adversarial text-to-image synthesis: A review. Neural Networks, 144, 187–209. https://doi.org/10.1016/j.neunet.2021.07.019
Hajirasouli, A., Banihashemi, S., Sanders, P., & Rahimian, F. (2023). BIM-enabled virtual reality (VR)-based pedagogical framework in architectural design studios. Smart and Sustainable Built Environment. https://doi.org/10.1108/SASBE-07-2022-0149
Harputlugil, T. (2018). Analytic Hierarchy Process (AHP) as an assessment approach for architectural design: Case study of architectural design studio. ICONARP International Journal of Architecture and Planning, 6(2), 217–245. https://doi.org/10.15320/ICONARP.2018.53
Huang, Y. C., Wang, S. Y., Liong, S. T., Huang, C. E., Hsieh, Y. C., Wang, H. Y., Lin, W. H., & Gan, Y. S. (2020). Who is the designer? ARC-100 database and benchmark on architecture classification. International Journal of Computational Intelligence Systems, 13(1), 1305–1314. https://doi.org/10.2991/ijcis.d.200824.001
Ismail, A. F. M. F., Sam, M. F. M., Bakar, K. A., Ahamat, A., Adam, S., & Qureshi, M. I. (2022). Artificial intelligence in healthcare business ecosystem. International Journal of Online and Biomedical Engineering (IJOE), 18(9), 100–114. https://doi.org/10.3991/ijoe.v18i09.32251
Kathuria, N. (2017). Role of internet in contemporary architectural pedagogy. International Journal of Advance Research, Ideas and Innovations in Technology, 3(6), 550–557. https://www.ijariit.com/manuscript/role-of-internet-in-contemporary-architectural-pedagogy/
Li, J., & Chen, S. (2009). Evaluating the architectural design services by using fuzzy AHP. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (pp. 287–291), Tianjin, China. https://doi.org/10.1109/FSKD.2009.360
Li, S. (2022). Potential of using computer vision to predict graphics for learning-by-doing. In 2022 IEEE 5th International Conference on Knowledge Innovation and Invention (pp. 169–172), Hualien, Taiwan. https://doi.org/10.1109/ICKII55100.2022.9983585
Liang, J., Pei, W., & Lu, F. (2020). CPGAN: Content-Parsing Generative Adversarial Networks for text-to-image synthesis. In A. Vedaldi, H. Bischof, T. Brox, & J. M. Frahm (Eds.), Lecture notes in computer science: Vol. 12349. Computer Vision – ECCV 2020 (pp. 491–508). Springer. https://doi.org/10.1007/978-3-030-58548-8_29
Lim, J. Y., & Baboo, S. B. (2022). Perception of creative arts practitioners on the use of digital technologies for creative projects among creative arts students in institutions of higher learning. Journal of Language and Communication, 9(1), 102–115. https://doi.org/10.47836/jlc.9.1.07
Lin, R. Y., & Alvarez, J. B. (2020). Industry perspectives and commercial opportunities of artificial intelligence in medicine. In Artificial intelligence in medicine: Technical basis and clinical applications (pp. 479–502). Elsevier. https://doi.org/10.1016/B978-0-12-821259-2.00024-7
Liu, V., Vermeulen, J., Fitzmaurice, G., & Matejka, J. (2022). 3DALL-E: Integrating Text-to-Image AI in 3D design workflows. ArXiv. https://doi.org/10.48550/ArXiv.2210.11603
McManus, M. (2018). An in-depth look at design students as they embark on teaching architecture to children. ARCC Conference Repository. https://arcc-journal.org/index.php/repository/article/view/545
Oxman, R. (2017). Thinking difference: Theories and models of parametric design thinking. Design Studies, 52, 4–39. https://doi.org/10.1016/j.destud.2017.06.001
Ploennigs, J., & Berger, M. (2022). AI art in architecture. ArXiv. https://doi.org/10.48550/arXiv.2212.09399
Purcell, A. T., & Gero, J. S. (1998). Drawings and the design process: A review of protocol studies in design and other disciplines and related research in cognitive psychology. Design Studies, 19(4), 389–430. https://doi.org/10.1016/S0142-694X(98)00015-5
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2022). DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation. ArXiv. https://doi.org/10.48550/arXiv.2208.12242
Salama, A. M. (1995). New trends in architectural education: Designing the design studio (1st ed.). Tailored Text and Unlimited Potential Publishers. https://researchportal.northumbria.ac.uk/en/publications/new-trends-in-architectural-education-designing-the-design-studio
Schön, D. A. (2017). The reflective practitioner: How professionals think in action. Routledge. https://doi.org/10.4324/9781315237473
Shalem, Y., & De Clercq, F. (2022). Randomized control trials in education (RCTs): What is their contribution to education theory about teaching? Journal of Education, 89, 3–22. https://doi.org/10.17159/2520-9868/i89a01
Tepavčević, B. (2017). Design thinking models for architectural education. The Journal of Public Space, 2(3), 67–72. https://doi.org/10.5204/jps.v2i3.115
van Berkel, B. (2020). Architecture and the impact of the fourth industrial revolution. Architectural Design, 90(5), 126–133. https://doi.org/10.1002/ad.2619
Vermisso, E. (2022). Fragmented layers of design thinking: Limitations and opportunities of neural language model-assisted processes for design creativity. Design Computation Input/Output. https://doi.org/10.47330/DCIO.2022.MMLW2640
Vimpari, V., Kultima, A., Hämäläinen, P., & Guckelsberger, C. (2023). “An adapt-or-die type of situation”: Perception, adoption, and use of text-to-image-generation AI by game industry professionals. ArXiv. https://doi.org/10.48550/arXiv.2302.12601
Wei, A. (2020). The application of “Artificial Intelligence +” in the construction of architectural majors in higher vocational colleges. In 2020 International Conference on Educational Science (pp. 579–584). https://doi.org/10.38007/Proceedings.0000343
Wei, L. (2019). AI-Design: Architectural intelligent design approaches based on AI. DEStech Transactions on Engineering and Technology Research. https://doi.org/10.12783/dtetr/icaen201/28985
Yadav, A. (2021). How artificial intelligence is transforming the banking industry: Data analysis review. International Journal of Advanced Research in Science, Communication and Technology, 7(2), 76–81. https://doi.org/10.48175/IJARSCT-1707
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0
Zhang, H., Bai, X., & Ma, Z. (2022). Consumer reactions to AI design: Exploring consumer willingness to pay for AI‐designed products. Psychology & Marketing, 39(11), 2171–2183. https://doi.org/10.1002/mar.21721