The transformative integration of artificial intelligence in architectural practice: from generative design to sustainable building performance
Keywords:
machine learning, generative algorithms, architectural innovation, sustainable design, building optimizationAbstract
The integration of artificial intelligence (AI) in architecture has emerged as a transformative force, reshaping design methodologies and construction technologies. The evolving fields of machine learning, generative design, and evolutionary algorithms have facilitated the exploration of new forms and structures, enhancing creativity, sustainability, and efficiency in architectural practices. This review aims to evaluate the impact of AI on architectural design, focusing on the advancements in machine learning, generative algorithms, and their application in sustainable and efficient design solutions. The study identifies the potential and limitations of these AI-driven technologies in reshaping architectural practice. A comprehensive literature review was conducted using major scientific databases such as Scopus, Web of Science, and IEEE Xplore. Studies from the last 5 years (2020-2025) were selected, focusing on AI's role in architectural design, from conceptualization to construction, with an emphasis on performance optimization, sustainability, and innovation. The review reveals that AI-driven tools like machine learning and generative design significantly enhance architectural creativity by enabling real-time, adaptive design processes. These technologies facilitate energy-efficient and sustainable building designs, optimize space usage, and improve structural durability. Moreover, AI is playing a key role in automating repetitive tasks, thus allowing architects to focus on more creative aspects of design. AI is revolutionizing architectural practices by enhancing design creativity, efficiency, and sustainability. Future research should focus on refining AI tools, addressing limitations such as data scarcity, and ensuring the ethical implementation of AI in architectural design to foster responsible innovation
https://doi.org/10.59214/euch/1.2025.66
References
Asif, M., Naeem, G., & Khalid, M. (2024). Digitalization for sustainable buildings: Technologies, applications, potential, and challenges. Journal of Cleaner Production, 450, 141814. https://doi.org/10.1016/j.jclepro.2024.141814
Baudoux, G. (2024). The benefits and challenges of artificial intelligence image generators for architectural ideation: Study of an alternative human-machine co-creation exchange based on sketch recognition. International Journal of Architectural Computing, 22(2), 201–215. https://doi.org/10.1177/14780771241253438
Behzadi, M., & Ilies, H. T. (2021). Real-time topology optimization in 3D via deep transfer learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2102.07657
Chaillou, S. (2020). ArchiGAN: Artificial intelligence x architecture. In P. F. Yuan, M. Xie, N. Leach, J. Yao, & X. Wang (Eds.), Architectural intelligence (pp. 117–132). Springer. https://doi.org/10.1007/978-981-15-6568-7_8
Chellakkannu, S. (2024). Embracing generative AI in design: Practical implementations. In Lecture notes in networks and systems (pp. 189–206). Springer. https://doi.org/10.1007/978-3-031-71429-0_15
Chen, J., Wang, D., Shao, Z., Zhang, X., Ruan, M., Li, H., & Li, J. (2023). Using artificial intelligence to generate master-quality architectural designs from text descriptions. Buildings, 13(9), 2285. https://doi.org/10.3390/buildings13092285
Chen, X., Gao, W., Chu, Y., & Song, Y. (2024). Enhancing interaction in virtual-real architectural environments: A comparative analysis of generative AI-driven reality approaches. Building and Environment, 266, 112113. https://doi.org/10.1016/j.buildenv.2024.112113
Del Campo, M., & Leach, N. (2022). Can machines hallucinate architecture? AI as design method. Architectural Design, 92(3), 6–13. https://doi.org/10.1002/ad.2807
Dorissa, P. (2024). Democratizing access to architecture: Between automation and creativity. Argument, 16, 58–75. https://doi.org/10.54508/argument.16.04
El Moussaoui, M. (2025). Architectural practice process and artificial intelligence – An evolving practice. Open Engineering, 15(1), 20240098. https://doi.org/10.1515/eng-2024-0098
Ghimire, P., Kim, K., & Acharya, M. (2024). Opportunities and challenges of generative AI in construction industry: Focusing on adoption of text-based models. Buildings, 14(1), 220. https://doi.org/10.3390/buildings14010220
Hanafy, N. O. (2023). Artificial intelligence’s effects on design process creativity: A study on used A.I. text-to-image in architecture. Journal of Building Engineering, 80, 107999. https://doi.org/10.1016/j.jobe.2023.107999
Heo, S., & Seunguk, N. (2025). Ready for departure: Factors to adopt Large Language Model (LLM)-based artificial intelligence (AI) technology in the architecture, engineering and construction (AEC) industry. Results in Engineering, 104325. https://doi.org/10.1016/j.rineng.2025.104325
Heyn, H., Knauss, E., & Pelliccione, P. (2023). A compositional approach to creating architecture frameworks with an application to distributed AI systems. Journal of Systems and Software, 198, 111604. https://doi.org/10.1016/j.jss.2022.111604
Horvath, A., & Pouliou, P. (2024). AI for conceptual architecture: Reflections on designing with text-to-text, text-to-image, and image-to-image generators. Frontiers of Architectural Research, 13(3), 593–612. https://doi.org/10.1016/j.foar.2024.02.006
Huynh, D., & Nguyen-Ky, S. (2020). Engaging building automation data visualization using building information modelling and progressive web application. Open Engineering, 10(1), 434–442. https://doi.org/10.1515/eng-2020-0054
Hwang, Y.-M., & Lee, M.-S. (2025). Examining automation rate in basic architectural design tasks with artificial intelligence integration. Journal of the Architectural Institute of Korea, 41(2), 95–104. https://doi.org/10.5659/JAIK.2025.41.2.95
Jo, H., Lee, J., Lee, Y., & Choo, S. (2024). Generative artificial intelligence and building design: Early photorealistic render visualization of façades using local identity-trained models. Journal of Computational Design and Engineering, 11(2), 85–105. https://doi.org/10.1093/jcde/qwae017
Lam, B., Ong, Z., Ooi, K., Ong, W., Wong, T., Watcharasupat, K. N., Boey, V., Lee, I., Hong, J. Y., Kang, J., Lee, K. F. A., Christopoulos, G., & Gan, W. (2024). Automating urban soundscape enhancements with AI: In-situ assessment of quality and restorativeness in traffic-exposed residential areas. Building and Environment, 266, 112106. https://doi.org/10.1016/j.buildenv.2024.112106
Li, C., Zhang, T., Du, X., Zhang, Y., & Xie, H. (2024a). Generative AI models for different steps in architectural design: A literature review. Frontiers of Architectural Research, 14(3), 759–783. https://doi.org/10.1016/j.foar.2024.10.001
Li, Y. (2024). “Will artificial intelligence platforms replace designers in the future?” Analyzing the impact of artificial intelligence platforms on the engineering design industry through color perception. Engineering Applications of Artificial Intelligence, 138, 109369. https://doi.org/10.1016/j.engappai.2024.109369
Li, Y., Chen, H., Yu, P., & Yang, L. (2025). A review of artificial intelligence in enhancing architectural design efficiency. Applied Sciences, 15(3), 1476. https://doi.org/10.3390/app15031476
Li, Z., Li, S., Hinchcliffe, G., Maitless, N., & Birbilis, N. (2024b). Automated architectural space layout planning using a physics-inspired generative design framework. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2406.14840
Matter, N., & Gado, N. (2024). Artificial intelligence in architecture: Integration into architectural design process. Engineering Research Journal, 181, 1–16. https://doi.org/10.21608/erj.2024.344313
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, 11, 100319. https://doi.org/10.1016/j.chbr.2023.100319
Sourek, M. (2024). AI in architecture and engineering from misconceptions to game-changing prospects. Architectural Intelligence, 3(1). https://doi.org/10.1007/s44223-023-00046-9
Tanasra, H., Shaham, T. R., Michaeli, T., Austern, G., & Barath, S. (2023). Automation in interior space planning: Utilizing conditional generative adversarial network models to create furniture layouts. Buildings, 13(7), 1793. https://doi.org/10.3390/buildings13071793
Zhang, Y., Liu, H., Kang, S., & Al-Hussein, M. (2020). Virtual reality applications for the built environment: Research trends and opportunities. Automation in Construction, 118, 103311. https://doi.org/10.1016/j.autcon.2020.103311
Zheng, H., & Yuan, P. F. (2021). A generative architectural and urban design method through artificial neural networks. Building and Environment, 205, 108178. https://doi.org/10.1016/j.buildenv.2021.108178
Zhong, W., Schröder, T., & Bekkering, J. (2021). Biophilic design in architecture and its contributions to health, well-being, and sustainability: A critical review. Frontiers of Architectural Research, 11(1), 114–141. https://doi.org/10.1016/j.foar.2021.07.006
Luchenko, O., Doronina, O., & Chervinko, Y. (2024). The use of English medium instruction in multilingual classrooms in Japanese language teaching. Advanced Education, 58–74.
Luchenko, O., Doronina, O., & Chervinko, Y. (2024). Possible factors influencing the willingness to use English in teaching Japanese as a foreign language by non-native speakers. Sustainable Multilingualism/Darnioji daugiakalbystė, 24, 45–78.
Ковінько, К. В., Лученко, О. О., & Єршова, І. Ш. (2025). Важливість впровадження курсу ESP “Military English” для військовослужбовців з урахуванням рівня володіння англійською мовою. Педагогічна Академія: наукові записки, (15).
Luchenko, O. (2021). Application of the positive Japanese experience in the professional adaptation of novice teachers for developing countries. In Scientific Collection «InterConf», 41, with the Proceedings of the 7th International Scientific and Practical Conference «Scientific Horizon in the Context of Social Crises» (pp. 6–8).
