Embracing Artificial Intelligence (AI) in Architectural Education: A Step towards Sustainable Practice?
Abstract
:1. Introduction
2. Theoretical Background
3. Materials and Methods
3.1. Research Questions and Hypothesis
3.2. Research and Questionnaire Design
4. Results
5. Discussion
6. Conclusions
- (1)
- while the hypothesis that AI usage positively influences sustainability ratings was not confirmed, the findings open up new avenues for research and educational focus; understanding the broader context of how AI integrates with traditional architectural practices remains a key area for future exploration.
- (2)
- The perceived benefits of using AI in architecture influence the acceptance of AI tools by architecture students. These findings have significant implications for educational strategies and the implementation of AI tools in architecture programs. Educators and policymakers should emphasize the benefits of AI to foster greater acceptance among students. However, it is also crucial to address students’ concerns and challenges proactively, perhaps through targeted support, resources, and training to mitigate these issues.
- (3)
- The perceived concerns and challenges of using AI influence the acceptance of AI tools among architecture students.
- (4)
- There are no significant differences between the Serbian and Montenegrin architecture students in terms of the evaluation of sustainability indicators, the use of AI, and the perceived benefits and challenges of AI use.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Europen Union. Regulation (EU) 2024/1689 of European the Parliament and of the Council of 13 June 2024. Available online: https://eur-lex.europa.eu/eli/reg/2024/1689/oj (accessed on 25 July 2024).
- Intersections between the Academy and Practice, Papers from the 2017 AIA/ACSA Intersections Symposium—Association of Collegiate Schools of Architecture. Available online: https://www.acsa-arch.org/proceeding/intersections-between-the-academy-and-practice-papers-from-the-2017-aia-acsa-intersections-symposium/ (accessed on 26 May 2024).
- Waddell, P. A Behavioral Simulation Model for Metropolitan Policy Analysis and Planning: Residential Location and Housing Market Components of Urbansim. Environ. Plan. B Plan. Des. 2000, 27, 247–263. [Google Scholar] [CrossRef]
- Deng, J.; Lin, Y. The Benefits and Challenges of ChatGPT: An Overview. Front. Comput. Intell. Syst. 2022, 2, 81–83. [Google Scholar] [CrossRef]
- Li, J.; Cao, H.; Lin, L.; Hou, Y.; Zhu, R.; El Ali, A. User Experience Design Professionals’ Perceptions of Generative Artificial Intelligence. In Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 11–16 May 2024. [Google Scholar] [CrossRef]
- Syarifudin, I.; Prabawasari, V.W.; Nugroho, A. Studi komparasi penggunaan tools cahaya omni sebagai pendukung cahaya spotlight pada render exterior dengan software rendering lumion 11, enscape 3.4 dan twinmotion edu 2022. J. Tek. Dan Sci. 2023, 2, 61–71. [Google Scholar] [CrossRef]
- Extance, A. How AI technology can tame the scientific literature. Nature 2018, 561, 273–275. Available online: https://go.gale.com/ps/i.do?p=HRCA&sw=w&issn=00280836&v=2.1&it=r&id=GALE%7CA572907321&sid=googleScholar&linkaccess=fulltext. (accessed on 26 May 2024). [CrossRef]
- Eneyew, D.D.; Capretz, M.A.M.; Bitsuamlak, G.T. Toward Smart-Building Digital Twins: BIM and IoT Data Integration. IEEE Access 2022, 10, 130487–130506. [Google Scholar] [CrossRef]
- Jia, M.; Komeily, A.; Wang, Y.; Srinivasan, R.S. Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications. Autom. Constr. 2019, 101, 111–126. [Google Scholar] [CrossRef]
- Sultan, W.F.A.; Elghonaimy, I.H. Smart Design via Digital Architecture. IET Conf. Proc. 2023, 2023, 470–476. [Google Scholar] [CrossRef]
- Mohamed, Y.; Piras, G.; Muzi, F.; Tiburcio, V.A. Digital Management Methodology for Building Production Optimization through Digital Twin and Artificial Intelligence Integration. Buildings 2024, 14, 2110. [Google Scholar] [CrossRef]
- Kaewunruen, S.; Rungskunroch, P.; Welsh, J. A digital-twin evaluation of Net Zero Energy Building for existing buildings. Sustainability 2019, 11, 159. [Google Scholar] [CrossRef]
- Zhang, Z.; Fort, J.M.; Mateu, L.G. Exploringthe Potential of Artificial Intelligence as a Tool for Architectural Design: A Perception Study Using Gaudí’sWorks. Buildings 2023, 13, 1863. [Google Scholar] [CrossRef]
- Cohen-Almagor, R. Internet architecture, freedom of expression and social responsibility: Critical realism and proposals for a better future. Innov. Eur. J. Soc. Sci. Res. 2015, 28, 147–166. [Google Scholar] [CrossRef]
- Herwix, A. Toward a Responsible Design Science Research Ecosystem for the Digital Age: A Critical Pragmatist Perspective. Available online: http://www.uni-koeln.de/ (accessed on 10 August 2024).
- Xiao, X.; Tian, L.; Alahmadi, K.M.; Gu, X. LANDSCAPE ARCHITECTURE CONSTRUCTION USING LANDSCAPE URBANISM and DYNAMIC NONLINEAR SYSTEM THINKING. Fractals 2022, 30, 2240079. [Google Scholar] [CrossRef]
- Xu, W.; IEEE, S.M.; Gao, Z. An Intelligent Sociotechnical Systems (iSTS) Concept: Toward a Sociotechnically-Based Hierarchical Human-Centered AI Approach. January 2024. Available online: https://arxiv.org/abs/2401.03223v3 (accessed on 10 August 2024).
- Environmental Ethics Anthropocentric to Eco-Centric Approach: A Paradigm Shift on JSTOR. Available online: https://www.jstor.org/stable/43953654 (accessed on 10 August 2024).
- Bölek, B.; Tutal, O.; Özbaşaran, H. A systematic review on artificial intelligence applications in architecture. J. Des. Resil. Archit. Plan. 2023, 4, 91–104. [Google Scholar] [CrossRef]
- Donovan, E. Explaining Sustainable Architecture. IOP Conf. Ser. Earth Environ. Sci. 2020, 588, 032086. [Google Scholar] [CrossRef]
- Khamseh, N.S.M. Global Need for Low Carbon Architecture. J. Sustain. Dev. 2014, 7, 1. [Google Scholar] [CrossRef]
- Lu, W.; Tam, V.W.Y.; Chen, H.; Du, L. A holistic review of research on carbon emissions of green building construction industry. Eng. Constr. Archit. Manag. 2020, 27, 1065–1092. [Google Scholar] [CrossRef]
- Sijakovic, M.; Peric, A. Sustainable architectural design: Towards climate change mitigation. Archnet-IJAR Int. J. Archit. Res. 2021, 15, 385–400. [Google Scholar] [CrossRef]
- Singh, V.; Saxena, S. Sustainable Water Management in Urban Architectural Design. Int. J. Res. Appl. Sci. Eng. Technol. 2023, 11, 6622–6635. [Google Scholar] [CrossRef]
- Kangkum, E.L.A.A. The Role of Sustainable Architecture in Human Health and Well-Being: A Review. Int. J. Res. Sci. Innov. 2023, 10, 16–22. [Google Scholar] [CrossRef]
- Norouzi, M.; Chàfer, M.; Cabeza, L.F.; Jiménez, L.; Boer, D. Circular economy in the building and construction sector: A scientific evolution analysis. J. Build. Eng. 2021, 44, 102704. [Google Scholar] [CrossRef]
- Rahla, K.; Mateus, R.; Bragança, L. Implementing Circular Economy Strategies in Buildings—From Theory to Practice. Appl. Syst. Innov. 2021, 4, 26. [Google Scholar] [CrossRef]
- Wu, R. Application of AI in Construction. Appl. Comput. Eng. 2023, 8, 98–102. [Google Scholar] [CrossRef]
- Li, C.; Zhang, T.; Du, X.; Zhang, Y.; Xie, H. Generative AI for Architectural Design: A Literature Review. arXiv 2024, arXiv:2404.01335. [Google Scholar]
- Cugurullo, F.; Caprotti, F.; Cook, M.; Karvonen, A.; McGuirk, P.; Marvin, S. Artificial Intelligence and the City: Urbanistic Perspectives on AI; Routledge: London, UK, 2023; pp. 1–400. [Google Scholar] [CrossRef]
- Ploennigs, J.; Berger, M. AI art in architecture. AI Civ. Eng. 2023, 2, 8. [Google Scholar] [CrossRef]
- Pan, Y.; Zhang, L. Integrating BIM and AI for Smart Construction Management: Current Status and Future Directions. Arch. Comput. Methods Eng. 2022, 30, 1081–1110. [Google Scholar] [CrossRef]
- Baneres, D.; Guerrero-Roldán, A.E.; Rodríguez-González, M.E.; Karadeniz, A. A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System. Appl. Sci. 2021, 11, 5781. [Google Scholar] [CrossRef]
- Chen, Y.; Li, X.; Liu, X.; Ai, B. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. Int. J. Geogr. Inf. Sci. 2014, 28, 234–255. [Google Scholar] [CrossRef]
- Hurtubia, R. Development of Prototype UrbanSim Models. Available online: https://www.academia.edu/60298769/Development_of_prototype_UrbanSim_models (accessed on 25 May 2024).
- Li, Z.; Ning, H. Autonomous GIS: The next-generation AI-powered GIS. Int. J. Digit. Earth 2023, 16, 4668–4686. [Google Scholar] [CrossRef]
- Minoli, D.; Occhiogrosso, B. Practical Aspects for the Integration of 5G Networks and IoT Applications in Smart Cities Environments. Wirel. Commun. Mob. Comput. 2019, 2019, 5710834. [Google Scholar] [CrossRef]
- Rafsanjani, H.N.; Nabizadeh, A.H. Towards digital architecture, engineering, and construction (AEC) industry through virtual design and construction (VDC) and digital twin. Energy Built Environ. 2023, 4, 169–178. [Google Scholar] [CrossRef]
- Almaz, A.F.; El-Agouz, E.A.E.A.; Abdelfatah, M.T.; Mohamed, I.R. The Future Role of Artificial Intelligence (AI) Design’s Integration into Architectural and Interior Design Education is to Improve Efficiency, Sustainability, and Creativity. Civ. Eng. Archit. 2024, 12, 1749–1772. [Google Scholar] [CrossRef]
- Hayes-Roth, B. An architecture for adaptive intelligent systems. Artif. Intell. 1995, 72, 329–365. [Google Scholar] [CrossRef]
- Rafsanjani, H.N.; Nabizadeh, A.H. Towards human-centered artificial intelligence (AI) in architecture, engineering, and construction (AEC) industry. Comput. Hum. Behav. Rep. 2023, 11, 100319. [Google Scholar] [CrossRef]
- Tsog, N.; Behnam, M.; Sjödin, M.; Bruhn, F. Intelligent Data Processing Using In-Orbit Advanced Algorithms on Heterogeneous System Architecture. In Proceedings of the 2018 IEEE Aerospace Conference, Big Sky, MT, USA, 3–10 March 2018; pp. 1–8. [Google Scholar] [CrossRef]
- Bayar, M.S.; Aziz, Z. Rapid Prototyping and Its Role in Supporting Architectural Design Process. J. Archit. Eng. 2018, 24, 05018003. [Google Scholar] [CrossRef]
- Alanko, J.; Wallin, M. Evaluation of AI-driven Generative Design and Redesign of a MINI-LINK Mounting Kit. Available online: http://hdl.handle.net/20.500.12380/307095 (accessed on 25 May 2024).
- Rane, N. Integrating Leading-Edge Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies for Smart and Sustainable Architecture, Engineering and Construction (AEC) Industry: Challenges and Future Directions. SSRN Electron. J. Sep. 2023, 2. [Google Scholar] [CrossRef]
- Alhassan, M.; Alkhawaldeh, A.; Betoush, N.; Sawalha, A.; Amaireh, L.; Onaizi, A. Harmonizing smart technologies with building resilience and sustainable built environment systems. Results Eng. 2024, 22, 102158. [Google Scholar] [CrossRef]
- Burri, S.; Kumar, A.; Baliyan, A.; Kumar, T.A. Predictive intelligence for healthcare outcomes: An ai architecture overview. In Proceedings of the 2nd International Conference on Smart Technologies and Systems for Next Generation Computing, Villupuram, India, 21–22 April 2023. [Google Scholar]
- Parisi, F.; Fanti, M.P.; Mangini, A.M. Information and communication technologies applied to intelligent buildings: A review. J. Inf. Technol. Constr. 2021, 26, 458. [Google Scholar] [CrossRef]
- Jha, A.K.; Ghimire, A.; Thapa, S.; Jha, A.M.; Raj, R. A Review of AI for Urban Planning: Towards Building Sustainable Smart Cities. In Proceedings of the 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 20–22 January 2021; pp. 937–944. [Google Scholar] [CrossRef]
- Son, T.H.; Weedon, Z.; Yigitcanlar, T.; Sanchez, T.; Corchado, J.M.; Mehmood, R. Algorithmic urban planning for smart and sustainable development: Systematic review of the literature. Sustain. Cities Soc. 2023, 94, 104562. [Google Scholar] [CrossRef]
- Bibri, S.E.; Krogstie, J.; Kaboli, A.; Alahi, A. Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environ. Sci. Ecotechnology 2024, 19, 100330. [Google Scholar] [CrossRef]
- Srivastava, A.; Jawaid, S.; Singh, R.; Gehlot, A.; Akram, S.V.; Priyadarshi, N.; Khan, B. Imperative Role of Technology Intervention and Implementation for Automation in the Construction Industry. Adv. Civ. Eng. 2020, 2022, 6716987. [Google Scholar] [CrossRef]
- Emaminejad, N.; Akhavian, R. Trustworthy AI and robotics: Implications for the AEC industry. Autom. Constr. 2022, 139, 104298. [Google Scholar] [CrossRef]
- Pejić, M.S.; Terzić, M.; Stanojević, D.; Peško, I.; Mučenski, V. Improving construction projects and reducing risk by using artificial intelligence. Soc. Inform. J. 2023, 2, 33–40. [Google Scholar] [CrossRef]
- Nishant, R.; Kennedy, M.; Corbett, J. Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. Int. J. Inf. Manage. 2020, 53, 102104. [Google Scholar] [CrossRef]
- van Wynsberghe, A. Sustainable AI: AI for sustainability and the sustainability of AI. AI Ethics 2021, 1, 213–218. [Google Scholar] [CrossRef]
- Jin, S.; Tu, H.; Li, J.; Fang, Y.; Qu, Z.; Xu, F.; Liu, K.; Lin, Y. Enhancing Architectural Education through Artificial Intelligence: A Case Study of an AI-Assisted Architectural Programming and Design Course. Buildings 2024, 14, 1613. [Google Scholar] [CrossRef]
- Chaillou, S. AI and architecture: An experimental perspective. In The Routledge Companion to Artificial Intelligence in Architecture; Routledge: London, UK, 2021; pp. 420–441. [Google Scholar] [CrossRef]
- Burt, R.S. Network items and the general social survey. Soc. Netw. 1984, 6, 293–339. [Google Scholar] [CrossRef]
- Aaker, A.D.; Kumar, V.; Day, S.G. Marketing Research; John, 9th., Ed.; Wiley & Sons: New York, NY, USA, 2007. [Google Scholar]
- Waas, T.; Hugé, J.; Block, T.; Wright, T.; Benitez-Capistros, F.; Verbruggen, A. Sustainability Assessment and Indicators: Tools in a Decision-Making Strategy for Sustainable Development. Sustainability 2014, 6, 5512–5534. [Google Scholar] [CrossRef]
- Bolek, M.; Bolek, C.; Shopovski, J.; Marolov, D. The consistency of peer-reviewers: Assessment of separate parts of the manuscripts vs final recommendations. Account. Res. 2023, 30, 493–515. [Google Scholar] [CrossRef]
- Ejidike, C.; Mewomo, M.; Olawumi, T.O. Global Trend in Retrofitting Using Smart Technology: A Scientometric Review. In Towards a Sustainable Construction Industry: The Role of Innovation and Digitalisation; Springer International Publishing: Cham, Switzerland, 2023; pp. 153–165. [Google Scholar]
- Skea, J. Research and evidence needs for decarbonisation in the built environment: A UK case study. Build. Res. Inf. 2012, 40, 432–445. [Google Scholar] [CrossRef]
- Asadi, E.; da Silva, M.G.; Antunes, C.H.; Dias, L. A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Build. Environ. 2012, 56, 370–378. [Google Scholar] [CrossRef]
- Ascione, F.; Bianco, N.; Böttcher, O.; Cappiello, A.; Mastellone, M.; Mauro, G.M.; Mühle, J.; Tariello, F. Social housing as an open issue of energy consumption in the building sector in Europe: A case study in Berlin. In Proceedings of the 2023 8th International Conference on Smart and Sustainable Technologies (SpliTech), Split/Bol, Croatia, 20–23 June 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Sourek, M. AI in architecture and engineering from misconceptions to game-changing prospects. Archit. Intell. 2024, 3, 4. [Google Scholar] [CrossRef]
- Lu, L.; Meng, X.; Mao, Z.; Karniadakis, G.E. DeepXDE: A Deep Learning Library for Solving Differential Equations. SIAM Rev. 2021, 63, 208–228. [Google Scholar] [CrossRef]
- Jin, X.; Ahmed, Z.; Pata, U.K.; Kartal, M.T.; Erdogan, S. Do investments in green energy, energy efficiency, and nuclear energy R&D improve the load capacity factor? An augmented ARDL approach. Geosci. Front. 2024, 15, 101646. [Google Scholar] [CrossRef]
Sustainability in Architecture | M | SD | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|---|
Reducing/excluding carbon emissions in buildings | 3.82 | 1.16 | 5% | 9% | 20% | 31% | 35% |
Enhance water efficiency by techniques (cutting back on use, upping recycling, using alternate water sources) | 3.96 | 1.12 | 5% | 6% | 16% | 34% | 39% |
Enhance health and well-being through effective building design, construction, use, accessibility, operations, maintenance, etc. | 4.19 | 1.10 | 5% | 3% | 13% | 26% | 53% |
Use life cycle thinking to reduce, reuse, recover, and recycle for responsible design, production, purchasing, and consuming | 4.07 | 1.07 | 4% | 5% | 14% | 34% | 43% |
Using smart buildings and other technology for sustainability, health, and resilience goals | 4.08 | 1.12 | 4% | 6% | 16% | 26% | 48% |
Cronbach’s Alpha | 0.91 |
Facilitating AI in Architecture | M | SD | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|---|
Performance-based (energy-saving, benefit from daylight, passive design solutions) | 2.73 | 1.38 | 28% | 16% | 23% | 21% | 12% |
Form finding (building envelope design, parametric designs, modular architecture) | 2.91 | 1.36 | 22% | 15% | 28% | 20% | 15% |
Spatial planning (site plan suggestions, plan solution suggestions, mass settlements proposals on an urban scale) | 2.88 | 1.36 | 23% | 14% | 30% | 18% | 15% |
Multi-objective (applications of active and passive systems, mass and façade recommendation for maximum benefits) | 2.91 | 1.44 | 27% | 9% | 28% | 18% | 18% |
Restoration (completing the missing part of structures, transferring important structures to digital platforms) | 2.82 | 1.45 | 26% | 18% | 22% | 16% | 18% |
Design tool development (designing, urban planning, interior) that solve various design problems | 3.02 | 1.44 | 21% | 18% | 19% | 22% | 20% |
Cronbach’s Alpha | 0.96 |
Benefits of Using AI in Architecture | M | SD | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|---|
Increased efficiency | 3.52 | 1.11 | 6% | 10% | 31% | 32% | 21% |
Innovative design solutions (designing, urban planning, and interior) | 3.45 | 1.24 | 9% | 14% | 23% | 31% | 23% |
Improved sustainability | 3.17 | 1.24 | 10% | 21% | 29% | 22% | 18% |
Cronbach’s Alpha | 0.80 |
Challenges of Using AI in Architecture | M | SD | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|---|
Ethical issues | 3.34 | 1.13 | 7% | 13% | 37% | 25% | 18% |
Legal issues | 3.24 | 1.19 | 9% | 17% | 32% | 25% | 17% |
Job displacement | 3.57 | 1.10 | 4% | 11% | 34% | 26% | 25% |
Reliability | 3.30 | 1.19 | 6% | 21% | 31% | 21% | 21% |
Cronbach’s Alpha | 0.84 |
Spearman’s Rho | Sustainability in Architecture | Facilitating AI in Architecture | Benefits of Using AI in Architecture | Challenges of Using AI in Architecture | |
---|---|---|---|---|---|
Sustainability in architecture | Correlation coefficient | 1.00 | 0.10 | 0.26 | 0.13 |
Sig. (2-tailed) | 0.34 | 0.01 | 0.19 | ||
Facilitating AI in architecture | Correlation coefficient | 0.10 | 1.00 | 0.49 | 0.31 |
Sig. (2-tailed) | 0.34 | 0.00 | 0.00 | ||
Benefits of using AI in architecture | Correlation coefficient | 0.26 | 0.49 | 1.00 | 0.42 |
Sig. (2-tailed) | 0.01 | 0.00 | 0.00 | ||
Challenges of using AI in architecture | Correlation coefficient | 0.13 | 0.31 | 0.42 | 1.00 |
Sig. (2-tailed) | 0.19 | 0.00 | 0.00 |
Sustainability in Architecture | N | M | SD | Shapiro–Wilk | |
---|---|---|---|---|---|
Statistic | Sig. | ||||
AI tools used in project design—YES | 45 | 3.96 | 0.98 | 0.86 | 0.00 |
AI tools used in project design—NO | 55 | 4.08 | 0.94 | 0.83 | 0.00 |
Country | N | M | SD | Shapiro–Wilk | ||
---|---|---|---|---|---|---|
Statistic | Sig. | |||||
Sustainability in architecture | Serbia | 51 | 3.98 | 1.10 | 0.82 | 0.00 |
Montenegro | 49 | 4.07 | 0.79 | 0.89 | 0.00 | |
Facilitating AI in architecture | Serbia | 51 | 3.13 | 1.25 | 0.93 | 0.00 |
Montenegro | 49 | 2.62 | 1.28 | 0.92 | 0.00 | |
Benefits of using AI in architecture | Serbia | 51 | 3.48 | 0.95 | 0.94 | 0.01 |
Montenegro | 49 | 3.28 | 1.08 | 0.96 | 0.08 | |
Challenges of using AI in architecture | Serbia | 51 | 3.28 | 0.93 | 0.97 | 0.19 |
Montenegro | 49 | 3.44 | 0.96 | 0.95 | 0.06 |
Sustainability in Architecture | Facilitating AI in Architecture | Benefits of Using AI in Architecture | Challenges of Using AI in Architecture | |
---|---|---|---|---|
Mann–Whitney U | 1190.00 | 971.50 | 1091.50 | 1131.00 |
Wilcoxon W | 2415.00 | 2196.50 | 2316.50 | 2457.00 |
Z | −0.41 | −1.92 | −1.10 | −0.82 |
Asymp. Sig. (2-tailed) | 0.68 | 0.05 | 0.27 | 0.41 |
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Komatina, D.; Miletić, M.; Mosurović Ružičić, M. Embracing Artificial Intelligence (AI) in Architectural Education: A Step towards Sustainable Practice? Buildings 2024, 14, 2578. https://doi.org/10.3390/buildings14082578
Komatina D, Miletić M, Mosurović Ružičić M. Embracing Artificial Intelligence (AI) in Architectural Education: A Step towards Sustainable Practice? Buildings. 2024; 14(8):2578. https://doi.org/10.3390/buildings14082578
Chicago/Turabian StyleKomatina, Dragan, Mirjana Miletić, and Marija Mosurović Ružičić. 2024. "Embracing Artificial Intelligence (AI) in Architectural Education: A Step towards Sustainable Practice?" Buildings 14, no. 8: 2578. https://doi.org/10.3390/buildings14082578