KaydedilenlerDerginizi ekleyin

Dergiler / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 34 Sayı: Uygarlığın Dönüşümü: Yapay Zekâ Özel Sayı

YAPAY ZEKA ÇAĞINDA ÇEVRE TASARIMI: SOSYAL BİLİMLER PERSPEKTİFİNDEN BİBLİYOMETRİK VE TEMATİK İÇGÖRÜLER

ENVIRONMENTAL DESIGN IN THE AGE OF AI: BIBLIOMETRIC AND THEMATIC INSIGHTS FROM A SOCIAL SCIENCES PERSPECTIVE

Ahmet Akay(SELÇUK ÜNİVERSİTESİ, MİMARLIK VE TASARIM FAKÜLTESİ, PEYZAJ MİMARLIĞI BÖLÜMÜ, PEYZAJ MİMARLIĞI PR.)0000-0001-7215-9676
Sayfa
452–473
Yayın Tarihi
2025-07-20
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bibliyometrik analiz, bu yayınların zaman içindeki değişimini, coğrafi dağılımını, iş birlikçi ağ yapısını ve atıf profillerini nicel verilerle ortaya koymuştur.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Günümüzde yapay zekâ (YZ) teknolojileri birçok disiplini dönüştürmekte olup, çevre tasarımında da yeni olanaklar yaratmaktadır.
📊
Temel Bulgular & Veri:YZ araçlarının çevre tasarımındaki potansiyeli teknik süreçlerin ötesinde insan-çevre etkileşimini de etkilemektedir.
💡
Sonuç & Katkı:Bibliyometrik analiz, bu yayınların zaman içindeki değişimini, coğrafi dağılımını, iş birlikçi ağ yapısını ve atıf profillerini nicel verilerle ortaya koymuştur.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Bibliyometrik Analiz · Çevresel Tasarım · Tematik Analiz

Özet

Günümüzde yapay zekâ (YZ) teknolojileri birçok disiplini dönüştürmekte olup, çevre tasarımında da yeni olanaklar yaratmaktadır. YZ araçlarının çevre tasarımındaki potansiyeli teknik süreçlerin ötesinde insan-çevre etkileşimini de etkilemektedir. Ancak yapılan çalışmalarda YZ'nin bu alandaki rolü genellikle teknik odaklı kalmış, insan, toplum ve kültür gibi sosyal boyutlar yeterince derinlemesine incelenmemiştir. Bu çalışma, YZ teknolojilerinin çevre tasarımı alanındaki kullanımını sosyal bilimler perspektifinden ele alan bibliyometrik ve tematik bir analiz sunmaktadır. Scopus veri tabanında ‘yapay zekâ’ ve ‘çevresel tasarım’ kavramları ile ilişkili olduğu belirlenen 170 çalışma arasından konu kategorisi ‘Sosyal Bilimler’ olan 39 makale analiz için seçilmiştir. Bibliyometrik analiz, bu yayınların zaman içindeki değişimini, coğrafi dağılımını, iş birlikçi ağ yapısını ve atıf profillerini nicel verilerle ortaya koymuştur. Ardından tematik analiz ile bu makalelerde öne çıkan araştırma konuları, veri kaynakları ve uygulanan yöntemler incelenmiştir. Bulgular; YZ’nin algı ve deneyimin nicelleştirilmesinden insan refahı ve güvenlik analizlerine, toplumsal/etik boyutlardan üretken YZ’nin tasarım süreçlerine entegrasyonuna kadar geniş bir yelpazede kullanıldığını göstermektedir. Bu çalışma, YZ'nin çevre tasarımında sadece teknik bir araç olmanın ötesinde, toplumsal yapıları ve insan deneyimlerini etkileyen bir olgu olarak kabul görmesi gerektiğini savunarak, alanın sosyal boyutlarının daha iyi anlaşılmasına katkı sağlamayı ve gelecek araştırmalar için zemin oluşturmayı hedeflemektedir.

Abstract

Today, artificial intelligence (AI) technologies are transforming numerous disciplines and creating new opportunities in the field of environmental design. The potential of AI tools in environmental design extends beyond technical processes, influencing human-environment interaction. However, existing studies on this topic have predominantly maintained a technical focus, with limited in-depth exploration of social dimensions such as human, society, and culture. This paper presents a bibliometric and thematic analysis of AI technologies in environmental design from a social sciences perspective. From an initial set of 170 works identified in Scopus as related to “artificial intelligence” and “environmental design,” 39 articles classified under the “Social Sciences” category were selected for analysis. The bibliometric analysis quantitatively examines the temporal evolution, geographical distribution, collaborative network structures, and citation profiles of these publications. Subsequently, thematic analysis investigates the prominent research topics, data sources, and methodological approaches applied in these studies. Findings reveal a broad spectrum of AI applications from quantifying perception and experience to analyses of human well-being and safety, and from socio-ethical considerations to the integration of generative AI in design processes. By arguing that AI in environmental design should be regarded not merely as a technical tool but as a phenomenon influencing social structures and human experiences, this study aims to deepen understanding of the field’s social dimensions and provides a foundation for future research.

🔖

Bu Makaleye Atıf Yap

Akay, A. (2025). YAPAY ZEKA ÇAĞINDA ÇEVRE TASARIMI: SOSYAL BİLİMLER PERSPEKTİFİNDEN BİBLİYOMETRİK VE TEMATİK İÇGÖRÜLER. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 34 Sayı: Uygarlığın Dönüşümü: Yapay Zekâ Özel Sayı, 452–473. https://doi.org/10.35379/cusosbil.1696548

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (50)

  1. Ahmed, H. T. & Aly, A. M. (2023). Recycled waste materials in landscape design for sustainable development (al-ahsa as a model). Sustainability (Switzerland), 15(15), Article 11705. https://doi.org/10.3390/su151511705
  2. AlTawil, T. N. & Rahhal, A. (2025). Examining synergies between uae corporate social responsibility laws and corporate governance frameworks. Journal of Money Laundering Control, 28(2), 369-384. https://doi.org/10.1108/JMLC-03-2024-0053
  3. Arango-Uribe, M. L., Barrera-Causil, C. J., Pallares, V., Rojas, J. M., Mercado Díaz, L. R., Marrone, R. & Marmolejo-Ramos, F. (2023). Statistical modelling of the impact of online courses in higher education on sustainable development. International Journal of Sustainability in Higher Education, 24(2), 404-425. https://doi.org/10.1108/IJSHE-12-2021-0495
  4. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  5. Bakour, F., & Chougui, A. (2024). Transforming architecture in the post-digital era: Leveraging actor network theory and spatial agency for interactive spaces. New Design Ideas, 8(Special Issue), 24-58 https://doi.org/10.62476/ndisi.24
  6. Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with computers, 23(1), 4-17. http://dx.doi.org/10.1016/j.intcom.2010.07.003
  7. Choi, W., Na, J. & Lee, S. (2025). Assessment of residents for cpted-based crime prevention services in south korea. International Journal of Urban Sciences. https://doi.org/10.1080/12265934.2025.2488733
  8. De Luca, F., Natanian, J. & Wortmann, T. (2024). Ten questions concerning environmental architectural design exploration. Building and Environment, 261, Article 111697. https://doi.org/10.1016/j.buildenv.2024.111697
  9. Dong, W., Dai, D., Liu, M., Wang, Y., Li, S. & Shen, P. (2025). Combined effects of the visual-thermal environment on restorative benefits in hot outdoor public spaces: A case study in shenzhen, china. Building and Environment, 272, Article 112690. https://doi.org/10.1016/j.buildenv.2025.112690
  10. Dwyer, C. (2011). Socio-technical Systems Theory and Environmental Sustainability. All Sprouts Content. 431. https://aisel.aisnet.org/sprouts_all/431
  11. Ekici, B., Kazanasmaz, Z. T., Turrin, M., Taşgetiren, M. F. & Sariyildiz, I. S. (2021). Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems, algorithms, results, and method validation. Solar Energy, 224, 309-326.
  12. Fernberg, P., George, B. H. & Chamberlain, B. (2023). Producing 2d asset libraries with ai-powered image generators. Journal of Digital Landscape Architecture, 2023(8), 186-194. https://doi.org/10.14627/537740020
  13. Guo, X., Lee, K., Wang, Z. & Liu, S. (2021). Occupants’ satisfaction with leed- and non-leed-certified apartments using social media data. Building and Environment, 206, Article 108288. https://doi.org/10.1016/j.buildenv.2021.108288
  14. Gupta, S., Kaur, S., Gupta, M. & Singh, T. (2024). Ai empowered academia: A fuzzy prioritization framework for academic challenges. Journal of International Education in Business. https://doi.org/10.1108/JIEB-06-2024-0071
  15. Karadağ, D. & Ozar, B. (2025). A new frontier in design studio: Ai and human collaboration in conceptual design. Frontiers of Architectural Research. https://doi.org/10.1016/j.foar.2025.01.010
  16. Kim, J. & Lee, Y. (2024). Accuracy evaluation of tree images created using generative artificial intelligence. Journal of Digital Landscape Architecture, 2024(9), 1029-1037. https://doi.org/10.14627/537752098
  17. Kim, S. & Lee, Y. (2024). Comparative analysis of landscape element images created by text-to-image artificial intelligence tools in the design process. Journal of Digital Landscape Architecture, 2024(9), 1021-1028. https://doi.org/10.14627/537752097
  18. Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford, 2005; online edn, Oxford Academic, https://doi.org/10.1093/oso/9780199256044.001.0001
  19. Lee, C. & Lee, Y. (2024). Harnessing artificial intelligence for designers: Conversion of design sketches into digital images using ai image generators. Journal of Digital Landscape Architecture, 2024(9), 1012-1020. https://doi.org/10.14627/537752096
  20. Liu, S., Schiavon, S., Das, H. P., Jin, M. & Spanos, C. J. (2019). Personal thermal comfort models with wearable sensors. Building and Environment, 162, Article 106281. https://doi.org/10.1016/j.buildenv.2019.106281
  21. Lohani, N. (2024). AI-based environmental sustainbility: transforming conservation efforts. International Journal for Multidisciplinary Research, 6(2), 1-7. https://doi.org/10.36948/ijfmr.2024.v06i02.16997
  22. Ma, J. & Qu, B. (2025). Estimating the predictability of physical activities in urban parks based on landscape morphology—empirical analysis based on 10 urban parks in nanjing, china. Landscape Research, 50(1), 39-57. https://doi.org/10.1080/01426397.2024.2387174
  23. Mansouri, A., Naghdi, M. & Erfani, A. (2025). Machine learning for leadership in energy and environmental design credit targeting: Project attributes and climate analysis toward sustainability. Sustainability (Switzerland), 17(6), Article 2521. https://doi.org/10.3390/su17062521
  24. Nava, C. & Melis, A. (2024). Generative ai and complexity towards a new paradigm in regenerative digital design. Agathon - International Journal of Architecture, Art and Design, 16, 40-49. https://doi.org/10.19229/2464-9309/1632024
  25. Ogawa, Y., Oki, T., Zhao, C., Sekimoto, Y. & Shimizu, C. (2024). Evaluating the subjective perceptions of streetscapes using street-view images. Landscape and Urban Planning, 247, Article 105073. https://doi.org/10.1016/j.landurbplan.2024.105073
  26. Qi, Z., Li, J., Yang, X. & He, Z. (2025). How the characteristics of street color affect visitor emotional experience. Computational Urban Science, 5(1), Article 7. https://doi.org/10.1007/s43762-025-00167-z
  27. Qin, X., Yang, D. & Wangari, V. W. (2024). Quantitative characterization and evaluation of highway greening landscape spatial quality based on deep learning. Environmental Impact Assessment Review, 107, Article 107559. https://doi.org/10.1016/j.eiar.2024.107559
  28. Ropohl, G. (1999). Philosophy of socio-technical systems. Society for Philosophy and Technology Quarterly Electronic Journal, 4(3), 186-194. https://doi.org/10.5840/techne19994311
  29. Santos, M. R., & Carvalho, L. C. (2025). AI-driven participatory environmental management: Innovations, applications, and future prospects. Journal of Environmental Management, 373, 123864. https://doi.org/10.1016/j.jenvman.2024.123864
  30. Senem, M. O., Tuncay, H. E., Koç, M. & As, I. (2024). Generating landscape layouts with gans and diffusion models. Journal of Digital Landscape Architecture, 2024(9), 137-144. https://doi.org/10.14627/537752013
  31. Song, Q., Luo, D., Li, M., Gong, P., Qiu, W. & Li, W. (2023). The influence of perceived landscape qualities on economic vitality: A case study of a retail coffee chain. Journal of Digital Landscape Architecture, 2023(8), 463-475. https://doi.org/10.14627/537740049
  32. Su, L., Chen, W., Zhou, Y. & Fan, L. (2023). Exploring city image perception in social media big data through deep learning: A case study of zhongshan city. Sustainability (Switzerland), 15(4), Article 3311. https://doi.org/10.3390/su15043311
  33. Su, N., Li, W. & Qiu, W. (2023). Measuring the associations between eye-level urban design quality and on-street crime density around new york subway entrances. Habitat International, 131, Article 102728. https://doi.org/10.1016/j.habitatint.2022.102728
  34. Sungur, M. & Akçaova, A. (2024). Experiential Learning Method In Interior Architecture Education, Stairs Example, A Vertical Circulation Element. The Turkish Online Journal of Design Art and Communication, 14 (4), 986-996. https://doi.org/10.7456/tojdac.1514342
  35. Tan, C., Zhong, X. & Fricker, P. (2024). Ai as a collaborative partner in landscape form-finding. Journal of Digital Landscape Architecture, 2024(9), 69-78. https://doi.org/10.14627/537752008
  36. Tang, Y., Xiao, W. & Yuan, F. (2025). Evaluating objective and perceived ecosystem service in urban context: An indirect method based on housing market. Landscape and Urban Planning, 254, Article 105245. https://doi.org/10.1016/j.landurbplan.2024.105245
  37. Tebyanian, N. (2020). Application of machine learning for urban landscape design: A primer for landscape architects. Journal of Digital Landscape Architecture, 2020(5), 217-226. https://doi.org/10.14627/537690023
  38. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Human relations, 4(1), 3-38. https://doi.org/10.1177/001872675100400101
  39. Vogiatzaki, M., Zerefos, S. & Tania, M. H. (2020). Enhancing city sustainability through smart technologies: A framework for automatic pre-emptive action to promote safety and security using lighting and ict-based surveillance. Sustainability (Switzerland), 12(15), Article 6142. https://doi.org/10.3390/su12156142
  40. Wang, H., Tassinary, L. G. & Newman, G. D. (2024). Developing the health effect assessment of landscape (heal) tool: Assessing the health effects of community greenspace morphology design on non-communicable diseases. Landscape and Urban Planning, 244, Article 104990. https://doi.org/10.1016/j.landurbplan.2023.104990
  41. Wang, X., Zhu, B., Chen, Z., Ma, D., Sun, C., Wang, M. & Jiang, X. (2024). Landscape perception in cultural and creative industrial parks: Integrating user-generated content (ugc) and electrodermal activity insights. Sustainability (Switzerland), 16(21), Article 9228. https://doi.org/10.3390/su16219228
  42. Xu, M., Wang, Y. & Yu, H. (2025). Realms of aesthetic experience of classical chinese gardens based on semantic analysis of online tourism reviews. Humanities and Social Sciences Communications, 12(1), Article 101. https://doi.org/10.1057/s41599-025-04438-2
  43. Yang, J., Fricker, P. & Jung, A. (2022). From intuition to reasoning: Analyzing correlative attributes of walkability in urban environments with machine learning. Journal of Digital Landscape Architecture, 2022(7), 71-81. https://doi.org/10.14627/537724008
  44. Yang, X., Jiang, D. & Liu, M. (2024). Construction and application of a landscape design teaching platform driven by artificial intelligence. International Journal of Web-Based Learning and Teaching Technologies, 19(1). https://doi.org/10.4018/IJWLTT.336483
  45. Yanik, P. M., Manganelli, J., Merino, J., Threatt, A. L., Brooks, J. O., Green, K. E. & Walker, I. D. (2014). A gesture learning interface for simulated robot path shaping with a human teacher. IEEE Transactions on Human-Machine Systems, 44(1), 41-54, Article 6687233. https://doi.org/10.1109/TSMC.2013.2291714
  46. Ye, X., Huang, T., Song, Y., Li, X., Newman, G., Wu, D. J. & Zeng, Y. (2025). Generating conceptual landscape design via text-to-image generative ai model. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/23998083251316064
  47. Zeng, Y., Chen, J., Jin, N., Jin, X. & Du, Y. (2022). Air quality forecasting with hybrid lstm and extended stationary wavelet transform. Building and Environment, 213, Article 108822. https://doi.org/10.1016/j.buildenv.2022.108822
  48. Zhang, Z. & Bowes, B. (2019). The future of artificial intelligence (ai) and machine learning (ml) in landscape design: A case study in coastal virginia, USA. Journal of Digital Landscape Architecture, 2019(4), 2-9. https://doi.org/10.14627/537663001
  49. Zhang, Z. & Cantrell, B. (2021). Cultivated wildness: Technodiversity and wildness in machines. Landscape Architecture Frontiers, 9(1), 52-65. https://doi.org/10.15302/J-LAF-1-020040
  50. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007