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Journals / Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi / 2026 / Cilt: 19 Sayı: 1

Reframing Architectural Pedagogy Within AI Supported Personalized Learning Experiences

Yapay Zekâ Destekli Kişiselleştirilmiş Öğrenme Deneyimleri Bağlaminda Mimarlik Pedagojisinin Yeniden Çerçevelenmesi

Havva Alkan Bala(ABDULLAH GUL UNIVERSITY)0000-0002-0027-7531
Furkan Göktepe(Çukurova)0009-0006-8599-8341
Pages
85–99
Publication Date
2026-06-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The framework organizes AI-supported practices into four functional clusters: generative AI, analytical-tectonic tools, visualization and presentation tools, and learning-guidance systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study examines how artificial intelligence (AI)-supported tools can contribute to personalized learning in architectural education through an integrative literature synthesis.
📊
Key Findings & Data:Rather than treating AI as a purely technical layer, the study positions it as a pedagogical actor that reshapes feedback practices, design judgment, authorship, and studio culture.
💡
Conclusion & Impact:The framework organizes AI-supported practices into four functional clusters: generative AI, analytical-tectonic tools, visualization and presentation tools, and learning-guidance systems.
🔬Design:Olgu Sunumu
📍Scope:Comprehensive Sample
⚙️Variables:Mimarlık eğitimi · eğitimde yapay zeka · mimarlık pedagojisi

Abstract

This study examines how artificial intelligence (AI)-supported tools can contribute to personalized learning in architectural education through an integrative literature synthesis. Rather than treating AI as a purely technical layer, the study positions it as a pedagogical actor that reshapes feedback practices, design judgment, authorship, and studio culture. The studio-centered and interdisciplinary nature of architectural education often struggles to address learner diversity through uniform curricula and limited feedback systems. Drawing on recent studies of AI-supported personalization, adaptive learning, design education, and architectural studio pedagogy, the article develops a conceptual framework that links learner needs with pedagogical safeguards. The framework organizes AI-supported practices into four functional clusters: generative AI, analytical-tectonic tools, visualization and presentation tools, and learning-guidance systems.

Özet

Bu çalışma, yapay zekâ (YZ) destekli araçların mimarlık eğitiminde kişiselleştirilmiş öğrenmeyi nasıl destekleyebileceğini entegratif bir literatür sentezi aracılığıyla incelemektedir. Çalışma, YZ’yi yalnızca teknik bir araç olarak değil; geri bildirim süreçlerini, tasarım yargısını, yazarlık anlayışını ve stüdyo kültürünü dönüştüren bir pedagojik aktör olarak ele almaktadır. Mimarlık eğitiminin stüdyo merkezli ve disiplinler arası yapısı, öğrenen çeşitliliğini tek tip müfredat ve sınırlı geri bildirim mekanizmalarıyla her zaman karşılayamamaktadır. Eğitimde YZ destekli kişiselleştirme, adaptif öğrenme, tasarım eğitimi ve mimarlık stüdyosu pedagojisine ilişkin güncel çalışmalar temel alınarak öğrenen ihtiyaçlarını pedagojik güvencelerle ilişkilendiren kavramsal bir çerçeve geliştirilmiştir. Bu çerçeve; üretken yapay zekâ, analitik-tektonik araçlar, görselleştirme ve sunum araçları ile öğrenme rehberliği sistemlerinden oluşan dört işlevsel küme etrafında yapılandırılmıştır.

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Cite This Article

Bala, H. A., Göktepe, F. (2026). Reframing Architectural Pedagogy Within AI Supported Personalized Learning Experiences. Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, Cilt: 19 Sayı: 1, 85–99. https://doi.org/10.20854/bujse.1938609

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References (40)

  1. Agarwal, M. K. (2024). Paradigm shift in architectural pedagogy incorporating artificial intelligence. International Journal for Research in Applied Science and Engineering Technology, 12(4), 1315-1329. https://doi.org/10.22214/ijraset.2024.60055.
  2. Alamasi, R., & Asfour, O. S. (2026). Applications of generative AI in architectural design education: A systematic review and future insights. Digital, 6(1), Article 6. https://doi.org/10.3390/digital6010006.
  3. Alana, H., Fikry, M., & Hasan, A. (2026). Human-AI collaborative design in architectural studios: Evaluating paradigm shifts across the six stages of the design process. Buildings, 16(7), Article 1445. https://doi.org/10.3390/buildings16071445.
  4. Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. https://doi.org/10.1016/j.caeai.2024.100215.
  5. Alfredo, R., Milesi, M., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Co-designing AI-powered learning analytics: Bringing students and teachers together. International Journal of Educational Technology in Higher Education, 22, Article 78. https://doi.org/10.1186/s41239-025-00572-8.
  6. Alonso de Castro, M. G., & Bueno, J. (2025). AI tools in education via Erasmus+ projects: Analysis and best practices. Zenodo. https://doi.org/10.5281/zenodo.17436162.
  7. Alshahrani, A., & Mostafa, A. M. (2025). Enhancing the use of artificial intelligence in architectural education: Case study Saudi Arabia. Frontiers in Built Environment, 11, Article 1610709. https://doi.org/10.3389/fbuil.2025.1610709.
  8. Aly, A., Elazazy, A., & Sharaf, N. (2025). Integrating generative AI in architectural education: A comparative study of traditional, stock LLMs, and custom tools. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Vol. 1, GRAPP, pp. 415-420). SciTePress. https://doi.org/10.5220/0013378000003912.
  9. Atalay, F. (2025). Artificial intelligence in architectural education: New learning and design approaches. Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA, 8(1), 343-355. https://doi.org/10.38027/ICCAUA2025TR0033.
  10. Bakir, R., & Alsaadani, S. (2022). A mixed methods study of architectural education during the initial COVID-19 lockdown: Student experiences in design studio and technology courses. Open House International, 47(2), 338-360. https://doi.org/10.1108/OHI-09-2021-0206.
  11. Başarır, L. (2022). Modelling AI in architectural education. Gazi University Journal of Science, 35(4), 1260-1278.
  12. Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), Article 4. https://doi.org/10.1186/s41239-023-00436-z.
  13. Crowther, P. (2013). Understanding the signature pedagogy of the design studio and the opportunities for its technological enhancement. Journal of Learning Design, 6(3), 18-28.
  14. Farhood, H., Nyden, M., Beheshti, A., & Muller, S. (2025). Artificial intelligence-based personalised learning in education: A systematic literature review. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00598-x.
  15. Gabay, R. A. E., Funa, A. A., & Ricafort, J. D. (2026). Generative artificial intelligence (GenAI) for academic writing in higher education: A scoping review of applications, challenges, and implications. International Journal of Education in Mathematics, Science and Technology, 14(1), 200-232. https://doi.org/10.46328/ijemst.5682.
  16. Göçen, A., & Aydemir, F. (2020). Artificial intelligence in education and schools. Research on Education and Media, 12(1), 13-21.
  17. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information and Libraries Journal, 26(2), 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x.
  18. Gunaydin Donduran, C., Kasali, A., & Dogan, F. (2024). Artificial intelligence as a pedagogical tool for architectural design education. Journal of Design Studio, 6(2), 219-229. https://doi.org/10.46474/jds.1533480.
  19. Hariyanto, Kristianingsih, F. X. D., & Maharani, R. (2025). Artificial intelligence in adaptive education: A systematic review of techniques for personalized learning. Discover Education, 4(1), Article 458. https://doi.org/10.1007/s44217-025-00908-6.
  20. Horvath, A.-S., & 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.
  21. Inuwa, A. U., Sulaiman, S., & Samsudin, R. (2025). Systematic literature review on artificial intelligence-driven personalized learning. International Journal of Advanced Computer Science and Applications, 16(6). https://doi.org/10.14569/IJACSA.2025.0160636.
  22. Jian, M. (2023). Personalized learning through AI. Advances in Engineering Innovation, 5(1), 16-19. https://doi.org/10.54254/2977-3903/5/2023039.
  23. Karadag, D. (2025). AI in architectural education: Rethinking studio culture. PLANARCH - Design and Planning Research, 9(2), 243-253. https://doi.org/10.54864/planarch.1749891.
  24. Karadag, D., & Ozar, B. (2025). A new frontier in design studio: AI and human collaboration in conceptual design. Frontiers of Architectural Research, 14(6), 1536-1550. https://doi.org/10.1016/j.foar.2025.01.010.
  25. Kaur, H., & Pavlova, M. (2026). Machine-assisted thematic mapping of AI in education discourse: Systematic review from 2005-2024. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-07342-5.
  26. Kerimbayev, N., Adamova, K., Shadiev, R., & Altinay, Z. (2025). Intelligent educational technologies in individual learning: A systematic literature review. Smart Learning Environments, 12(1), Article 3. https://doi.org/10.1186/s40561-024-00360-3.
  27. Kofinas, A. K., Tsay, C. H.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522-2549. https://doi.org/10.1111/bjet.13585.
  28. Lekesiz, G., & Muezzinoglu, C. (2025). An approach to AI-supported learning in architectural education: Case of speculative space design. A/Z ITU Journal of Faculty of Architecture, 22, 217-231. https://doi.org/10.58278/0. 2025.81.
  29. Ma, Y., Su, Y., Li, M., Zhang, Y., Chai, W., & Huang, A. (2025). Preparing students for an AI-driven world: Generative AI and curriculum reform in higher education. Frontiers of Digital Education, 2(4), Article 30. https://doi.org/10.1007/s44366-025-0067-6.
  30. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Dartmouth College.
  31. Mehan, A., Howell, C., Montejano, E., Stuckemeyer, J., & Mostafavi, S. (2024). FabriCity-XR: A phygital lattice structure mapping spatial justice-integrated design to AR-enabled assembly workflow. In 112th ACSA Annual Meeting Proceedings: Disruptors on the Edge (pp. 180-187).
  32. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693.
  33. 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.
  34. Oh, D. (2024). Generative AI in higher education design studio. In Proceedings of NordDesign 2024 (pp. 588-597). The Design Society. https://doi.org/10.35199/NORDDESIGN2024.63.
  35. Park, H.(2025) Generative AI in Studio-Based Design Education: Human–AI Relationships, Collaboration and Assessment, in Chang, C.-Y., Chen, C.-H., & Hsu, Y. (eds.), IASDR 2025: Design Next, 02-05 December, Taipei, Taiwan. https://doi.org/10.21606/iasdr.2025.845.
  36. Sapawi, M.S., & Yusoff, N.M. (2025). Artificial Intelligence in Curriculum Development: A Global Systematic Review of Trends, Challenges, and Strategic Directions. Journal of Curriculum Studies Research.
  37. Sharma, M., Pant, P. & Gopinath, A. (2025). Embedding AI into Architectural Workflows: Design Process Reengineered. International Journal of Environmental Sciences, 2949-2955. https://doi.org/10.64252/443wrx75.
  38. Song, F., & Rabu, S.N. (2025). Trends, Advantages, and Challenges: A Systematic Literature Review of Artificial Intelligence in Design Education. Journal of Educational and Social Research.
  39. Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, New Series, Vol. 59, No. 236. (Oct., 1950), pp. 433-460. https://www.cs.mcgill.ca/~dprecup/courses/AI/Materials/turing1950.pdf.
  40. Whittemore, R. and Knafl, K. (2005), The integrative review: updated methodology. Journal of Advanced Nursing, 52: 546-553. https://doi.org/10.1111/j.1365-2648.2005.03621.x.