Algorithmic Intimacy: AI Co-authorship and Student Designer identity in Architectural Education

Abstract

This study investigates the psychological dimensions of Human-AI collaboration in architectural education through a retrospective analysis of pedagogical documentation and student work from a second-year architectural design studio. The concept of algorithmic intimacy is introduced to characterise the emotional and collaborative bonds students develop with Artificial Intelligence (AI) agents, specifically examining how these relationships influence authorship attribution and professional identity formation. The research identifies four key themes: the prompt as a medium of personal expression, student ambivalence regarding authorship during critique defences, the tension between efficiency and deep comprehension in the design thinking process, and the projection of personal identity onto AI-generated outputs. While this inverse, concept-first pedagogical model facilitates rapid conceptual exploration, it presents challenges that may constrain students’ creative agency. Consequently, this study proposes a pedagogy of ‘algorithmic reflexivity’ to assist students in navigating authorship, agency, and ethical practice in an AI-augmented discipline. By shifting the analytical focus from the final design outcomes to the Human-AI interaction itself, this study offers critical strategies for integrating generative technologies into creative education.

Keywords

• architectural pedagogy
• artificial intelligence
• design identity
• human-AI collaboration
• generative design
• concept-first pedagogy

Algoritmik Yakınlık: Mimarlık eğitiminde öğrenci tasarımcı kimliği ve yapay zeka ortaklığı

Öz

Bu çalışma, ikinci sınıf bir mimari tasarım stüdyosundaki pedagojik dokümantasyon ve öğrenci çalışmalarının retrospektif bir analizi yoluyla mimarlık eğitiminde İnsan-Yapay Zeka iş birliğinin psikolojik boyutlarını araştırmaktadır. Öğrencilerin Yapay Zeka (YZ) ajanlarıyla geliştirdikleri duygusal ve iş birlikçi bağları karakterize etmek için “algoritmik yakınlık” kavramı tanıtılmakta ve özellikle bu ilişkilerin yazarlık atıflarını ve profesyonel kimlik oluşumunu nasıl etkilediği incelenmektedir. Araştırma dört temel temayı tanımlamaktadır: kişisel ifade aracı olarak ipucu, eleştiri savunmaları sırasında öğrencilerin yazarlık konusundaki ikilemi, tasarım düşünme sürecinde verimlilik ve derin anlayış arasındaki gerilim ve kişisel kimliğin YZ tarafından üretilen çıktılara yansıtılması. Bu ters, kavram odaklı pedagojik model hızlı kavramsal keşfi kolaylaştırırken, yaratıcı etkiyi kısıtlayabilecek zorluklar sunmaktadır. Sonuç olarak, bu çalışma, YZ destekli bir disiplinde öğrencilerin yazarlık, etki ve etik uygulamaları kavramalarına yardımcı olmak için bir “algoritmik refleksivite” pedagojisi önermektedir. Bu çalışma, analitik odağı nihai tasarım çıktılarından İnsan-yapay zeka etkileşiminin kendisine kaydırarak, yaratıcı eğitime üretken teknolojileri entegre etmek için kritik stratejiler sunmaktadır.

Anahtar Kelimeler

• Mimarlık pedagojisi
• Yapay zeka
• Tasarım Kimliği
• İnsan-Yapay zeka iş birliği
• Üretken tasarım
• Kavram-öncelikli pedagoji

Kaynakça

1. Ansone, A., Zālīte-Supe, Z., & Daniela, L. (2025). Generative Artificial Intelligence as a Catalyst for Change in Higher Education Art Study Programs. Computers, 14(4), 154.
2. As, I., Pal, S., & Basu, P. (2018). Artificial intelligence in architecture: Generating conceptual design via deep learning. International Journal of Architectural Computing, 16(4), 306-327.
3. Bai, Y. & Wang, S. (2024). Impact of generative AI interaction and output quality on university students’ learning outcomes: a technology-mediated and motivation-driven approach. Sci Rep 15, 24054.
4. Batista, J., Mesquita, A., & Carnaz, G. (2024). Generative AI and Higher Education: Trends, Challenges, and Future Directions from a Systematic Literature Review. Information, 15(11), 676.
5. British Educational Research Association [BERA]. (2018). Ethical guidelines for educational research (4th ed.). https://www.bera.ac.uk/publication/ethical-guidelines-for-educational-research-2018
6. Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101
7. Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design. Harvard University Press.
8. Caetano, I., Santos, L., & Leitão, A. (2020). Computational design in architecture: Defining parametric, generative and algorithmic design. Frontiers of Architectural Research, 9(2), 287-300.

9. Carpo, M. (2017). The second digital turn: Design beyond intelligence. MIT Press.
10. Ching, F. D. K. (2015). Architecture: Form, space, and order (4th ed.). Wiley.
11. Choudhury, M. M., Eisenbart, B., & Kuys, B. (2025). Artificial intelligence (AI) in the design process – a review and analysis on generative AI perspectives. Proceedings of the Design Society, 5, 631–640.
12. Cross, N. (2006). Designerly ways of knowing. Springer.
13. Cuff, D. (1991). Architecture: The story of practice. MIT Press.
14. Eisenman, P. (1999). Diagram diaries. Thames & Hudson.
15. Eisenman, P. (2007). Written into the void: Selected writings, 1990-2004. Yale University Press.
16. Elish, M. C. & boyd, d. (2018). Situating methods in the magic of Big Data and AI. Communication Monographs, 85(1), 57-80.
17. Felten, P. (2013). Principles of good practice in SoTL. Teaching & Learning Inquiry, 1(1), 121–125
18. Finlay, L. (2002). “Outing” the researcher: The provenance, process, and practice of reflexivity. Qualitative Health Research, 12(4), 531-545.
19. Frampton, K. (2020). Modern architecture: A critical history (5th ed.). Thames & Hudson.
20. Ginsburg, J. C. & Budiardjo, L. A. (2019). Authors and machines. Berkeley Technology Law Journal, 34(2), 343-456.
21. Goldschmidt, G. (2003). The backtalk of self-generated sketches. Design Issues, 19(1), 72-88.
22. Hammersley, M. & Atkinson, P. (2019). Ethnography: Principles in practice (4th ed.). Routledge.
23. Harman, G. (2011). The quadruple object. Zero Books.
24. Hertzmann, A. (2018). Can computers create art? Arts, 7(2), 18.
25. Hollan, J., Hutchins, E., & Kirsh, D. (2000). Distributed cognition: Toward a new foundation for human-computer interaction research. ACM Transactions on Computer-Human Interaction, 7(2), 174-196.
26. Hung-Hsiang, W. & Chun-Han, A.W. (2024). Teaching design students machine learning to enhance motivation for learning computational thinking skills, Acta Psychologica, vol 251.
27. Hutchings, P. (Ed.). (2000). Opening lines: Approaches to the scholarship of teaching and learning. Carnegie Foundation for the Advancement of Teaching.
28. Ibrahim, I., Abu Talib,M., Ammar, A., Tabet Aoul, K.A., & Abuimara, T. (2026). Comparative and experimental analysis of leading text-to-image generative artificial intelligence models for regional residential architectural designs, Results in Engineering, Vol 29, 108835.
29. Kadirhan, Z. & Yildirim, S. (2025). The educational use of Facebook: a phenomenological exploration of faculty members’ and students’ lived experiences. Front. Educ. 10:1719345.
30. Kantosalo, A., Toivonen, H., Xiao, P., & Toivanen, J. M. (2022). From isolated creation to interaction: Observing creative AI in a co-creative setting. In Proceedings of the 13th International Conference on Computational Creativity (pp. 205-213). Association for Computational Creativity.
31. Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
32. Lave, J. & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
33. Lin, Y. & Song, M. (2024). Exploring the Potential of Generative Adversarial Networks in Enhancing Urban Renewal Efficiency. Sustainability, 16(13), 5768.
34. Liu, Y., Li, T. & Fu, Z. (2023). Computational creativity: The Innovative Thinking, Practical methods and Aesthetic Paradigms of AI-driven Design. In: Waldemar Karwowski and Tareq Ahram (eds) Artificial Intelligence, Social Computing and Wearable Technologies. AHFE (2023) International Conference. AHFE Open Access, vol 113. AHFE International, USA.
35. Mallgrave, H. F. (2005). Modern architectural theory: A historical survey, 1673-1968. Cambridge University Press.
36. Norberg-Schulz, C. (1980). Genius loci: Towards a phenomenology of architecture. Rizzoli.
37. Offert, F. & Bell, P. (2021). Perceptual bias and technical metapictures: Critical machine vision as a humanities challenge. AI & Society, 36(4), 1133-1144.
38. Orr, S. & Shreeve, A. (2018). Art and design pedagogy in higher education: Knowledge, values and ambiguity in the creative curriculum. Routledge.
39. Oxman, R. (2006). Theory and design in the first digital age. Design Studies, 27(3), 229-265.
40. Rezwana, J. & Maher, M. L. (2023). Designing creative AI partners with COFI: A framework for modeling interaction in human-AI co-creation. ACM Transactions on Computer-Human Interaction, 30(5), 1-28.
41. Salama, A. M. (2015). Spatial design education: New directions for pedagogy in architecture and beyond. Routledge.
42. Schön, D. A. (1983). The reflective practitioner: How professionals think in action. Basic Books.
43. Schön, D. A. (1985). The design studio: An exploration of its traditions and potentials. RIBA Publications.
44. Schön, D.A. (1987). Educating the reflective practitioner: Toward a new design for teaching and learning in the professions. Jossey-Bass.
45. Speaks, M. (2002). Design intelligence and the new economy. Architectural Record, 190(1), 72-76.
46. Terzidis, K. (2006). Algorithmic architecture. Architectural Press.
47. Tschumi, B. (1996). Architecture and disjunction. MIT Press.
48. Webster, H. (2008). Architectural education after Schön: Cracks, blurs, boundaries and beyond. Journal for Education in the Built Environment, 3(2), 63–74.
49. Wilson, A.D. & Golonka, S. (2013). Embodied cognition is not what you think it is. Front. Psychology 4, vol 58.
50. Yazıcı, G. & Doğan, F. (2019). Interactive Imagery and Shared Mental Models in Design Learning, in Börekçi, N., Koçyıldırım, D., Korkut, F. and Jones, D. (eds.), Insider Knowledge, DRS Learn X Design Conference 2019, 9-12 July, Ankara, Turkey.