Exploring design generativity when AI is in the loop

For four decades, design research has advanced by treating designing as something that can be modelled. It has done so empirically, through studies of design cognition and, more recently, design neurocognition (Dorst & Cross, 2001; J. S. Gero & Milovanovic, 2020; Hay et al., 2022; Hay, 2025); axiomatically and conceptually, through design theory (Hatchuel & Weil, 2008; Suh, 2001; Zeng, 2026); and computationally, through systematic method and models of design processes (Gero & Kannengiesser, 2004; Pahl et al., 2007). The recent emergence of large language models, foundation models, and generative AI adds a fourth mode: systems that themselves generate designs, in natural language and in graphical or geometric form, and that increasingly participate in the design activity rather than merely support it (Chiarello et al., 2024; Regenwetter et al., 2022; Shea et al., 2025; Song et al., 2024).

This shift is not only a matter of new tools. It reopens foundational questions that the design computing and cognition community has long pursued. What is the nature of generativity when it is distributed between human designers and generative systems? How do our cognitive and computational models of designing hold up, or need to be revised, when a model can propose, expand, and restructure the space of concepts and objects (Raina et al., 2019)? What becomes of design theory’s account of the unknown, of fixation and expansion (Jansson & Smith, 1991), and of the relationship between concepts and knowledge (Hatchuel & Weil, 2008), once foundation models are in the loop?

This thematic collection invites contributions that address the intersection of artificial intelligence and design at exactly this level: not AI as an application layer, but AI as a phenomenon that challenges and renews our theories, methods, and models of designing. It follows the theme of DCC’26 and welcomes extended versions of work presented there, while remaining open to the broader research community. AI EDAM has already begun to host this conversation, with recent studies in the journal examining generative AI as a co-ideation partner in the design process (Wang et al., 2025), the effects of generative model type on design creativity (Ge & Hou, 2025), as well the dynamics reallocation of workload between humans and AI-assisted machines (Kirgil-Budakli et al., 2025). This thematic collection aims to deepen and consolidate it.

We seek rigorous theoretical, empirical, and computational contributions that advance our understanding of designing under conditions of artificial intelligence. We welcome contributions from the full breadth of design research traditions and are particularly interested in work that:

• treats generativity as an object of study in its own right (Bordas et al., 2024), clarifying what generative AI adds to, or subtracts from, the generative capacity of human designers and design organizations;
• confronts established frameworks of design theory, design cognition, and design computing with the behavior of contemporary AI systems, and proposes revisions where those frameworks strain;
• moves beyond demonstrations of capability toward explanatory or predictive models of AI-augmented and AI-mediated designing;
• takes seriously the questions of measurement and method raised when generative systems are both instruments and objects of design research (Shah et al., 2003).

Topics include, but are not limited to:

• Generative AI, foundation models, and large language models as design agents and design partners
• Human–AI collaboration: division of design labor, control, and initiative between designers and generative systems
• Fixation, variety, and expansion in AI-supported and AI-generated design
• Cognitive and neurocognitive studies of designers working with generative AI
• Computational models of design processes that incorporate learning-based generative components
• Evaluation of AI-generated designs: originality, feasibility, value, and the limits of current metrics
• Prompting, specification, and requirement formulation as design activities
• Multi-agent and distributed generative design; AI in collaborative and organizational design settings
• Representation, visual and spatial reasoning, and geometry generation with AI
• Epistemological and methodological implications of using AI systems as instruments in design research
• AI in design education: ideation support, studio critique, and the formation of designers

Timeline (indicative)

Submissions will only be accepted during the submission window, between March 1 and June 30, 2027.

Manuscripts submitted outside this window will not be considered. All submissions will be processed after June 30 and will be sent for review with the aim of providing a first decision by August 2027.

Submission window open: March 1 2027

Submission window closes: June 30 2027

First review decision: August 2027

Revised manuscript: October 2027

Second review decision: January 2028

Revised manuscript: March 2028

Publication of thematic collection: May 2028

A paper development workshop will be held as a side event of the Design Theory SIG in Paris, on the afternoon of Wednesday, February 3, 2027. Authors considering a submission are warmly invited to submit a full paper or at least an extended abstract to all guest editors by December 11, 2026. Participation in the PDW will be in person, with no remote presentations. Participation is optional and is not a condition of submission.  

Submission

Manuscripts should be submitted through the AI EDAM online submission system, selecting this thematic collection, and prepared according to the journal’s author guidelines (multimediafluids.uam.elogim.com/aie).

Informal enquiries and proposals for contributions are warmly encouraged and may be sent to the guest editors in advance of submission.

Proposed Guest Editors

Antoine Bordas – Mines Paris PSL

Filippo Chiarello – University of Pisa

Pascal Le Masson – Mines Paris PSL

Yong Zeng – Concordia University (as AI EDAM Associate Editor)

References

Bordas, A., Le Masson, P., Thomas, M., & Weil, B. (2024). What is generative in generative artificial intelligence? A design-based perspective. Research in Engineering Design. https://doi.org/10.1007/s00163...

Chiarello, F., Barandoni, S., Škec, M. M., & Fantoni, G. (2024). Generative large language models in engineering design: Opportunities and challenges. Proceedings of the Design Society, 4, 1959–1968. https://doi.org/10.1017/pds.20...

Dorst, K., & Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437. https://doi.org/10.1016/S0142-...

Ge, W., & Hou, G. (2025). The effects of generative AI model type and visual stimuli type on design creativity. AI EDAM, 39, e17. https://doi.org/10.1017/S08900...

Gero, J., & Kannengiesser, U. (2004). The Situated Function-Behaviour-Structure Framework. Design Studies, 25, 373–391. https://doi.org/10.1016/j.dest...

Gero, J. S., & Milovanovic, J. (2020). A framework for studying design thinking through measuring designers’ minds, bodies and brains. Design Science, 6, e19. https://doi.org/10.1017/dsj.20...

Hatchuel, A., & Weil, B. (2008). C-K design theory: An advanced formulation. Research in Engineering Design, 19(4), 181.

Hay, L. (2025). The future of design neurocognition research. Designing, 1(1), 38–46. https://doi.org/10.1177/304976...

Hay, L., Duffy, A. H. B., Gilbert, S. J., & Grealy, M. A. (2022). Functional magnetic resonance imaging (fMRI) in design studies: Methodological considerations, challenges, and recommendations. Design Studies, 78, 101078. https://doi.org/10.1016/j.dest...

Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11. https://doi.org/10.1016/0142-6...

Kirgil-Budakli, R., Zeng, Y., & Akgunduz, A. (2025). Dynamic workload reallocation for human–robot teams based on real-time stress analysis. AI EDAM, 39, e18. https://doi.org/10.1017/S08900...

Pahl, G., Beitz, W., Feldhusen, J., & Grote, K.-H. (2007). Engineering Design. Springer. https://doi.org/10.1007/978-1-...

Raina, A., McComb, C., & Cagan, J. (2019). Learning to Design From Humans: Imitating Human Designers Through Deep Learning. Journal of Mechanical Design, 141(111102). https://doi.org/10.1115/1.4044...

Regenwetter, L., Nobari, A. H., & Ahmed, F. (2022). Deep Generative Models in Engineering Design: A Review. Journal of Mechanical Design, 144(071704). https://doi.org/10.1115/1.4053...

Shah, J. J., Smith, S. M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Design Studies, 24(2), 111–134. https://doi.org/10.1016/S0142-...

Shea, K., Stanković, T., Agrawal, A., Cagan, J., & McComb, C. (2025). Expanding the Generative Power of Large Language Models for Design Through Formal Design Grammars and Languages. Journal of Computing and Information Science in Engineering, 25(120802). https://doi.org/10.1115/1.4070...

Song, B., Zhu, Q., & Luo, J. (2024). Human-AI collaboration by design. Proceedings of the Design Society, 4, 2247–2256. https://doi.org/10.1017/pds.20...

Suh, N. P. (2001). Axiomatic design: Advances and applications. Oxford University Press. http://catdir.loc.gov/catdir/e...

Wang, P., Khinvasara, Y., Creijghton, G. J., Scholing, T., Wang, Y., Zhou, Z., Childs, P. R. N., & Yin, Y. (2025). Enhancing designer creativity through human–AI co-ideation: A co-creation framework for design ideation with custom GPT. AI EDAM, 39, e22. https://doi.org/10.1017/S08900...

Zeng, Y. (2026). Designics: A Science of Intentional Change. Journal of Integrated Design and Process Science, 10920617261450751. https://doi.org/10.1177/109206...