Balancing Design Intentionality and Serendipitous Emergence: How Visual Designers Navigate Generative AI Co-creation
Abstract
Generative artificial intelligence (GenAI) tools including Midjourney, Stable Diffusion and Comfy user interface (UI) have evolved from experimental novelties to widely used commercial tools integrated into professional visual design workflows. Yet diffusion models’ probabilistic nature creates persistent tension for designers between preserving deliberate design intent and embracing unplanned emergent visuals. Unlike conventional digital tools enabling predictable direct editing, GenAI often yields unforeseen outputs, swinging practitioners between creative frustration and fortuitous inspiration. To explore how designers resolve this conflict, this qualitative empirical study recruited 10 experienced commercial visual, illustration and UI designers. Drawing on dual evidence: Stimulated recall interviews and process records including prompt logs and draft visuals, alongside thematic analysis, it develops a three-stage spiral model: “Loss of Control - Taming - Symbiosis”. Results: (1) Designers’ cognitive-emotional journey is not a simple linear shift from anxiety to joy, but spiral cognitive reappraisal triggered by chance discoveries. (2) Four operational strategies manage algorithmic variability across a control-divergence spectrum: prompt denoising, regional inpainting, reverse engineering, hybrid collaging. (3) Designer agency persists, shifting from low-level manual manipulation to high-level meta-control over style rules, constraints and selection. This framework explains reshaped human creative agency in human-AI co-creation and informs future AI interface and design education.
References
[1] Anantrasirichai, N., Bull, D. (2022) Artificial intelligence in the creative industries: a review. Artificial Intelligence Review, 55(1), 589-656.
[2] Epstein, Z., Hertzmann, A., Investigators of Human Creativity, Akten, M., Farid, H., Fjeld, J., Smith, A. (2023) Art and the science of generative AI. Science, 380(6650), 1110-1111.
[3] Paananen, V., Oppenlaender, J., Visuri, A. (2024) Using text-to-image generation for architectural design ideation. International Journal of Architectural Computing, 22(3), 458-474.
[4] Tigre Moura, F. (2023) Artificial intelligence, creativity, and intentionality: the need for a paradigm shift. The Journal of Creative Behavior, 57(3), 336-338.
[5] Smets, A. (2023) Designing for serendipity: a means or an end? Journal of Documentation, 79(3), 589-607.
[6] Rafner, J., Beaty, R. E., Kaufman, J. C., Lubart, T., Sherson, J. (2023) Creativity in the age of generative AI. Nature Human Behaviour, 7(11), 1836-1838.
[7] Rezwana, J., Maher, M. L. (2023) Designing creative AI partners with COFI: a framework for modeling interaction in human-AI co-creative systems. ACM Transactions on Computer-Human Interaction, 30(5), 1-28.
[8] Verganti, R., Vendraminelli, L., Iansiti, M. (2020) Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212-227.
[9] Liu, Y. L. E., Huang, Y. M. (2025) Exploring the perceptions and continuance intention of AI-based text-to-image technology in supporting design ideation. International Journal of Human-Computer Interaction, 41(1), 694-706.
[10] Lyu, Y., Shi, M., Zhang, Y., Lin, R. (2023) From image to imagination: Exploring the impact of generative AI on cultural translation in jewelry design. Sustainability, 16(1), 65.
[11] Oppenlaender, J., Linder, R., Silvennoinen, J. (2025) Prompting AI art: an investigation into the creative skill of prompt engineering. International Journal of Human-Computer Interaction, 41(16), 10207-10229.
[12] Kim, J., Maher, M. L. (2023) The effect of AI-based inspiration on human design ideation. International Journal of Design Creativity and Innovation, 11(2), 81-98.
[13] Zhang, W. (2024) Ebb and flow: design fixation and creativity in professional groups. Journal of Engineering Design, 35(3), 263-289.
[14] Hernández-Ramírez, R., Ferreira, J. B. (2024) The future end of design work: a critical overview of managerialism, generative AI, and the nature of knowledge work, and why craft remains relevant. She Ji: The Journal of Design, Economics, and Innovation, 10(4), 414-440.