TY - CHAP A1 - Irawati, Diah Ayu A1 - Bolukbasi, Elif A1 - Riener, Andreas ED - Sorce, Salvatore ED - Elagroudy, Passant ED - Khamis, Mohamed T1 - Advancing Generative AI Collaboration in Design-to-Code Workflows: Insights from Two Empirical Studies T2 - Proceedings of MUM 2025: The 24th International Conference on Mobile and Ubiquitous Multimedia N2 - For expert users to accept Generative AI (GenAI) as a true collaborative partner, it must move beyond simple task-awareness to an understanding of their workflow’s underlying structural rules. This paper introduces a paradigm for AI collaborators that moves beyond simple task awareness to an understanding of the semantic and hierarchical relationships within a component-based system. We investigate this concept within the context of the design-to-code workflow, where inefficiencies arise from the modification of components within design systems. Through two empirical studies with designers and developers, we found that GenAI output was often rejected because it violated the component hierarchy. Designers required granular and visual control for refinements, whereas developers valued automated setup but required transparent validation of the generated code’s logic. Based on these findings, we contribute design guidelines for achieving Component-Structure Awareness (CSA), with two core principles: the Atomic Recommender, which provides assistance that respects the component hierarchy, and Communication Archetypes, which allow GenAI to adapt its interaction style to the user’s role and the atomic nature of their task. This work provides a new, higher-level concept for designing the next generation of truly collaborative GenAI agents. UR - https://doi.org/10.1145/3771882.3771913 Y1 - 2025 UR - https://doi.org/10.1145/3771882.3771913 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-64848 SN - 979-8-4007-2015-4 SP - 33 EP - 46 PB - ACM CY - New York ER -