Text-to-image (T2I) models demonstrate strong generative capabilities and are increasingly used in design. However, their support for early exploratory ideation remains limited. Their linear, one-shot interaction paradigm aligns more closely with convergent, refinement-oriented stages of design. To address this gap, we present an interaction paradigm supporting early-stage ideation with T2I models, with a particular focus on novice designers. It introduces a dimension–attribute dictionary to guide prompt construction progressively and employs a dynamic, editable tree structure to help users organize and navigate their design space. Based on this paradigm, we developed a prototyping tool named InspirationGraph, focusing on the product design field. The results from a user study involving 24 participants highlight how this structured exploration approach supports divergent thinking and reduces cognitive load. We also uncover varying ideation patterns among designers and offer actionable insights into how T2I systems can be reimagined to better support the early-stage design.

In early-stage industrial design, teams generate essential but fragile process knowledge—semantic tags, sketches, exploration paths—that is rarely captured or reused but which may be useful at latter design stages, and AI could be used for this purpose. Yet existing AI creativity tools remain outcome-oriented, offering limited support for preserving, tracing, or recombining underlying reasoning. Our formative study (N=6) revealed persistent challenges in team–AI ideation across sessions and collaborators, including semantic–visual fragmentation, context loss, and cross-tool disruption. These insights inspired CoNode, a two-layer system that embeds AI nodes within a shared whiteboard through triplet workflows and augments them with workflow-level consolidation, reuse, and recombination via the CoSense module. We conducted a two-stage evaluation: User Study I (N=12) validates CoNode's foundational interaction paradigm layer, and User Study II (N=30) evaluates its process-oriented knowledge layer. Results show that CoNode significantly improves knowledge consolidation, reuse, and recombination, effectively facilitating the collaborative processes and demonstrating how generative AI can evolve process knowledge across collaborative rounds.