Generative AI has democratized content creation, but popular chatbot-based interfaces often prioritize execution, generating fully rendered artifacts right away. This issue can lead to premature convergence and design fixation, where users are being anchored to initial outputs. Recent works have proposed new interfaces to address this issue by supporting exploration, though typically constrained to be semantically close to a user's initial task framing, potentially limiting the creativity of the outcomes. We examine an approach grounded in the Geneplore model of creative cognition and instantiate it in a human-AI co-creation system, HAICo, for creative image generation. HAICo explicitly structures the creative process into two switchable modes: DIVERGENT mode scaffolds the broad exploration of remote conceptual ideas; CONVERGENT mode supports a targeted refinement of selected ideas. Through a within-subjects study (N=24) on a poster image creation task, we demonstrate that HAICo outperforms ChatGPT across multiple dimensions of creativity and usability. Our results highlight the critical need to shift from pure execution-focused chatbots to scaffolded co-creation systems that actively guide exploration and foster the creative process.

翻译：生成式人工智能已实现内容创作的民主化，但流行的聊天机器人界面往往优先执行输出，直接生成完整成品。这种倾向可能导致过早收敛和设计固着——用户被初始输出锚定在狭窄的思维路径上。近期研究通过支持探索功能设计了新型界面，但通常被限制在与用户初始任务语义相近的范围内，可能限制创作成果的创造性。本研究基于创意认知的Geneplore模型提出创新方案，并将其落地为人机协同创作系统HAICo，用于创意图像生成。HAICo明确将创作过程构建为两种可切换模式：发散模式支撑远程概念思维的广泛探索；收敛模式支持选定概念的精准优化。通过海报图像创作任务的被试内实验（N=24），我们证明HAICo在创造力与可用性多个维度上均优于ChatGPT。研究结果凸显了从纯执行型聊天机器人向具有支架式探索引导功能的协同创作系统转型的关键需求。