Large language models (LLMs) show strong potential to support creative tasks, but the role of the interface design is poorly understood. In particular, the effect of different modes of collaboration between humans and LLMs on co-creation outcomes is unclear. To test this, we conducted a randomized controlled experiment ($N = 486$) comparing: (a) two variants of reflective, human-led modes in which the LLM elicits elaboration through suggestions or questions, against (b) a proactive, model-led mode in which the LLM independently rewrites ideas. By assessing the effects on idea quality, diversity, and perceived ownership, we found that the model-led mode substantially improved idea quality but reduced idea diversity and users' perceived idea ownership. The reflective, human-led mode also improved idea quality, yet while preserving diversity and ownership. We independently validated the findings in a different context ($N = 640$). Our findings highlight the importance of designing interactions with generative AI systems as reflective thought partners that complement human strengths and augment creative processes.

翻译：大型语言模型（LLM）在支持创造性任务方面展现出巨大潜力，但其交互界面的设计作用尚未得到充分理解。特别是，人类与LLM之间不同的协作模式对共创成果的影响尚不明确。为验证此问题，我们开展了一项随机对照实验（$N = 486$），比较了以下两种模式：（a）两种反思性、人类主导的协作模式——LLM通过建议或提问引导人类深入阐述创意；（b）主动性、模型主导的模式——LLM独立重写创意方案。通过评估其对创意质量、多样性及用户感知所有权的影响，我们发现：模型主导模式显著提升了创意质量，但降低了创意多样性及用户对创意的感知所有权；而反思性、人类主导模式在提升创意质量的同时，保持了创意多样性和所有权。我们在另一独立场景中（$N = 640$）验证了上述结论。本研究结果凸显了将生成式AI系统设计为反思性思维伙伴的重要性，此类设计能有效补充人类优势并增强创造性过程。