Exposure to large language model output is rapidly increasing. How will seeing AI-generated ideas affect human ideas? We conducted an experiment (800+ participants, 40+ countries) where participants viewed creative ideas that were from ChatGPT or prior experimental participants and then brainstormed their own idea. We varied the number of AI-generated examples (none, low, or high exposure) and if the examples were labeled as 'AI' (disclosure). Our dynamic experiment design -- ideas from prior participants in an experimental condition are used as stimuli for future participants in the same experimental condition -- speaks to the interdependent process of cultural creation: creative ideas are built upon prior ideas. Hence, we capture the compounding effects of having LLMs 'in the culture loop'. We find that high AI exposure (but not low AI exposure) did not affect the creativity of individual ideas but did increase the average amount and rate of change of collective idea diversity. AI made ideas different, not better. There were no main effects of disclosure. We also found that self-reported creative people were less influenced by knowing an idea was from AI and that participants may knowingly adopt AI ideas when the task is difficult. Our findings suggest that introducing AI ideas may increase collective diversity but not individual creativity.

翻译：大型语言模型输出的曝光度正在迅速增加。接触AI生成的创意将如何影响人类创意？我们开展了一项实验（800多名参与者，覆盖40余个国家），让参与者先查看来自ChatGPT或先前实验参与者的创意示例，随后进行头脑风暴并提出自己的创意。我们设置了不同的AI生成示例数量（无接触、低接触或高接触）以及是否标注示例来源为“AI”（信息披露）。本实验采用动态设计——将同一实验条件下先前参与者的创意作为后续参与者的刺激材料——这反映了文化创造的相互依存过程：创意总是建立在先前创意的基础之上。因此，我们捕捉到了将大型语言模型置于“文化循环”中所产生的复合效应。研究发现：高AI接触（非低接触）虽未影响个体创意的创造性，但显著提升了集体创意多样性的平均水平和变化速率。AI使创意变得不同，而非更优。信息披露未产生主要影响。我们还发现：自评具有创造力的人群较少受“创意来自AI”这一认知的影响；当任务难度较高时，参与者可能会有意识地采纳AI创意。研究结果表明：引入AI创意可能提升集体多样性，但不会增强个体创造性。