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 -- mimics 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 were more likely to knowingly adopt AI ideas when the task was difficult. Our findings suggest that introducing AI ideas into society may increase collective diversity but not individual creativity.

翻译：接触大语言模型输出的现象正迅速增加。观察人工智能生成的创意将如何影响人类创意？我们开展了一项实验（800余名参与者，来自40多个国家），要求参与者先观看来自ChatGPT或先前实验参与者的创意，随后自主构思创意。实验中，我们操纵了人工智能生成示例的数量（无、低暴露或高暴露），以及示例是否标注为“人工智能”（信息披露）。采用动态实验设计——将实验条件下先前参与者的创意作为后续同条件参与者的刺激素材，以此模拟文化创造中相互依存的过程：创意总是建立在既有创意之上。因此，我们捕捉了将大语言模型“置于文化循环”中产生的复合效应。研究发现：高人工智能暴露（而非低暴露）虽未影响个体创意的创造力，但显著提升了集体创意多样性的平均变化速率与幅度。人工智能使创意变得与众不同，而非更优。信息披露未发现主效应。同时，自我报告具有创造力者受创意来源标识的影响较小，而当任务难度较高时，参与者更倾向主动采用人工智能生成的创意。研究结果表明，将人工智能创意引入社会可能提升集体多样性，但不会增强个体创造力。