Dynamics of Collective Diversity in Human–AI Co-Creation for Creative Tasks

Generative AI systems are increasingly deployed at scale to support human users in creative tasks across a variety of domains. However, as more users adopt the same AI agent (e.g., ChatGPT), the resulting artifacts may become increasingly homogeneous, leading to a collapse in collective diversity. Prior work has documented this risk, but lacks a systematic framework for understanding how collective diversity changes as AI adoption scales across co-creation settings. In this work, we conduct a large-scale crowdsourcing study to understand and model the dynamics of collective diversity, quality, and effort in human–AI co-creation for three creative tasks. We find that current AI agents used in co-creation settings can improve artifact quality and reduce effort, but that these gains come at the cost of declining collective diversity as adoption increases. We further show that more structured and fine-grained human-AI interaction, along with more diverse agent designs, can mitigate this collapse. Finally, we derive predictive models that capture how collective diversity scales across co-creation settings, providing valuable insights for the design of AI agents that aim to preserve collective diversity.