Homogenizing effect of large language models (LLMs) on creative diversity: An empirical comparison of human and ChatGPT writing

Generative AI systems, especially Large Language Models (LLMs) such as ChatGPT, have recently emerged as significant contributors to creative processes. While LLMs can produce creative content that might be as good as or even better than human-created content, their widespread use risks reducing creative diversity across groups of people. In the present research, we aimed to quantify this homogenizing effect of LLMs on creative diversity, not only at the individual level but also at the collective level. Across three preregistered studies, we analyzed 2,200 college admissions essays. Using a novel measure—the diversity growth rate—we showed that each additional human-written essay contributed more new ideas than did each additional GPT-4 essay. Notably, this difference became more pronounced as more essays were included in the analysis and persisted despite efforts to enhance AI-generated content through both prompt and parameter modifications. Overall, our findings suggest that, despite their potential to enhance individual creativity, the widespread use of LLMs could diminish the collective diversity of creative ideas. • We Introduce diversity growth rate to measure idea diversity added per creative output. • Each additional human-written essay contributes more new ideas than a GPT-generated essay. • Diversity gap widens with more essays, showing greater AI homogenization at scale. • Enhancing GPT's creative diversity through parameter- or prompt-modifications does not mitigate the diversity gap. • Our study suggests widespread LLM use could diminish collective diversity of creative ideas.