Using Generative AI Personas Increases Collective Diversity in Human Ideation
This addresses the problem of reduced creative diversity in human-AI collaboration for writers and ideators, offering a practical solution through AI personas.
This study tackled the tradeoff between generative AI's contribution to creative outcomes and decreased diversity by using ten unique GenAI personas to generate diverse plot ideas, finding that this approach eliminated the diversity reduction observed in prior work and even increased descriptive and emotional language diversity in human-written stories.
This study challenges the widely-reported tradeoff between generative AI's (GenAI) contribution to creative outcomes and decreased diversity of these outcomes. We modified the design of such a study, by Doshi and Hauser (2024), in which participants wrote short stories either aided or unaided by GenAI plot ideas[1]. In the modified study, plot ideas were generated through ten unique GenAI "personas" with diverse traits (e.g. cultural backgrounds, thinking styles, genre preferences), creating a pool of 300 story plots. While plot ideas from any individual persona showed high similarity (average cosine similarity of 0.92), ideas across different personas exhibited substantial variation (average similarity of 0.20). When human participants wrote stories based on these diverse plot ideas, their collective outputs maintained the same level of diversity as stories written without GenAI assistance, effectively eliminating the diversity reduction observed in [1]. Traditional text analytics further revealed that GenAI-assisted stories featured greater diversity in descriptive and emotional language compared to purely human-generated stories without GenAI assistance. Our findings demonstrate that introducing diversity at the AI input stage through distinct personas can preserve and potentially enhance the collective diversity of human creative outputs when collaborating with GenAI.