Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation
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| Format: | Preprint |
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2025
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| _version_ | 1866910071368187904 |
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| author | Wan, Yun Kalman, Yoram M |
| author_facet | Wan, Yun Kalman, Yoram M |
| contents | Recent studies suggest that while generative AI (GenAI) can enhance individual creativity, it often reduces the diversity of collective outputs. A well-known example of this homogenization effect is by Doshi and Hauser (2024) who found that GenAI-generated plot ideas improved story writing creativity but led to convergence across writers' outputs. This study extends their experiment, identifying the design choices behind the apparent creativity-diversity trade-off. In Phase 1, we used structured prompting with 10 diverse GenAI personas to generate 300 story plots, and confirmed the plots' diversity using text embedding analysis. In Phase 2, participants wrote stories with or without access to these plots. Results show that diverse GenAI inputs can preserve story diversity compared to a human-only baseline, with some evidence of enhancement in the 1-plot condition. Beyond addressing the diversity component of the trade-off, our findings offer broader insights for human-AI system design. Our findings suggest that the trade-off may emerge from uniform deployment practices rather than from an inherent limitation of GenAI, and that diversity can be intentionally built into AI-mediated collaboration. Our study highlights the risks of over-standardization, the importance of prompt variation, and the value of treating GenAI not as a static tool but as a configurable partner. These insights have important implications for the design of GenAI systems that support, not constrain, collective creativity. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_13868 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation Wan, Yun Kalman, Yoram M Human-Computer Interaction Artificial Intelligence I.2.7, H.5.0, H.4.0 Recent studies suggest that while generative AI (GenAI) can enhance individual creativity, it often reduces the diversity of collective outputs. A well-known example of this homogenization effect is by Doshi and Hauser (2024) who found that GenAI-generated plot ideas improved story writing creativity but led to convergence across writers' outputs. This study extends their experiment, identifying the design choices behind the apparent creativity-diversity trade-off. In Phase 1, we used structured prompting with 10 diverse GenAI personas to generate 300 story plots, and confirmed the plots' diversity using text embedding analysis. In Phase 2, participants wrote stories with or without access to these plots. Results show that diverse GenAI inputs can preserve story diversity compared to a human-only baseline, with some evidence of enhancement in the 1-plot condition. Beyond addressing the diversity component of the trade-off, our findings offer broader insights for human-AI system design. Our findings suggest that the trade-off may emerge from uniform deployment practices rather than from an inherent limitation of GenAI, and that diversity can be intentionally built into AI-mediated collaboration. Our study highlights the risks of over-standardization, the importance of prompt variation, and the value of treating GenAI not as a static tool but as a configurable partner. These insights have important implications for the design of GenAI systems that support, not constrain, collective creativity. |
| title | Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation |
| topic | Human-Computer Interaction Artificial Intelligence I.2.7, H.5.0, H.4.0 |
| url | https://arxiv.org/abs/2504.13868 |