用10种不同AI人格生成创意，避免人机协作时想法趋同。
Diverse AI Personas Can Mitigate the Homogenization Effect in Human-AI Collaborative Ideation
- 设计10种不同风格的AI角色生成故事梗概，提升创意多样性。
- 相比人类独立创作，引入多样AI输入可保持故事输出差异性。
- 适合关注人机协作创新、反对工具标准化的研究者和设计师。
近期研究表明，虽然生成式AI（GenAI）能提升个人创造力，但常导致集体产出趋于同质化。例如，Doshi和Hauser（2024）发现GenAI生成的故事构思虽提升了写作创造力，却使不同作者的作品趋于一致。本研究扩展该实验，揭示这一创造力-多样性权衡背后的可设计因素。第一阶段，采用结构化提示，利用10种不同风格的GenAI人格生成300个故事梗概，并通过文本嵌入分析验证其多样性。第二阶段，参与者在有或无这些梗概的情况下进行创作。结果显示，使用多样化的GenAI输入可有效维持故事多样性，相较于人类仅靠自身创作的基线水平；在仅提供一个梗概的条件下，还存在一定的创造力提升迹象。研究进一步表明，该权衡并非源于GenAI的固有限制，而是源于统一部署方式所致。因此，可通过主动设计多样性来实现人机协作中的创造性突破。研究强调过度标准化的风险，突出提示词变化的重要性，并主张将GenAI视为可配置的合作伙伴而非静态工具。这些发现对支持而非限制集体创造力的GenAI系统设计具有重要启示。
原文摘要 · Abstract (English)
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.
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