GenAI的丰富性既激发创意又降低多样性，关键在于人机协作的适配度。
Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration
- 用生成适配性理论解释人机协作中创意产出的差异
- 丰裕带来新可能，但适配不佳反致效率下降
- 适合想优化人机共创流程的研究者和设计师
关于人与生成式AI协作的研究结论矛盾：GenAI虽能提升创造力，却可能削弱集体多样性，且对不同技能水平者益处不均。我们提出，这并非矛盾，而是GenAI核心特性——丰裕性的体现。GenAI使想法、草稿与重组方案大量涌现，可能拓展假设空间并揭示意外可能性。然而，丰裕本身并不保证更好结果。我们提出‘生成适配性’作为统一机制，解释何时丰裕促进创造性成果，何时导致失效。基于生成性理论，生成适配性衡量系统生成潜力与群体创造能力的匹配程度。我们构建了人机协作场景的概念框架，其中参与者共享目标、相互依赖并需整合多元贡献。通过将丰裕映射至认知、社会与组织层面的集体创造力因素，我们解释了看似矛盾的现象，并为设计能将丰裕转化为有价值创意成果的工作流提供可操作建议。
原文摘要 · Abstract (English)
Research on human-GenAI collaboration yields conflicting findings: GenAI can enhance creativity yet reduce collective diversity, with uneven benefits across skill levels. Rather than treating these as contradictions, we argue they reflect a core feature of GenAI: abundance. GenAI makes ideas, drafts, and recombinations plentiful, potentially expanding the hypothesis space and surfacing unanticipated possibilities. However, abundance alone doesn't ensure better outcomes. We propose generative fit as a unifying mechanism explaining when abundance yields productive creativity and when it backfires. Drawing on Generativity Theory, generative fit captures how well a system's generative potential complements a community's generative capacities. We develop a conceptual framework for collaborative human-GenAI settings where participants share goals, depend on one another, and must integrate diverse contributions. By mapping abundance to cognitive, social, and organizational factors of collective creativity, we explain apparent tradeoffs and offer actionable implications for designing workflows that convert abundance into valued creative outcomes.
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