利用丰富性：人类与生成式人工智能协作的生成视角
Harnessing Abundance: A Generativity Perspective on Human-GenAI Collaboration
- The Open University of Israel（以色列开放大学）
- University of Houston Downtown（休斯顿大学市中心分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对人类与GenAI协作的矛盾结果，提出生成适配性机制与概念框架，解释丰富性的作用并为设计转化丰富性的工作流程提供启示。
AI中文摘要：
人类与生成式人工智能（GenAI）协作的研究结果相互矛盾：GenAI可提升创造力，但会降低集体多样性，且益处因技能水平而异。我们不将这些视为矛盾，而是认为它们反映了GenAI的核心特征——丰富性。GenAI使想法、草稿和重组内容变得丰富，可能扩大假设空间并揭示未预料到的可能性。然而，仅丰富性本身无法确保更好的结果。我们提出生成适配性（generative fit）作为统一机制，解释何时丰富性会产生富有成效的创造力，何时会产生反效果。基于生成性理论，生成适配性捕捉系统的生成潜力与社区的生成能力的互补程度。我们为人类与GenAI协作场景开发了概念框架，其中参与者拥有共同目标、相互依赖且必须整合多样化贡献。通过将丰富性映射到集体创造力的认知、社会和组织因素，我们解释了明显的权衡，并为设计将丰富性转化为有价值的创造性成果的工作流程提供了可行的启示。
英文摘要：
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.
