如何和为谁使用生成性AI影响创造力:一个实地实验.
Shuhua Sun1, Zhuyi Angelina Li2, Maw-Der Foo3
1A. B. Freeman School of Business, Tulane University.
The Journal of applied psychology
|June 16, 2025
概括
大型语言模型 (LLM) 的帮助通过提供认知资源来提高工作场所的创造力. 这种效应对使用元认知策略来利用LLM工具来增强解决问题和创新的员工来说最为强烈.
科学领域:
- 组织心理学 组织心理学
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 人工智能,特别是大型语言模型 (LLM) 的整合到专业环境中正在迅速增加.
- 了解LLM辅助对员工创造力和潜在心理机制的影响对于优化工作场所创新至关重要.
- 超认知策略在个人如何适应和利用新的认知工具方面发挥着关键作用.
研究的目的:
- 开发一个理论框架,解释LLM辅助如何以及为谁影响工作场所的创造力.
- 调查认知就业资源在LLM-创造力关系中的调解作用.
- 检查元认知策略对LLM创造力援助有效性的缓和作用.
主要方法:
- 在LLM辅助和员工创造力方面开发了一个理论视角.
- 在一家技术咨询公司的员工身上进行了实地实验.
- 参与者被随机分配到接受LLM辅助或不接受LLM辅助,由监督员和外部评估员评估创造力.
主要成果:
- 通过提供认知就业资源,LLM协助显著提高了员工的创造力.
- 与低水平员工相比,具有高水平元认知策略的员工在使用LLM协助时表现出更大的创造力增加.
- 在不同的评估方法中,LLM协助对创造力的积极影响是强大的.
结论:
- 法律法学士辅助作为一个有价值的认知工作资源,增强员工的创造力.
- 超认知策略对于有效利用LLM工具来释放创造性潜力至关重要.
- 这些发现为拟议的理论模型提供了经验支持,突出了技术,个人差异和工作场所创造力之间的相互作用.
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