AI增强的Brainwriting：研究LLM在群体创意构思中的应用
AI-Augmented Brainwriting: Investigating the use of LLMs in group ideation
AI总结：
本文提出群体-AI Brainwriting框架并设计LLM评估引擎，实验表明LLM可增强群体创意的发散生成、收敛评估及最终成果。
摘要：
大型语言模型（LLM）等生成式AI技术的日益普及，对创造性工作具有重大影响。本文探讨了将LLM融入创作过程的两个方面——创意生成的发散阶段，以及创意评估与选择的收敛阶段。我们设计了一个协作式群体-AI Brainwriting创意构思框架，将LLM作为增强组件纳入群体创意过程，并评估了创意生成过程及其产生的解空间。为评估在创意评估过程中使用LLM的潜力，我们设计了一个评估引擎，并将其与三名专家和六名新手评估者给出的创意评分进行比较。研究结果表明，在Brainwriting中整合LLM可以改善创意构思过程及其成果。我们还提供证据表明LLM能够支持创意评估。最后，我们讨论了研究结果对HCI教育与实践的启示。
英文摘要：
The growing availability of generative AI technologies such as large language models (LLMs) has significant implications for creative work. This paper explores twofold aspects of integrating LLMs into the creative process - the divergence stage of idea generation, and the convergence stage of evaluation and selection of ideas. We devised a collaborative group-AI Brainwriting ideation framework, which incorporated an LLM as an enhancement into the group ideation process, and evaluated the idea generation process and the resulted solution space. To assess the potential of using LLMs in the idea evaluation process, we design an evaluation engine and compared it to idea ratings assigned by three expert and six novice evaluators. Our findings suggest that integrating LLM in Brainwriting could enhance both the ideation process and its outcome. We also provide evidence that LLMs can support idea evaluation. We conclude by discussing implications for HCI education and practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
