黑客松如何促进创造力？迈向大规模的创造力人工智能协作评估
How Do Hackathons Foster Creativity? Towards AI Collaborative Evaluation of Creativity at Scale
- Aalborg University（奥尔堡大学）
- Aarhus University（奥胡斯大学）
- Eindhoven University of Technology（埃因霍温理工大学）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文通过对193,353个黑客松项目的大规模计算分析，探讨了参与者特征等因素对创造力的影响，并提出利用LLMs增强创造力评估的方法与挑战。
AI中文摘要：
黑客松已成为加速创意和原型开发的流行协作活动。已有多个案例研究展示了工业、教育和研究等领域的创造性成果。然而，目前缺乏关于黑客松中创造力的大规模研究，这类研究能够推进关于黑客松形式如何导致创造性成果的理论。我们对193,353个黑客松项目进行了计算分析。通过有用性和新颖性将创造力操作化，我们将数据集精炼至10,363个项目，使我们能够分析参与者特征、协作模式和黑客松设置如何影响创意项目的开发。本文的贡献有两方面：我们确定了组织者在黑客松中促进创造力的方法。我们还探索了使用大型语言模型（LLMs）来增强对创造性成果的评估，并讨论了这样做的挑战和机遇，这对整个创造力研究具有启示意义。
英文摘要：
Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lead to creative outcomes. We conducted a computational analysis of 193,353 hackathon projects. By operationalizing creativity through usefulness and novelty, we refined our dataset to 10,363 projects, allowing us to analyze how participant characteristics, collaboration patterns, and hackathon setups influence the development of creative projects. The contribution of our paper is twofold: We identified means for organizers to foster creativity in hackathons. We also explore the use of large language models (LLMs) to augment the evaluation of creative outcomes and discuss challenges and opportunities of doing this, which has implications for creativity research at large.
