AI systems may be better thought of as peers than as tools. This paper explores applications of augmented collective intelligence (ACI) beneficial to collaborative ideation. Design considerations are offered for an experiment that evaluates the performance of hybrid human- AI collectives. The investigation described combines humans and large language models (LLMs) to ideate on increasingly complex topics. A promising real-time collection tool called Polis is examined to facilitate ACI, including case studies from citizen engagement projects in Taiwan and Bowling Green, Kentucky. The authors discuss three challenges to consider when designing an ACI experiment: topic selection, participant selection, and evaluation of results. The paper concludes that researchers should address these challenges to conduct empirical studies of ACI in collaborative ideation.

翻译：人工智能系统可能更适合视为同行而非工具。本文探讨了增强集体智能(ACI)对协作创意有益的应用。提供了设计考虑因素，以评估混合人工和AI集体的表现的实验。所讨论的研究将人类和大型语言模型（LLMs）结合在一起，以对越来越复杂的主题进行创意构思。作者讨论了一个有前途的实时收集工具Polis，以促进ACI，包括来自台湾和肯塔基州鲍灵格林市的公民参与项目的案例研究。本文讨论了设计ACI实验时需要考虑的三个挑战：主题选择、参与者选择和结果评估。本文得出结论，研究人员应解决这些挑战，以进行协作创意中的ACI经验研究。