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：AI系统更应被视为协作伙伴而非工具。本文探讨了增强型集体智能（ACI）在协作构思中的有益应用，并提出了评估混合人机集体性能的实验设计考量。该研究结合人类与大型语言模型（LLMs），针对日益复杂的话题进行构思。研究考察了一种名为Polis的实时收集工具以促进ACI，包括台湾和肯塔基州鲍灵格林市民参与项目的案例研究。作者讨论了设计ACI实验时需面对的三大挑战：主题选择、参与者选择与结果评估。论文指出，研究者需应对这些挑战，方能开展ACI在协作构思中的实证研究。