# Augmented Collective Intelligence in Collaborative Ideation: Agenda and Challenges

arXiv: https://arxiv.org/abs/2303.18010 · HTML: https://mlsanity.com/paper/2303.18010/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 20 references that are in the map.
- First posted on arXiv 31 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [Emergent collective intelligence from massive-agent cooperation and competition](https://mlsanity.com/paper/2301.01609/) (2023)

## Abstract (arXiv)

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.