Augmented Collective Intelligence in Collaborative Ideation: Agenda and Challenges
This work addresses challenges in designing experiments for augmented collective intelligence in collaborative ideation, which is incremental as it builds on existing concepts without presenting new empirical results.
The paper tackles the problem of enhancing collaborative ideation by integrating AI as peers in hybrid human-AI collectives, using tools like Polis and case studies from Taiwan and Kentucky, and concludes with design considerations for future experiments.
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.