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.

Thom Golden, senior vice president of data science at Capture Higher Ed, took his family to Chaney's Dairy Barn in Bowling Green, Ky. The experience left him with more than just some premium homemade ice cream and an afternoon of fun in the country. He was able to see firsthand how Artificial Intelligence (AI) can open up new possibilities for a family business. "I've never been a dairy farmer, but I know enough to understand that it's exhausting," Thom says during a recent episode of The Weightlist, Capture's podcast that regularly discusses the areas between data, new technologies and enrollment management. He hosts the podcast with Brad Weiner, director of data science at Capture.

Conditional preference networks (CP-nets) are a commonly studied compact formalism for modeling preferences. To study the properties of CP-nets or the performance of CP-net algorithms on average, one needs to generate CP-nets in an equiprobable manner. We discuss common problems with naive generation, including sampling bias, which invalidates the base assumptions of many statistical tests and can undermine the results of an experimental study. We provide a novel algorithm for provably generating acyclic CP-nets uniformly at random. Our method is computationally efficient and allows for multi-valued domains and arbitrary bounds on the indegree in the dependency graph.