Guiding Generalized Team Problem-Solving Through a Collective Intelligence-Based Artificial Intelligence Facilitator

Abstract The use of artificial intelligence (AI) to guide team dynamics has the potential to transform collaborative problem-solving processes. Existing approaches to doing so are trained on prior problem-specific data, limiting them to problems that have already been solved. This research aims to extend AI-based approaches to novel situations by eliminating the need for prior data. This is accomplished by focusing on team communication and collective intelligence (CI) rather than problem-specific strategies. CI is a team's general ability to work well across various tasks and is more predictive of team performance than individual intelligence. This work introduces an AI facilitator that monitors CI attributes—collective attention, equal participation, and consistent communication—in real time and intervenes as necessary to guide teams toward better collaboration and overall performance. Two human subjects studies are performed on teams working together to design a mechanical system to test the AI facilitator. The studies vary in the structure of the problem-solving environment (virtual or colocated) and communication modality (text-only or verbal). The studies' findings support that the AI facilitator leads teams to better performance than without using the facilitator, and equivalent performance when compared to a human facilitator. This contribution to the field of AI in team management is notable because of the elimination of the need for prior data, making it applicable to novel situations. This work lays the groundwork for a new approach to potentially transform the future of teamwork.