In the information workplace of the future, teamwork will become increasingly critical and teamwork itself will be redefined. Teams will need to develop better skills in handling complex problems as routine work will be increasingly delegated to artificial intelligence (AI) technologies such as personal digital assistants. Teams will need to rapidly adapt to fluid membership and changing work structures with the growing gig economy, and as new workers enter the workforce bringing new cultural practices. Individuals will need to be able to perform effectively in heterogeneous teams as the workforce becomes more diverse and as globalization increases. The future of teamwork will require integration of technological advances to facilitate team performance, yet we are largely relying on tools and techniques from the 20th century for team facilitation. This project will develop and validate an intelligent (AI-based) team facilitator for information work utilizing sensing and dynamic intervention to promote better team coordination, higher performance, and ultimately lower worker burnout. The intelligent team facilitator will serve as a blueprint for a broad set of domains beyond information work, including medical care teams, control room settings, crisis management, and manufacturing, where team skills will be needed for interacting with AI, robots, and new technologies. The facilitator can also be used for training underrepresented groups to succeed in the workforce, a national priority.
The present project utilizes sensor technologies for tracking team behavior in information workplaces in addition to traditional methods of studying teams using observations and self- reports. Longitudinal precision tracking of teams in situ with a suite of sensors can provide objective measures, can scale, and will enable a deep understanding of how teams respond to changing contexts, how teams form and integrate new members, and how they develop rhythms of teamwork. This project examines team diversity broadly, considering demographics, attitudes, circadian rhythms and personal responsibilities. The first aim of this project is to develop models of critical team states and processes (e.g., team cohesion, team coordination, team mood/affect), based on unobtrusive, continual, longitudinal sensing of physiology, behavior, and communication in a real-world context along with measures of individual differences to understand factors that lead to team effectiveness. This project will use risk mitigation strategies to safeguard privacy and security of data. The second aim of this project is to use those insights to develop an intelligent (AI-based) team facilitator. Performance of teams who use the intelligent team facilitator will be experimentally compared against matched controls in a longitudinal in situ study. The results will contribute to a new understanding on how 21st century teams can manage complexity, how team heterogeneity can lead to team effectiveness, and will identify successful strategies for team adaptability.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.