Agent-Based Dynamic Collaboration Support in a Smart Office Space (2020)

Abstract: For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments. A series of experimental studies has provided an empirical foundation for the design of chat-based conversational agents that significantly improve learning over no-support control conditions and static-support control conditions. In this demo, we expand upon this foundation, bringing conversational agents to structure group interaction into physical spaces, with the specific goal of facilitating collaboration and learning in workplace scenarios.

## introduction ai-enhanced human learning is a broad area of research with a history at least 50 years long @xcite , with carbonell's scholar system being among the earliest systems @xcite . however, while great strides to introduce technologies to enhance both individual and collaborative learning have been made in relatively structured environments such as the lab and the classroom over the decades of research since that time, less progress has been made in more unstructured environments such as the workplace, where the stakes are far higher and social and political pressures play a more substantial role. this demo presents an apparatus for support of collaboration and learning in workplace scenarios using a virtual human facilitator interacting face-to-face through speech and gesture. large scale quantitative research, including experimental studies and carefully controlled quasiexperimental corpus studies, are the basis for learning generalizable principles (i.e., causal models) that underlie data-driven design of effective aienabled systems that support human learning. in recent decades, process data such as click logs, discourse data, biometric sensors, and images are used to understand the process of human learning more deeply @xcite . models trained over this process data are also used to enable realtime monitoring and support of learning processes even as groups learn through multi-party discussion @xcite . thus, the ability to draw causal inferences to motivate effective interventions and the ability to trigger personalized, just-in-time support go hand-in-hand towards development of ai-enhanced learning experiences. much of the prior research on workplace learning is qualitative work, which focuses on deep understanding of individual contexts rather than producing generalizable principles through intervention studies. thus, there is a dearth of empirical research that can rigorously motivate design of effective ai-enabled interventions to support workplace learning, and the data from such research is unable to support model-enabled real-time sensing technology that would facilitate just-in-time support for learning in the workplace. in response, we have constructed a "smart office space" in which to run lab studies with simulated work conditions in order to discover causal mechanisms that can form the foundation for design. ## demo session the video presentation of the demo for the online demo session will display scenarios in which groups of individuals work together on a task, with the vht providing guidance for task structuring and collaborative work processes. what makes the demo unique among other applications of in person multi-party dialogue is the use of the virtual human as a group learning facilitator, enabled through the bazaar architecture.