In situations the following novel and unique contributions in a involving hybrid human-AI teams, although there single system: is an increasing desire for AIs that act as collaborators Real-time tracking of participant speech, actions, with humans, modern AI systems struggle to gesture, and gaze when engaging in a account for such mental states in their human interlocutors shared task; (Sap et al., 2022; Ullman, 2023) that might expose shared or conflicting beliefs, and thus predict On-the-fly interpretation and integration of and explain in-context behavior (Premack and multimodal signals to provide a complete Woodruff, 1978). Additionally, in realistic scenarios scene representation for inference; such as collaborative problem solving (Nelson, Simultaneous detection of asserted propositional 2013), beliefs are communicated not just through content and epistemic positioning to language, but through multimodal signals including infer task-relevant information for which evidence gestures, tone of voice, and interaction with has been raised, or which the group has the physical environment (VanderHoeven et al., agreed is factual; 2024b). Since one of the critical capabilities that makes human-human collaboration so successful is A modular, extensible architecture adaptable the human ability to interpret multiple coordinated to new tasks and scenarios.

Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics. In multi-party working group environments, multimodal analytics is crucial for identifying non-verbal interactions of group members. In conjunction with their verbal participation, this creates an holistic understanding of collaboration and engagement that provides necessary context for the AI Partner. In this demo, we illustrate our present capabilities at detecting and tracking nonverbal behavior in student task-oriented interactions in the classroom, and the implications for tracking common ground and engagement.