For nearly two decades, conversational agents have been used to structure group interactions in online chat-based environments. More recently, this form of dynamic support for collaborative learning has been extended to physical spaces using a combination of multimodal sensing technologies and instrumentation installed within a physical space. This demo extends the reach of dynamic support for collaboration still further through an application of what has recently been termed on-device machine learning, which enables a portable form of multimodal detection to trigger real-time responses.
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Agent-Based Dynamic Collaboration Support in a Smart Office Space
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.
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- NSF-PAR ID:
- 10174662
- Date Published:
- Journal Name:
- Proceedings of the SIGdial 2020 Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning. The institute's research has three complementary strands. First, the institute creates narrative‐centered learning environments that generate interactive story‐based problem scenarios to elicit rich communication, encourage coordination, and spark collaborative creativity. Second, the institute creates virtual embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support student learning. Embodied conversational agents are driven by advances in natural language understanding, natural language generation, and computer vision. Third, the institute is creating an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the institute's activities is a strong focus on ethics, with an emphasis on creating AI‐augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy. The institute emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM. The institute brings together a multistate network of universities, diverse K‐12 school systems, science museums, and nonprofit partners. Key to all of these endeavors is an emphasis on diversity, equity, and inclusion.
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