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Immediate or Reflective?: Effects of Real-timeFeedback on Group Discussions over Videochat
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Having a group discussion with the members holding conflicting viewpoints is difficult. It is especially challenging for machine-mediated discussions in which the subtle social cues are hard to notice. We present a fully automated videochat framework that can automatically analyze audio-video data of the participants and provide real-time feedback on participation, interruption, volume, and facial emotion. In a heated discourse, these features are especially aligned with the undesired characteristics of dominating the conversation without taking turns, interrupting constantly, raising voice, and expressing negative emotion. We conduct a treatment-control user study with 40 participants having 20 sessions in total. We analyze the immediate and the reflective effects of real-time feedback on participants. Our findings show that while real-time feedback can make the ongoing discussion significantly less spontaneous, its effects propagate to successive sessions bringing significantly more expressiveness to the team. Our explorations with instant and propagated impacts of real-time feedback can be useful for developing design strategies for various collaborative environments.
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Cited by 1 Pith paper
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Teaming in the AI Era: AI-Augmented Frameworks for Forming, Simulating, and Optimizing Human Teams
A PhD proposal combining bandit algorithms, LLM feedback, and LLM simulation for team management, with early pilot results that lack statistical validation.
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