Exploring Needs and Design Opportunities for Proactive Information Support in In-Person Small-Group Conversations
Shaoze Zhou, Diana Nelly Rivera Rodriguez, Pedro Remior, Joaquin Frangi, Lingyao Li, Renkai Ma, Janet G. Johnson, Christine Lisetti, Chen Chen
TL;DR
The paper addresses the challenge of facilitating effective in-person small-group conversations, where real-time interaction and nonverbal cues complicate engagement. It adopts a qualitative, participatory design approach using two MR-based technology probes (a Wizard-of-Oz prototype and a reactive typing-based system powered by GPT-4o-mini) to explore proactive information support in group settings. Findings indicate that timely, non-intrusive, context-aware MR support can maintain focus and aid participation, but must preserve conversational ownership and be carefully rendered to avoid distraction; key information types include summaries of prior context, overlooked points and clarifications, and nonverbal cue indicators. The study offers design guidelines for adaptive, glanceable MR overlays to augment in-person group conversations and lays groundwork for future proactive AI agents, while acknowledging limitations such as MR headset obstruction and the non-experimental design, and outlining directions for more rigorous future work and broader participant sampling.
Abstract
In-person small-group conversations play a crucial role in everyday life; however, facilitating effective group interaction can be challenging, as the real-time nature demands full attention, offers no opportunity for revision, and requires interpreting non-verbal cues. Using Mixed Reality to provide proactive information support shows promise in helping individuals engage in and contribute to group conversations. We present a preliminary participatory design and qualitative study (N = 10) using focus groups and two technology probes to explore the opportunities of designing proactive information support in in-person small-group conversations. We reveal key design opportunities concerning how to maximize the benefits of proactive information support and how to effectively design such supporting information. Our study is crucial for paving the way toward designing future proactive AI agents to enable the paradigm of augmented in-person small-group conversation experience.