ArguScale: Evidence-Grounded Human-AI Sensemaking Across Concurrent Live Conversations

Abstract

Monitoring concurrent classroom collaborative reasoning discussions is challenging because instructors have to track collaboration-relevant behaviors across multiple ongoing conversations at once. Existing Human-AI conversational sensemaking systems can scale monitoring upward through summaries and visual abstractions, but these representations may distance users from underlying conversational evidence, e.g., relevant audio and transcript segments, needed to forage downward. Through our formative study, we developed ArguScale, an evidence-grounded Human-AI sensemaking system that organizes interactional signals into temporally aligned representations linked to transcript and audio evidence. A temporal signal-density view provides temporal context and supports rapid cross-group comparison, while selective evidence access allows instructors to inspect audio and transcript. We evaluated ArguScale in a mixed-design study with 20 participants. Compared to other conditions, ArguScale improved participants' ability to efficiently and accurately identify collaboration-relevant cues. We further identify strategies participants used to forage for and make sense of conversational evidence under live time constraints.