Personalized Real-time Jargon Support for Online Meetings

TL;DR

This paper introduces ParseJargon, an LLM-powered system that provides real-time personalized jargon explanations during online meetings, significantly improving comprehension and engagement for interdisciplinary communication.

Contribution

The paper presents a novel personalized jargon support system, ParseJargon, and demonstrates its effectiveness through controlled and field studies in real-time online meetings.

Findings

Personalized support improves comprehension and engagement.

General-purpose support can negatively affect engagement.

ParseJargon is usable and valuable in real-world settings.

Abstract

Effective interdisciplinary communication is frequently hindered by domain-specific jargon. To explore the jargon barriers in-depth, we conducted a formative diary study with 16 professionals, revealing critical limitations in current jargon-management strategies during workplace meetings. Based on these insights, we designed ParseJargon, an interactive LLM-powered system providing real-time personalized jargon identification and explanations tailored to users' individual backgrounds. A controlled experiment comparing ParseJargon against baseline (no support) and general-purpose (non-personalized) conditions demonstrated that personalized jargon support significantly enhanced participants' comprehension, engagement, and appreciation of colleagues' work, whereas general-purpose support negatively affected engagement. A follow-up field study validated ParseJargon's usability and practical value in real-time meetings, highlighting both opportunities and limitations for real-world deployment. Our findings contribute insights into designing personalized jargon support tools, with implications for broader interdisciplinary and educational applications.