{"ID":2882999,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10239","arxiv_id":"2508.10239","title":"Personalized Real-time Jargon Support for Online Meetings","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.","short_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 d...","url_abs":"https://arxiv.org/abs/2508.10239","url_pdf":"https://arxiv.org/pdf/2508.10239v2","authors":"[\"Yifan Song\",\"Wing Yee Au\",\"Hon Yung Wong\",\"Brian P. Bailey\",\"Tal August\"]","published":"2025-08-13T23:42:12Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}