Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions

要旨

Replying to formal emails is time-consuming and cognitively demanding, as it requires crafting polite phrasing and providing an adequate response to the sender's demands. Although systems with Large Language Models (LLMs) were designed to simplify the email replying process, users still need to provide detailed prompts to obtain the expected output. Therefore, we propose and evaluate an LLM-powered question-and-answer (QA)-based approach for users to reply to emails by answering a set of simple and short questions generated from the incoming email. We developed a prototype system, ResQ, and conducted controlled and field experiments with 12 and 8 participants. Our results demonstrated that the QA-based approach improves the efficiency of replying to emails and reduces workload while maintaining email quality, compared to a conventional prompt-based approach that requires users to craft appropriate prompts to obtain email drafts. We discuss how the QA-based approach influences the email reply process and interpersonal relationship dynamics, as well as the opportunities and challenges associated with using a QA-based approach in AI-mediated communication.

受賞
Honorable Mention
著者
Yusuke Miura
Waseda University, Tokyo, Japan
Chi-Lan Yang
The University of Tokyo, Tokyo, Japan
Masaki Kuribayashi
Waseda University, Tokyo, Japan
Keigo Matsumoto
The University of Tokyo, Tokyo, Japan
Hideaki Kuzuoka
The University of Tokyo, Bunkyo-ku, Tokyo, Japan
Shigeo Morishima
Waseda Research Institute for Science and Engineering, Tokyo, Japan
DOI

10.1145/3706598.3714016

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714016

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Conversations with AI

G414+G415
7 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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