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

TL;DR

This paper introduces ResQ, an LLM-powered question-and-answer system that simplifies formal email replies by using short questions, improving efficiency and reducing workload without compromising email quality.

Contribution

The paper presents a novel QA-based approach for email replying, demonstrating its effectiveness through a prototype and experiments, advancing AI-mediated communication tools.

Findings

QA-based approach improves reply efficiency

Reduces user workload in email composition

Maintains email quality comparable to traditional methods

Abstract

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 proposed and evaluated 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.