Understanding and Supporting Formal Email Exchange by Answering AI-Generated Questions
This addresses the challenge of reducing user effort in AI-mediated email communication, though it is incremental as it builds on existing LLM systems.
The paper tackled the problem of time-consuming and cognitively demanding formal email replies by proposing an LLM-powered QA-based approach, where users answer simple questions generated from incoming emails. Results from experiments with 20 participants showed that this method improves efficiency, reduces workload, and maintains email quality compared to conventional prompt-based approaches.
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.