Content-Driven Local Response: Supporting Sentence-Level and Message-Level Mobile Email Replies With and Without AI

TL;DR

This paper introduces Content-Driven Local Response (CDLR), a novel UI for mobile email replies that allows sentence-level editing and AI suggestions, enhancing flexibility and efficiency in diverse user workflows.

Contribution

It presents a new UI concept that integrates AI suggestions with sentence-level editing, enabling dynamic user control over AI involvement in email responses.

Findings

CDLR supports flexible workflows with varying AI involvement.

Users experience reduced typing and errors with CDLR.

CDLR outperforms manual typing and full AI reply generation in user study.

Abstract

Mobile emailing demands efficiency in diverse situations, which motivates the use of AI. However, generated text does not always reflect how people want to respond. This challenges users with AI involvement tradeoffs not yet considered in email UIs. We address this with a new UI concept called Content-Driven Local Response (CDLR), inspired by microtasking. This allows users to insert responses into the email by selecting sentences, which additionally serves to guide AI suggestions. The concept supports combining AI for local suggestions and message-level improvements. Our user study (N=126) compared CDLR with manual typing and full reply generation. We found that CDLR supports flexible workflows with varying degrees of AI involvement, while retaining the benefits of reduced typing and errors. This work contributes a new approach to integrating AI capabilities: By redesigning the UI for workflows with and without AI, we can empower users to dynamically adjust AI involvement.