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

Titelangaben

Zindulka, Tim ; Goller, Sven ; Lehmann, Florian ; Buschek, Daniel:
Content-Driven Local Response : Supporting Sentence-Level and Message-Level Mobile Email Replies With and Without AI.
In: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. - New York, NY, USA : Association for Computing Machinery , 2025
ISBN 979-8-4007-1394-1
DOI: https://doi.org/10.1145/3706598.3713890

Volltext

Link zum Volltext (externe URL):

Angaben zu Projekten

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.