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

arXiv: https://arxiv.org/abs/2502.06430 · HTML: https://mlsanity.com/paper/2502.06430/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [Gmail Smart Compose: Real-Time Assisted Writing](https://mlsanity.com/paper/1906.00080/) (2019)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- [Smart Reply: Automated Response Suggestion for Email](https://mlsanity.com/paper/1606.04870/) (2016)
- [The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writing](https://mlsanity.com/paper/2403.12004/) (2024)
- [Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting](https://mlsanity.com/paper/2303.03199/) (2023)
- [LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexia](https://mlsanity.com/paper/2207.02308/) (2022)
- [From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Self](https://mlsanity.com/paper/2310.03976/) (2023)
- [Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulation](https://mlsanity.com/paper/2401.10838/) (2024)
- [An Aligned Rank Transform Procedure for Multifactor Contrast Tests](https://mlsanity.com/paper/2102.11824/) (2021)
- ["AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI](https://mlsanity.com/paper/2309.16606/) (2023)
- ["One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors](https://mlsanity.com/paper/2310.15398/) (2023)
- [The Dearth of the Author in AI-Supported Writing](https://mlsanity.com/paper/2404.10289/) (2024)
- [Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communication](https://mlsanity.com/paper/2302.13382/) (2023)
- [Fragmented Moments, Balanced Choices: How Do People Make Use of Their Waiting Time?](https://mlsanity.com/paper/2404.02880/) (2024)

## Built on by (most cited first, 2 of 2)

- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [PersonaMail: Learning and Adapting Personal Communication Preferences for Context-Aware Email Writing](https://mlsanity.com/paper/2602.17340/) (2026)

## Abstract (arXiv)

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.