mlsanityData to 14 Oct 2026

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

arXiv 2502.06430 ↗First posted 10 Feb 2025cs.HC cs.CLcited by 2

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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.

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

18 of 67 references.

References · 18

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  3. 2023Does Writing with Language Models Reduce Content Diversity?cited by 192
  4. 2019Gmail Smart Compose: Real-Time Assisted Writingcited by 98
  5. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 87
  6. 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
  7. 2016Smart Reply: Automated Response Suggestion for Emailcited by 54
  8. 2024The Value, Benefits, and Concerns of Generative AI-Powered Assistance in Writingcited by 37
  9. 2023Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Promptingcited by 36
  10. 2022LaMPost: Design and Evaluation of an AI-assisted Email Writing Prototype for Adults with Dyslexiacited by 27
  11. 2023From Text to Self: Users' Perceptions of Potential of AI on Interpersonal Communication and Selfcited by 18
  12. 2024Rambler: Supporting Writing With Speech via LLM-Assisted Gist Manipulationcited by 18
6 more
  1. 2021An Aligned Rank Transform Procedure for Multifactor Contrast Testscited by 11
  2. 2023"AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AIcited by 11
  3. 2023"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviorscited by 11
  4. 2024The Dearth of the Author in AI-Supported Writingcited by 10
  5. 2023Comparing Sentence-Level Suggestions to Message-Level Suggestions in AI-Mediated Communicationcited by 9
  6. 2024Fragmented Moments, Balanced Choices: How Do People Make Use of Their Waiting Time?cited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshopcited by 7
  2. 2026PersonaMail: Learning and Adapting Personal Communication Preferences for Context-Aware Email Writingcited by 1