@inproceedings{kim-etal-2025-voice,
title = "Voice Interaction With Conversational {AI} Could Facilitate Thoughtful Reflection and Substantive Revision in Writing",
author = "Kim, Jiho and
Laban, Philippe and
Chen, Xiang and
Arnold, Kenneth C.",
editor = "Padmakumar, Vishakh and
Gero, Katy and
Wambsganss, Thiemo and
Sterman, Sarah and
Huang, Ting-Hao and
Zhou, David and
Chung, John",
booktitle = "Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico, US",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.in2writing-1.7/",
pages = "69--73",
ISBN = "979-8-89176-239-8",
abstract = "Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers' engagement with higher-order concerns, facilitating iterative refinement of their reflections, and reduce cognitive load compared to text-based interactions. To investigate these effects, we propose a formative study exploring how text vs. voice input influence writers' reflection and subsequent revisions. Findings from this study will inform the design of intelligent and interactive writing tools, offering insights into how voice-based interactions with LLM-powered conversational agents can support reflection and revision."
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<abstract>Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers’ engagement with higher-order concerns, facilitating iterative refinement of their reflections, and reduce cognitive load compared to text-based interactions. To investigate these effects, we propose a formative study exploring how text vs. voice input influence writers’ reflection and subsequent revisions. Findings from this study will inform the design of intelligent and interactive writing tools, offering insights into how voice-based interactions with LLM-powered conversational agents can support reflection and revision.</abstract>
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%0 Conference Proceedings
%T Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing
%A Kim, Jiho
%A Laban, Philippe
%A Chen, Xiang
%A Arnold, Kenneth C.
%Y Padmakumar, Vishakh
%Y Gero, Katy
%Y Wambsganss, Thiemo
%Y Sterman, Sarah
%Y Huang, Ting-Hao
%Y Zhou, David
%Y Chung, John
%S Proceedings of the Fourth Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico, US
%@ 979-8-89176-239-8
%F kim-etal-2025-voice
%X Writing well requires not only expressing ideas but also refining them through revision, a process facilitated by reflection. Prior research suggests that feedback delivered through dialogues, such as those in writing center tutoring sessions, can help writers reflect more thoughtfully on their work compared to static feedback. Recent advancements in multi-modal large language models (LLMs) now offer new possibilities for supporting interactive and expressive voice-based reflection in writing. In particular, we propose that LLM-generated static feedback can be repurposed as conversation starters, allowing writers to seek clarification, request examples, and ask follow-up questions, thereby fostering deeper reflection on their writing. We argue that voice-based interaction can naturally facilitate this conversational exchange, encouraging writers’ engagement with higher-order concerns, facilitating iterative refinement of their reflections, and reduce cognitive load compared to text-based interactions. To investigate these effects, we propose a formative study exploring how text vs. voice input influence writers’ reflection and subsequent revisions. Findings from this study will inform the design of intelligent and interactive writing tools, offering insights into how voice-based interactions with LLM-powered conversational agents can support reflection and revision.
%U https://aclanthology.org/2025.in2writing-1.7/
%P 69-73
Markdown (Informal)
[Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing](https://aclanthology.org/2025.in2writing-1.7/) (Kim et al., In2Writing 2025)
ACL