GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866918081906868224 |
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| author | Yeh, Catherine Ramos, Gonzalo Ng, Rachel Huntington, Andy Banks, Richard |
| author_facet | Yeh, Catherine Ramos, Gonzalo Ng, Rachel Huntington, Andy Banks, Richard |
| contents | Large language models (LLMs) have become ubiquitous in providing different forms of writing assistance to different writers. However, LLM-powered writing systems often fall short in capturing the nuanced personalization and control needed to effectively support users -- particularly for those who lack experience with prompt engineering. To address these challenges, we introduce GhostWriter, an AI-enhanced design probe that enables users to exercise enhanced agency and personalization during writing. GhostWriter leverages LLMs to implicitly learn the user's intended writing style for seamless personalization, while exposing explicit teaching moments for style refinement and reflection. We study 18 participants who use GhostWriter on two distinct writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style. Based on this study, we present insights on how specific design choices can promote greater user agency in AI-assisted writing and discuss people's evolving relationships with such systems. We conclude by offering design recommendations for future work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08855 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency Yeh, Catherine Ramos, Gonzalo Ng, Rachel Huntington, Andy Banks, Richard Human-Computer Interaction Artificial Intelligence Large language models (LLMs) have become ubiquitous in providing different forms of writing assistance to different writers. However, LLM-powered writing systems often fall short in capturing the nuanced personalization and control needed to effectively support users -- particularly for those who lack experience with prompt engineering. To address these challenges, we introduce GhostWriter, an AI-enhanced design probe that enables users to exercise enhanced agency and personalization during writing. GhostWriter leverages LLMs to implicitly learn the user's intended writing style for seamless personalization, while exposing explicit teaching moments for style refinement and reflection. We study 18 participants who use GhostWriter on two distinct writing tasks, observing that it helps users craft personalized text generations and empowers them by providing multiple ways to control the system's writing style. Based on this study, we present insights on how specific design choices can promote greater user agency in AI-assisted writing and discuss people's evolving relationships with such systems. We conclude by offering design recommendations for future work. |
| title | GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2402.08855 |