用可调滑块让AI根据读者背景个性化改写科学文本
Steering AI-Driven Personalization of Scientific Text for General Audiences
- 通过用户画像控制AI生成文本的亲和度，滑块调节0到100度
- 高亲和度提升理解，低亲和度更简洁，用户反馈双模式有效
- 适合科普平台、教育工具及人机协同界面设计者参考
数字媒体平台（如科学博客）为向大众传播科学内容提供了规模化机会。然而，受众在科学素养、阅读水平和个人背景上存在差异，使有效传播面临挑战。为此，我们设计了TranSlider——一个基于用户画像（如兴趣、所在地、教育程度）生成个性化科学文本翻译的AI工具。该工具配备交互式滑块，支持用户将个性化程度从0（弱关联）调节至100（强关联），并利用大语言模型生成相应译文。通过对15名参与者开展探索性研究，我们考察了个性化译文的实用性以及交互功能对理解与阅读体验的影响。结果发现：偏好高个性化度的用户认为关联性强、上下文丰富的译文更易理解；偏好低个性化度的用户则更欣赏简洁且含少量背景信息的版本。此外，用户普遍报告多重译文叠加显著提升了对科学内容的理解。基于这些发现，我们讨论了促进科学传播及设计可调控人机协同界面的若干启示。
原文摘要 · Abstract (English)
Digital media platforms (e.g., science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool that generates personalized translations of scientific text based on individual user profiles (e.g., hobbies, location, and education). Our tool features an interactive slider that allows users to steer the degree of personalization from 0 (weakly relatable) to 100 (strongly relatable), leveraging LLMs to generate the translations with chosen degrees. Through an exploratory study with 15 participants, we investigated both the utility of these AI-personalized translations and how interactive reading features influenced users' understanding and reading experiences. We found that participants who preferred higher degrees of personalization appreciated the relatable and contextual translations, while those who preferred lower degrees valued concise translations with subtle contextualization. Furthermore, participants reported the compounding effect of multiple translations on their understanding of scientific content. Drawing on these findings, we discuss several implications for facilitating science communication and designing steerable interfaces to support human-AI alignment.
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