AI 中文总结
本文介绍了一个教育工件，通过本地LLM和桑基图可视化从数字活动到个性化推荐的路径，试点研究表明它能有效教授概念关系并引发隐私意识，但行为改变预期有限。
AI 中文摘要
个人数字活动日益塑造在线体验，但很少有用户接受过关于将原始交互转化为个性化建议的过程的教育。我们开发了一个教育工件，通过示例模拟人工智能如何利用用户的数字活动来塑造在线推荐（如广告）。该工件使用本地托管的LLM处理用户的数字活动，生成用户画像，包含推断的兴趣和个性化推荐。一个三层桑基图将数据源映射到推断的兴趣，再到个性化推荐。交互式过滤器使用户能够探索不同数据源组合如何影响个性化结果。本文描述了该工件及其教育价值，并报告了一项针对六名年轻成年人的试点出声思维研究的结果。我们发现，该工件有效地教会了参与者数字活动与个性化推荐之间的概念关系。虽然这确实使参与者形成了隐私意识，但由于感知到平台参与的不可避免性，他们预期行为改变甚微。
英文摘要
Personal digital activity increasingly shapes online experiences, yet few users have been educated regarding the processes transforming raw interactions into personalized suggestions. We developed an education artifact that illustratively simulates how AI leverages users' digital activities to shape online recommendations (e.g., ads). Our artifact processes users' digital activity using a locally-hosted LLM to generate user profiles of their inferred interests and personalized recommendations. A three-layered Sankey diagram maps data sources through inferred interests to personalized recommendations. Interactive filters enable users to explore how different combinations of data sources influence personalized outcomes. This paper describes the artifact and its educational value, and reports findings of a pilot think-aloud study with six young adults. We find that the artifact effectively taught participants the conceptual relationship between digital activities and personalized recommendations. While this did lead participants to develop privacy awareness, they anticipated minimal behavior change due to the perceived unavoidability of platform participation.
