Non-native English speakers performing English-related tasks at work struggle to sustain EFL learning, despite their motivation. Often, study materials are disconnected from their work context. Our formative study revealed that reviewing work-related English becomes burdensome with current systems, especially after work. Although workers rely on LLM-based assistants to address their immediate needs, these interactions may not directly contribute to their English skills. We present LingoQ, an AI-mediated system that allows workers to practice English using quizzes generated from their LLM queries during work. LingoQ leverages these on-the-fly queries using AI to generate personalized quizzes that workers can review and practice on their smartphones. We conducted a three-week deployment study with 28 EFL workers to evaluate LingoQ. Participants valued the quality-assured, work-situated quizzes and constantly engaging with the app during the study. This active engagement improved self-efficacy and led to learning gains for beginners and, potentially, for intermediate learners. Drawing on these results, we discuss design implications for leveraging workers' growing reliance on LLMs to foster proficiency and engagement while respecting work boundaries and ethics.

翻译：在工作场所执行英语相关任务的非母语人士尽管有学习动机，却难以持续进行英语作为外语的学习。常见问题是学习材料与工作情境脱节。我们的形成性研究表明，使用现有系统复习工作相关英语内容（尤其是下班后）会带来沉重负担。尽管工作者依赖基于大语言模型的助手来满足即时需求，但这些互动未必能直接提升其英语能力。本文提出LingoQ——一个AI介导的系统，允许工作者通过工作中产生的LLM查询记录生成测验来练习英语。LingoQ运用AI技术即时处理这些查询，生成个性化测验供工作者在智能手机上复习练习。我们开展了为期三周、涉及28名EFL工作者的部署研究以评估LingoQ。参与者高度认可经过质量保证且贴合工作情境的测验，并在研究期间持续使用该应用。这种主动参与提升了自我效能感，为初学者带来了显著的学习收益，对中级学习者也可能产生积极影响。基于这些发现，我们探讨了如何利用工作者日益增长的LLM依赖来提升语言熟练度与参与度的设计启示，同时兼顾工作边界与伦理考量。