LingoQ: Bridging the Gap between EFL Learning and Work through AI-Generated Work-Related Quizzes

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.

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Publication Details

Published
2026-04-13
DOI
https://doi.org/10.1145/3772318.3791342
Citations
1
Primary Topic
Robotics and Automated Systems
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article
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0.00
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article

LingoQ: Bridging the Gap between EFL Learning and Work through AI-Generated Work-Related Quizzes

Sangdoo Yun, Jean Song
1 citations
Robotics and Automated Systems
article

LingoQ: Bridging the Gap between EFL Learning and Work through AI-Generated Work-Related Quizzes

Sangdoo Yun, Jean Song
article en
1 citations

Abstract

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.

Daegu Gyeongbuk Institute of Science and Technology (KR), Yonsei University (KR), Interaction Design (United Kingdom) (GB), Naver (South Korea) (KR)
Openalex Percentile: Top 99%
Robotics and Automated Systems
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