LingoQ: Bridging the Gap between EFL Learning and Work through AI-Generated Work-Related Quizzes
September 22, 2025 Β· Declared Dead Β· π ACM CHI 2026
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Authors
Yeonsun Yang, Sang Won Lee, Jean Y. Song, Sangdoo Yun, Young-Ho Kim
arXiv ID
2509.17477
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CL
Citations
0
Venue
ACM CHI 2026
Last Checked
4 months ago
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.
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