mlsanityData to 14 Oct 2026

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

arXiv 2509.17477 ↗First posted 22 Sep 2025cs.HC cs.AI cs.CLcited by 0

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Abstract (arXiv)

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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References · 14

  1. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  2. 2024On-Device Language Models: A Comprehensive Reviewcited by 104
  3. 2023Bridging the Gulf of Envisioning: Cognitive Design Challenges in LLM Interfacescited by 72
  4. 2022Keep Me Updated! Memory Management in Long-term Conversationscited by 63
  5. 2021The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writerscited by 61
  6. 2023"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Modelscited by 48
  7. 2023A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Educationcited by 35
  8. 2023The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers?cited by 12
  9. 2023Storyfier: Exploring Vocabulary Learning Support with Text Generation Modelscited by 12
  10. 2025CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writingcited by 5
  11. 2024Design Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakerscited by 4
  12. 2024WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakerscited by 2
2 more
  1. 2017ARbis Pictus: A Study of Language Learning with Augmented Realitycited by 1
  2. 2025Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Modelscited by 1

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