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

arXiv: https://arxiv.org/abs/2509.17477 · HTML: https://mlsanity.com/paper/2509.17477/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 104 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 14 of 14)

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- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
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- [The Impact of Multiple Parallel Phrase Suggestions on Email Input and Composition Behaviour of Native and Non-Native English Writers](https://mlsanity.com/paper/2101.09157/) (2021)
- ["What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models](https://mlsanity.com/paper/2304.06597/) (2023)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [The 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?](https://mlsanity.com/paper/2305.02878/) (2023)
- [Storyfier: Exploring Vocabulary Learning Support with Text Generation Models](https://mlsanity.com/paper/2308.03864/) (2023)
- [CorpusStudio: Surfacing Emergent Patterns in a Corpus of Prior Work while Writing](https://mlsanity.com/paper/2503.12436/) (2025)
- [Design Opportunities for Explainable AI Paraphrasing Tools: A User Study with Non-native English Speakers](https://mlsanity.com/paper/2405.07475/) (2024)
- [WordDecipher: Enhancing Digital Workspace Communication with Explainable AI for Non-native English Speakers](https://mlsanity.com/paper/2404.07005/) (2024)
- [ARbis Pictus: A Study of Language Learning with Augmented Reality](https://mlsanity.com/paper/1711.11243/) (2017)
- [Unknown Word Detection for English as a Second Language (ESL) Learners Using Gaze and Pre-trained Language Models](https://mlsanity.com/paper/2502.10378/) (2025)

## 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.