@inproceedings{shimabukuro-etal-2025-langeye,
title = "{L}ang{E}ye: Toward `Anytime' Learner-Driven Vocabulary Learning From Real-World Objects",
author = "Shimabukuro, Mariana and
Panchal, Deval and
Collins, Christopher",
editor = {Kochmar, Ekaterina and
Alhafni, Bashar and
Bexte, Marie and
Burstein, Jill and
Horbach, Andrea and
Laarmann-Quante, Ronja and
Tack, Ana{\"i}s and
Yaneva, Victoria and
Yuan, Zheng},
booktitle = "Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.bea-1.33/",
doi = "10.18653/v1/2025.bea-1.33",
pages = "446--459",
ISBN = "979-8-89176-270-1",
abstract = "We present LangEye, a mobile application for contextual vocabulary learning that combines learner-curated content with generative NLP. Learners use their smartphone camera to capture real-world objects and create personalized ``memories'' enriched with definitions, example sentences, and pronunciations generated via object recognition, large language models, and machine translation.LangEye features a three-phase review system {---} progressing from picture recognition to sentence completion and free recall. In a one-week exploratory study with 20 French (L2) learners, the learner-curated group reported higher engagement and motivation than those using pre-curated materials. Participants valued the app{'}s personalization and contextual relevance. This study highlights the potential of integrating generative NLP with situated, learner-driven interaction. We identify design opportunities for adaptive review difficulty, improved content generation, and better support for language-specific features. LangEye points toward scalable, personalized vocabulary learning grounded in real-world contexts."
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<abstract>We present LangEye, a mobile application for contextual vocabulary learning that combines learner-curated content with generative NLP. Learners use their smartphone camera to capture real-world objects and create personalized “memories” enriched with definitions, example sentences, and pronunciations generated via object recognition, large language models, and machine translation.LangEye features a three-phase review system — progressing from picture recognition to sentence completion and free recall. In a one-week exploratory study with 20 French (L2) learners, the learner-curated group reported higher engagement and motivation than those using pre-curated materials. Participants valued the app’s personalization and contextual relevance. This study highlights the potential of integrating generative NLP with situated, learner-driven interaction. We identify design opportunities for adaptive review difficulty, improved content generation, and better support for language-specific features. LangEye points toward scalable, personalized vocabulary learning grounded in real-world contexts.</abstract>
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%0 Conference Proceedings
%T LangEye: Toward ‘Anytime’ Learner-Driven Vocabulary Learning From Real-World Objects
%A Shimabukuro, Mariana
%A Panchal, Deval
%A Collins, Christopher
%Y Kochmar, Ekaterina
%Y Alhafni, Bashar
%Y Bexte, Marie
%Y Burstein, Jill
%Y Horbach, Andrea
%Y Laarmann-Quante, Ronja
%Y Tack, Anaïs
%Y Yaneva, Victoria
%Y Yuan, Zheng
%S Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-270-1
%F shimabukuro-etal-2025-langeye
%X We present LangEye, a mobile application for contextual vocabulary learning that combines learner-curated content with generative NLP. Learners use their smartphone camera to capture real-world objects and create personalized “memories” enriched with definitions, example sentences, and pronunciations generated via object recognition, large language models, and machine translation.LangEye features a three-phase review system — progressing from picture recognition to sentence completion and free recall. In a one-week exploratory study with 20 French (L2) learners, the learner-curated group reported higher engagement and motivation than those using pre-curated materials. Participants valued the app’s personalization and contextual relevance. This study highlights the potential of integrating generative NLP with situated, learner-driven interaction. We identify design opportunities for adaptive review difficulty, improved content generation, and better support for language-specific features. LangEye points toward scalable, personalized vocabulary learning grounded in real-world contexts.
%R 10.18653/v1/2025.bea-1.33
%U https://aclanthology.org/2025.bea-1.33/
%U https://doi.org/10.18653/v1/2025.bea-1.33
%P 446-459
Markdown (Informal)
[LangEye: Toward ‘Anytime’ Learner-Driven Vocabulary Learning From Real-World Objects](https://aclanthology.org/2025.bea-1.33/) (Shimabukuro et al., BEA 2025)
ACL