@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://preview.aclanthology.org/ingest-emnlp/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."
}