LangEye: Toward ‘Anytime’ Learner-Driven Vocabulary Learning From Real-World Objects

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 learnercurated 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 realworld contexts.