Learning Low-Resource Languages Through NLP -Driven F lashcards: A Case Study of Hokkien in Language Learning Applications (2025)

Abstract: LangLearn is an open-source framework designed to facilitate autonomous learning of low-resource languages (LRL). By combining a language-agnostic approach with AI-enhanced flashcards, LangLearn empowers users to generate custom flashcards for their vocabulary, while offering structured learning through both pre-curated and self-curated decks. The framework integrates six key components: the word definition, corresponding Hanji characters, romanization with numeric tones, audio pronunciation, a sample sentence, as well as a contextual AI-generated image. LangLearn currently supports English and Taiwanese Hokkien (a variety of Southern Min), with plans to extend support for other dialects. Our preliminary study demonstrates that LangLearn positively empowers users to engage with LRLs using their vocabulary preferences, with a comprehensive user study currently underway. LangLearn’s modular structure enables future expansion, including ASR-based pronunciation practice. The code is available at https://github.com/HokkienTranslation/HokkienTranslation.

## introduction language learning programs have becoming increasingly popular, with apps such as duolingo reaching over 200 million users 2023 @xcite . however, a key limitation in many of these apps is their focus on popular languages, leaving lowresource languages (lrls) such as taiwanese hokkien underrepresented. taiwanese hokkien, a variety of southern min, is facing a dramatic decline in speakers -less than 10% of people aged 18-24 can speak hokkien compared to 66% of those 65 and older @xcite . while recent advances in generative artificial intelligence (genai) models have enabled more personalized language learning experiences with shifts to learner-centered experiences @xcite , lrls remain poorly supported by popular large language models due to the lack of training data @xcite . dialect differences and the lack of written material make it particularly difficult to develop language models or learning tools for these languages, creating a critical gap in technology support. notably, while personalization was not shown to increase vocabulary learning, it was shown to improve learner motivation @xcite . to address these challenges, we present lan-glearn, a modular framework designed to facilitate the autonomous learning of lrls. langlearn integrates multiple ai models into a flashcard generation pipeline, producing personalized ai-enhanced flashcards that support vocabulary acquisition, pronunciation practice as well as contextualized learning. the framework is built to be extensible, allowing for easy adaption for other lrls through language-specific translation/speech synthesis models @xcite @xcite . as an autonomous selfstudy app, langlearn utilizes ai models to to meet the user at their interests. this paper describes the design and implementation of langlearn, focusing particularly on how it supports the self-study of hokkien as a second language @xcite . results of preliminary user studies showed that 82% of users agreed that the app was easy to use, with numerous users enjoying the novelty of the contextualized definitions. reliability issues around ai-generated content such as inconsistent translations and image relevance are being continually addressed with a community rating system and user feedback, with future updates planned to improve the accuracy of ai models, enhance pronunciation support, and expand lan-glearn's language offerings. ## langlearn -learner-centered design langlearn is designed to address the unique challenges of learning lrls like hokkien, which are predominantly oral with limited written resources @xcite . the application has two major components: (1) a dictionary and (2) flashcard features. figure 1 illustrates the primary user flow through the application, from signing up to practicing with flashcards and testing knowledge with quizzes. learner account. users can create an account with either their email or google account. once the user is authenticated, they can access langlearn's features. the user account enables personalization of the learner's experience and safe storage of their progress. ## user study to help assess the usability of langlearn, we completed a preliminary study over three weeks using an early version of the application to learn hokkien, we recruited 39 participants. the participants were undergraduate students from two canadian universities without prior knowledge of hokkien. they were asked to use the application for three weeks and provide feedback for the application through usability surveys at the end of each week. ## conclusion as an open-source framework, langlearn is designed to facilitate autonomous low-resource language (lrl) learning. langlearn empowers learners by giving them the power to generate contextualized flashcards complete with definitions, sample sentences, as well as audio pronunciation in the target language. these flashcards can be put into decks, or added to the existing categories to support learners in their daily life, with support for quizzes and sharing flashcards out-of-the-box. langlearn currently supports english and taiwanese hokkien, with plans to expand towards asr-based pronunciation practice to emphasize the spoken nature of many low-resource languages.