LearnLanguage: Design and Implementation of a Localized AI Language Learning System Based on LLM

LearnLanguage is a localized AI language learning system based on Large Language Models (LLM). It supports four languages: Spanish, Russian, French, and Simplified Chinese. By integrating OpenAI structured output, Edge-TTS speech synthesis technology, and the principle of retrieval practice from cognitive science, it builds a complete learning loop. The system adopts a localized data storage strategy to protect privacy and support offline use, providing both browser-based web application and desktop application versions to adapt to different scenario needs.

Language learning software has evolved from paper textbooks to digital applications and then to AI-assisted tools. LearnLanguage represents a new design approach: using LLM as the core engine, integrating speech synthesis and spaced repetition principles to build a localized intelligent system. It supports multiple languages and optimizes for different writing systems (e.g., Latin alphabet, Cyrillic alphabet, Chinese characters), catering to both browser and desktop application scenarios.

The functions are designed around vocabulary acquisition, scenario comprehension, active recall, and speech output loop: Vocabulary generation supports user-defined or preset topics; scenario comprehension generates short texts with explanations. The tech stack uses Python backend and browser frontend, and it is recommended to use tutors/tutor3 to integrate the web application. Data is stored locally in the runtime directory (ignored by Git) to protect privacy and enable offline use. When OpenAI service is unavailable, it automatically switches to demo content.

Speech output uses the Edge-TTS engine, providing independent audio control (play item by item, batch review) and real-time waveform graphs for each learning item. Multilingual support includes rendering processing for non-Latin alphabet texts, and local caching of translation results to avoid repeated API calls, adapting to unstable network environments.

Following the principle of retrieval practice, the testing module includes four question types: spelling recall (orthography), dictation (speech-to-text), sound discrimination (voice differentiation), and scenario comprehension (reading). The insight module tracks data such as accuracy rate and weak points, recommends personalized practice focus, and optimizes the learning path.

The project highlights include: Using LLM as the content generation engine to break the limitations of fixed word banks; Localization-first strategy to balance privacy and offline availability; Translating cognitive science principles into functional modules. It provides AI education tool developers with a fully functional and clearly structured reference implementation.

Deployment steps: Install dependencies → Configure OpenAI API key → Start local server; Optional environment variables can override the default model (e.g., gpt-4o-mini). Usage recommendations: Switch target languages via national flags, select vocabulary/scenario learning mode, enter the testing module to consolidate after learning, and adjust practice focus based on insight data.