AI Personalized Language Learning Application

This paper presents the design and implementation of an AI personalized language learning application that addresses limitations in existing platforms through promptengineered language models and personalized content delivery. The system employs large language models with specialized prompting techniques to provide assessments and feedback interaction modalities. The application's architecture integrates weighted evaluation metrics to analyze conversation quality, grammatical accuracy, and contextual relevance, while dynamically adjusting content difficulty based on user performance patterns. Implementation results demonstrate potential for improved engagement through personalized learning pathways. This research contributes to educational technology by establishing a framework for LLM-driven language learning that balances structured assessment with adaptive content delivery, while proposing evaluation methods to validate effectiveness in real-world scenarios.