Generative AI for Personalized E-Learning: A Conversational and Adaptive Framework for Language Acquisition

The integration of generative AI into e-learning is reshaping language education by providing personalized, adaptive, and interactive learning experiences. This study proposes a modular AI driven architecture for English language acquisition, incorporating automated content generation, interactive conversational agents, and real-time feedback mechanisms. By leveraging advanced natural language processing (NLP) models, the system dynamically adjusts learning materials to match individual learner needs, ensuring a context-aware and continuously evolving educational experience. The proposed implementation follows a structured six-phase development model, utilizing transformer-based NLP models to generate customized exercises and facilitate real-time interactions with an AI powered chatbot. The system’s automated feedback mechanism enables instant correction and progress tracking, encouraging learners to refine their grammar, pronunciation, and fluency through practical engagement. While the system is still under development, preliminary observations suggest its potential to enhance learner engagement and provide a more interactive learning experience compared to traditional e-learning platforms. Future work will focus on completing system evaluation, refining AI generated content, and integrating additional personalization techniques. Key challenges such as AI model interpretability, learner engagement, and ethical considerations must also be addressed. By incorporating emotion-aware AI and multimodal learning approaches, this research aims further to improve the adaptability and effectiveness of AI powered language education.