Artificial intelligence-driven personalized language learning: Customizing content and feedback to learners’ needs and proficiency levels

Artificial Intelligence (AI)-driven personalized language learning holds great promise for customizing content and feedback according to learners’ needs and proficiency levels. This study explores the potential of AI, particularly large language models (LLMs), for enhancing personalized language learning experiences. The research examines how LLMs can be leveraged to provide dynamic, tailored content and real-time feedback, adapting to individual learners’ progress and learning preferences. A group of 100 language learners participated in a 6-week experiment where they engaged with AI-powered platforms that adjusted the content’s difficulty and offered personalized feedback based on their performance and proficiency levels for English grammar and vocabulary acquisition. The study used a randomized controlled trial design, assessing learners’ language proficiency, motivation, engagement, and perceived effectiveness of the AI-assisted learning experience before and after the intervention. The results showed that LLMs significantly improved learners’ language proficiency, with noticeable increases in their engagement and motivation. Learners reported higher satisfaction with the personalized feedback and content, appreciating the system’s ability to adapt to their unique needs. However, challenges such as occasional misinterpretations by the AI and limited content variety were identified. This study contributes to understanding the effectiveness of AI in creating personalized language learning environments and highlights the potential of integrating such technologies into mainstream language education.