Adaptive Artificial Intelligence in English Language Learning: Integrating Personalized Feedback, Motivation, and Cognitive Load in Second Language Acquisition

This study investigates the application of adaptive artificial intelligence (AI) in the process of learning English as a second language (L۲), focusing on three main dimensions: personalized feedback, learner motivation, and cognitive load. The research aims to analyze the effects of AI-based adaptive systems on learners' language performance and overall learning experience. To this end, a systematic review and comparative analysis of both domestic and international literature was conducted, encompassing ۲۰ selected relevant articles. The findings indicate that adaptive AI systems can enhance learner motivation by providing immediate and individually tailored feedback while simultaneously reducing cognitive load. This leads to improvements in listening, speaking, writing, and reading comprehension skills. Furthermore, the analysis demonstrates that the use of adaptive algorithms and intelligent chatbots in both online and face-to-face learning environments increases learner interaction with content and personalizes the learning experience. The results of this study can be valuable for instructional designers, second language researchers, and educational software developers. It is recommended that adaptive AI tools with real-time personalized feedback be integrated into language learning programs to maximize learning effectiveness. Additionally, this study identifies gaps in the current research literature and suggests directions for future investigation.

کلیدواژه ها:

Adaptive artificial intelligence ، personalized feedback ، learner motivation ، cognitive load ، second language learning