The Impact of AI-Driven Personalized Learning on Language Acquisition and Assessment Strategies for Multilingual Learners (literature review)

Abstract

This paper explores the transformative impact of Artificial Intelligence (AI)-driven personalized learning on language acquisition and assessment strategies, specifically focusing on multilingual learners. As global interconnectedness increases, the demand for effective language proficiency among diverse linguistic populations has never been more critical. Traditional language learning methodologies often struggle to cater to the heterogeneous needs of multilingual learners, leading to varied learning outcomes and engagement levels. AI-driven personalized learning offers a promising paradigm shift by adapting educational content, pace, and instructional strategies to individual learner profiles. This review synthesizes current literature to elucidate how AI technologies, including adaptive learning platforms, intelligent tutoring systems, and natural language processing, facilitate enhanced language acquisition through individualized pathways, real-time feedback, and dynamic content recommendations. Furthermore, the paper examines the evolution of assessment strategies in an AI-enhanced environment, highlighting the potential for more equitable, valid, and reliable evaluations for multilingual learners. It addresses the challenges associated with integrating AI into both learning and assessment, such as data privacy, algorithmic bias, and technological infrastructure limitations. By analyzing existing research and identifying emerging trends, this paper aims to provide a comprehensive understanding of the opportunities and implications of AI in fostering linguistic competence and refining assessment practices for multilingual learners, ultimately contributing to more inclusive and effective language education.