Improved Personalized Language Learning for English Learners: A Systematic Review of NLP's Impact

English proficiency is essential for non-native speakers, shaping academic and career outcomes in an increasingly globalized world. With rising demand for English language skills, advancements in Artificial Intelligence (AI), particularly Natural Language Processing (NLP), have transformed Computer-Assisted Language Learning (CALL). This study systematically reviews current research on the impact of NLP on personalized language learning for English learners, addressing how NLP technologies enhance educational outcomes by adapting to individual needs and fostering engagement. Conducted using a systematic review method and the PRISMA protocol, the study included searches across Scopus, Web of Science, and Wiley databases using keywords like 'Natural Language Processing,' 'Personalized Learning,' and 'English Language Learners'. The systematic search yielded ۲۴۰ articles, of which ۲۲ met the inclusion criteria for final analysis. Findings highlight that NLP technologies such as speech recognition, adaptive learning models, and real-time feedback systems significantly contribute to personalized education by customizing content, offering immediate feedback, and integrating gamified elements that promote active engagement. The review also identifies specific NLP techniques, including conversational agents and intelligent speech recognition, which enhance learner motivation and comprehension. The study concludes that NLP-driven personalized learning supports language proficiency and learner satisfaction and establishes NLP as a vital tool for innovation in language education.

کلیدواژه ها:

Natural Language Processing (NLP) ، Personalized Learning ، English Language Learners ، Artificial Intelligence ، Language Teaching