Artificial Intelligence in English Language Learning: A Systematic Review of Personalization and Learner Engagement

Abstract

Artificial Intelligence (AI) is revolutionizing the way English learning is approached, providing the opportunity to create personalized learning experiences that adapt and respond to each student's unique needs and emotions. This systematic review reviews the findings of recent empirical and theoretical research to examine the effect of AI-based technologies such as Intelligent Tutoring Systems (ITS), interactive chatbots, and generative AI models such as ChatGPT on personalization and learner engagement. Results show that AI promotes tailored learning and instructional approaches through adaptive learning algorithms and comprehensive student models, which adapt instruction and feedback to the cognitive and linguistic needs of each student. In terms of learner engagement, AI tools have been demonstrated to have a substantial impact on motivation, self-efficacy, and the decrease of Foreign Language Learning Anxiety (FLLA), as they offer a safe, low-risk learning environment. Despite such advantages, the review highlights key obstacles such as the lack of a cohesive, theoretical framework for designing AI applications, improving teacher AI literacy, and addressing ethical issues like data privacy and language bias. The review concludes by recommending a mixed-methods pedagogy that blends the precision of the machines and the socio-emotional support of humans, and advocates for the need for follow-up studies to evaluate the long-term impact of AI on language retention and its translation to real-world language ability.