Artificial Intelligence (AI) is transforming language learning by offering personalized, adaptive, and emotionally responsive educational experiences. This review synthesizes findings from 26 recent empirical and theoretical studies to evaluate the effectiveness of AI tools such as chatbots, pedagogical agents, and generative AI in enhancing learner engagement, reducing foreign language anxiety, and improving vocabulary acquisition. The results indicate that AI-driven systems contribute to better vocabulary retention, emotional regulation, and learner motivation, particularly when informed by educational theories like self-determination and design thinking. Despite these benefits, the review identifies significant challenges, including digital inequality, insufficient teacher training, algorithmic bias, and a limited linguistic range. While AI can promote learner autonomy and provide low anxiety learning environments, it may also lead to technostress and dependency if not properly integrated with pedagogical support. The study highlights the importance of educator preparedness and ethical AI implementation. Using qualitative-comparative and bibliometric analysis, the review proposes a multidimensional model that emphasizes adaptive feedback, emotional scaffolding, and theoretical alignment. It calls for inclusive AI design, equitable access to technology, and continuous professional development for educators. Future research should adopt longitudinal, interdisciplinary, and culturally adaptive frameworks to examine AI's long-term and sustainable impact on language acquisition in varied educational settings.
Jack Ng Kok Wah (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: