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This literature review examines the role of artificial intelligence (AI) in enhancing feedback mechanisms within English language learning, emphasizing the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and speech recognition to provide real-time, personalized feedback. The purpose of this review is to synthesize existing research findings on the effectiveness of AI-driven feedback in improving language skills such as grammar, vocabulary, and pronunciation, while also assessing learner engagement and retention rates. A thorough methodology involving a selection of recent peer-reviewed articles, academic databases, and a mixed-methods approach for data synthesis and analysis was employed to ensure comprehensive coverage of the topic. The review highlights that AI feedback often surpasses traditional methods in terms of speed, availability, and personalization. However, it also identifies significant challenges, including issues with accuracy, dependency on extensive datasets, and institutional resistance, which could hinder the broader adoption of AI in educational settings. Also, the paper discusses potential technological improvements, the need for integrative feedback approaches combining human and AI elements, and highlights gaps in current research that offer directions for future inquiry. This comprehensive analysis aims to provide educators, technologists, and policymakers with insights into leveraging AI to foster more effective and engaging language learning experiences.
Sutrisno Sadji Evenddy (Fri,) studied this question.