The Effect of Machine Feedback on Foreign Language Writing

The advancement of artificial intelligence technology has made machine feedback an important tool for teaching foreign language writing. This article systematically explores the technical principles, historical development, and dual impact on writing quality through a literature review. Machine feedback is based on natural language processing (NLP), evolving from early spell checking to intelligent systems that support grammar, vocabulary, and logic optimization, significantly improving learners' vocabulary diversity (such as semantic word recommendation) and text accuracy (reducing grammar error rates by 20% -30%), and reinforcing learning motivation through instant feedback. However, research also reveals its limitations: excessive dependence leads to a decline in students' ability to write independently, algorithmic standardization ignores cultural context and creative expression, and even triggers academic ethical risks. Through analyzing international empirical research in the past decade, it has been found that machine feedback needs to be coordinated with teacher feedback to balance efficiency and critical thinking development. In the future, we should focus on the design of human-machine collaboration models and pay attention to the differentiated needs of learners with different language proficiency levels, promoting innovation and balanced development of foreign language writing education.