Natural language processing for automatic error detection in Chinese language learning

With the rapid development of global Chinese language education, the demand for efficient and accurate automated teaching assistance tools is growing.Traditional manual grading methods are often time-consuming and yield inconsistent results, highlighting the necessity for intelligent technological solutions.This paper explores the application of natural language processing techniques in automatic error detection for Chinese as a second language.It proposes a method based on pre-trained language models and evaluates it using a publicly available corpus of Chinese learner compositions.Experimental results demonstrate the strong performance of the proposed method in identifying grammatical and lexical errors, achieving detection accuracy exceeding 80% for major error categories.This represents a significant improvement over baseline systems (over 25% increase).This technology shows great potential as an efficient teaching support tool, enabling more effective and consistent feedback mechanisms within intelligent educational environments.