A Study on the Effectiveness of Large Language Models in Foreign Language Teaching: A Case Study of French Grammatical Error Correction

With the rapid development of artificial intelligence technology, Large Language Models (LLMs) have shown tremendous potential in enhancing teaching effectiveness and promoting personalized learning for students. In foreign language learning, LLMs have been widely used by learners for grammar error correction and writing improvement, making it crucial to understand their advantages and limitations in grammar correction. This study systematically evaluates the effectiveness of three mainstream LLMs—ChatGPT, DeepSeek, and Le Chat—in French Grammar Error Correction. The study found that all three models are capable of identifying and correcting errors in French texts to some extent: DeepSeek performs best in overall capability, ChatGPT excels at comprehensively identifying potential errors, Le Chat performs relatively weaker. All three models perform well in spelling correction, but limitations remain in correcting vocabulary errors and specific grammatical issues, such as agreement in gender and number. This study provides valuable insights into the practical application of LLMs in French education. Future research could further enhance the application of LLMs in foreign language teaching by expanding corpus data and optimizing prompt design.