Leveraging Natural Language Processing (NLP) and machine learning in Task-Based Language Teaching: enhancing Chinese language acquisition with AI-driven feedback systems

This study explores the innovative application of intelligent technology in the task-based Chinese teaching method, focusing on the effectiveness of real-time guidance of speech recognition and intelligent analysis technology on learners' pronunciation, grammar, and vocabulary. In the experiment, 100 Chinese second language learners were divided into intelligent assistant groups and traditional teaching groups for comparative observation. According to the data, the task completion efficiency of the intelligent group increased by 20%, and the language proficiency evaluation index increased by an average of 30%. More than 80% of learners reported that the instant feedback mechanism effectively improved their confidence and participation in learning. The research proves that intelligent technology can build dynamic learning paths and optimize language acquisition efficiency through personalized training modules. Although the system has technical bottlenecks in the dimension of understanding cultural context, the experimental results provide empirical support for the deep integration of intelligent technology and language teaching, and lay the technical foundation for further research and development of a culturally sensitive intelligent teaching system.