Exploration and Research on the Application of Generative AI in Selecting and Matching Teaching Content for College Chinese Classes——A Case Study of Selecting and Matching Examples and Exercises for

Abstract

With the rapid development of generative artificial intelligence (AI), its application in education has attracted increasing attention. This study explores the feasibility and effectiveness of applying generative AI to the selection and matching of teaching content in college Chinese classes, with a particular focus on the intelligent generation of examples and exercises. Using a case-based research method, this study integrates AI tools into the teaching practice of College Chinese and examines their performance in aligning instructional content with curricular goals, language levels, and learning needs. The findings indicate that generative AI can improve the relevance, diversity, and efficiency of content selection and matching, enhancing both teaching effectiveness and student engagement. However, the study also identifies certain limitations, such as content accuracy and contextual appropriateness, which require further refinement and human oversight. This research provides valuable insights and practical strategies for the integration of AI in language education and supports the intelligent upgrading of college Chinese teaching models.