CHUNK-ORIENTED INSTRUCTION IN TEACHING CHINESE AS A FOREIGN LANGUAGE: AN AI-ENHANCED AND CORPUS-INFORMED TRANSLATION APPROACH

Abstract

Recent advances in cognitive linguistics and artificial intelligence have reshaped perspectives on foreign language pedagogy. Chunk-oriented instruction, which focuses on formulaic multi-word units, has proven effective in second language acquisition; however, its application in Teaching Chinese as a Foreign Language (TCFL) remains insufficiently developed. This study reinterprets chunk theory through the lenses of corpus linguistics, translation studies, and AI-assisted language learning. By examining recurrent Chinese language chunks and their translational behavior, the study argues that chunk-based pedagogy significantly improves learners’ fluency, pragmatic accuracy, and translation quality. The findings indicate that integrating chunk theory with corpus-driven analysis and AI-supported translation tools offers a sustainable and innovative model for TCFL.