Research on the Application Scenarios and Limitations of Artificial Intelligence-Assisted Chinese Oral Language Teaching

Abstract

With the breakthrough advancements in artificial intelligence technology, its deep integration with second language acquisition theories is reshaping the structure and paradigms of Chinese oral language teaching. This study systematically explores the theoretical mechanisms, multidimensional applications, and inherent limitations of AI-assisted Chinese oral language instruction. By analyzing the transformation of teaching paradigms and the deconstruction of competency components driven by intelligent technology, the research constructs systematic application scenarios across dimensions such as pronunciation feedback, contextual dialogue, adaptive learning, and immersive environments. Simultaneously, the study reveals multiple constraints currently faced by technology-enabled teaching, including technical bottlenecks in cultivating complex communicative competence, adaptive limitations in teaching Chinese tones and pragmatic cultural elements, fairness concerns arising from data and algorithms, and ethical challenges posed by the blurring boundaries between humans and machines. This study aims to provide a systematic analytical framework for prudently advancing the deep integration of artificial intelligence and Chinese oral language teaching.