Optimizing Chinese Pronunciation Instruction for Portuguese-Speaking Learners: a Data Science and AI-Integrated Approach

EasyChair Preprint 14809

Abstract

This paper focuses on enhancing the teaching of Chinese pronunciation to Portuguese-speaking learners through the integration of data science and artificial intelligence (AI). By examining tone mispronunciation challenges faced by these learners, the study employs a comparative analysis involving three groups: human-machine collaboration, interpersonal assistance, and individual self-aid utilizing AI tools. The findings reveal the strengths and limitations of each approach. The significance of this research is multifaceted: it underscores the distinctiveness of the human-machine collaboration era, emphasizes the necessity and importance of optimizing Chinese pronunciation teaching from a data science perspective, and alerts educators and researchers in the field of Chinese as a foreign language (CFL) to the transformative impact of big data and AI on CFL instruction and broader education.

Keyphrases: AI Integration, CFL (Chinese as a foreign language), Chinese pronunciation instruction, Data Science, Portuguese-speaking learners, human-machine collaboration., tone mispronunciation

@booklet{EasyChair:14809,
  author    = {Zhang Jin Ping and Xiaohui Zou},
  title     = {Optimizing Chinese Pronunciation Instruction for Portuguese-Speaking Learners: a Data Science and AI-Integrated Approach},
  howpublished = {EasyChair Preprint 14809},
  year      = {EasyChair, 2024}}