AI-Assisted Strategies for Improving Chinese Proficiency in Non-Native AP Exam Takers

This paper explores the role of AI-assisted strategies in enhancing Chinese language proficiency for non-native students preparing for the AP Chinese exam. By analyzing the key requirements of the AP Chinese exam, the study identifies how AI tools, such as Speechling, FluentU, and HelloChinese, can support students in improving their listening, speaking, reading, and writing skills. The paper also addresses the ethical and social implications of AI in education, focusing on data privacy, algorithmic bias, and educational equity. Key strategies for optimizing AI in language learning are discussed, alongside a comparison of different AI tools in AP exam preparation. The study emphasizes the need for a balanced approach to AI implementation in education, considering both the technological advantages and the ethical challenges. Future research directions are outlined to further explore the long-term impact and integration of AI in educational contexts.

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Publication Details

Journal
Journal of Computer Signal and System Research
Published
2025-03-02
DOI
https://doi.org/10.71222/4tksvc16
Primary Topic
Higher Education Learning Practices
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Assisted Strategies for Improving Chinese Proficiency in Non-Native AP Exam Takers

Mo Zheng
Journal of Computer Signal and System Research
Higher Education Learning Practices
article

AI-Assisted Strategies for Improving Chinese Proficiency in Non-Native AP Exam Takers

Mo Zheng
article en

Abstract

This paper explores the role of AI-assisted strategies in enhancing Chinese language proficiency for non-native students preparing for the AP Chinese exam. By analyzing the key requirements of the AP Chinese exam, the study identifies how AI tools, such as Speechling, FluentU, and HelloChinese, can support students in improving their listening, speaking, reading, and writing skills. The paper also addresses the ethical and social implications of AI in education, focusing on data privacy, algorithmic bias, and educational equity. Key strategies for optimizing AI in language learning are discussed, alongside a comparison of different AI tools in AP exam preparation. The study emphasizes the need for a balanced approach to AI implementation in education, considering both the technological advantages and the ethical challenges. Future research directions are outlined to further explore the long-term impact and integration of AI in educational contexts.

Journal of Computer Signal and System ResearchVol. 2(1)
Suzhou University of Science and Technology (CN)
Openalex Percentile: Top 96%
Higher Education Learning Practices
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