AI-Assisted Strategies for Improving Chinese Proficiency in Non-Native AP Exam Takers
DOI:
https://doi.org/10.71222/4tksvc16Keywords:
AI-assisted learning, AP Chinese exam, language proficiency, data privacy, educational equityAbstract
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.
References
1. Q. Tang, "Exploring the application of generative ai in tprs international chinese language teaching practice: a case study of international students with elementary chinese proficiency," Eurasia J. Sci. Technol., no. 5, 2024, doi: 10.61784/EJST3034.
2. D. Zhang, T. Hoang, S. Pan, Y. Hu, Z. Xing, M. Staples, and A. Quigley, "Test-takers have a say: Understanding the implications of the use of AI in language tests," arXiv preprint arXiv:2307.09885, 2023, doi: 10.48550/arXiv.2307.09885.
3. Z. Li, C. J. Bonk, and C. Zhou, "Supporting learners self-management for self-directed language learning: A study within Du-olingo,"Interact. Technol. Smart Educ., vol. 3, pp. 381–402, 2024, doi: 10.1108/ITSE-05-2023-0093.
4. L. Huang, "Ethics of artificial intelligence in education: Student privacy and data protection," Sci. Insights Educ. Front., vol. 16, no. 2, pp. 2577–2587, 2023, doi: 10.15354/SIEF.23.RE202.
5. R. S. Baker and A. Hawn, "Algorithmic bias in education," Int. J. Artif. Intell. Educ., pp. 1–41, 2022, doi: 10.1007/S40593-021-00285-9.
6. K. Holstein and S. Doroudi, "Equity and artificial intelligence in education," in The Ethics of Artificial Intelligence in Education, Routledge, 2022, pp. 151–173, ISBN 9780429329067.
7. S. Aslam, O. Faisal, and H. Kamal, "Analyzing AI’s role in promoting diversity and inclusivity within educational systems, addressing different learning styles and needs,"Rev. Appl. Manag. Soc. Sci., vol. 7, no. 4, pp. 1099–1113, 2024, doi: 10.47067/ramss.v7i4.446.
8. D. S. Sumo and M. L. Bah, "Chinese language education in the era of artificial intelligence: Innovation development, pedagogy & the smart classroom,"Educ. Q. Rev., vol. 4, no. 4, 2021. doi: 10.31219/osf.io/axr27.
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Copyright (c) 2025 Zengxian Mo (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
