Application of Artificial Intelligence in ESL Curriculum Design for Mandarin-Speaking Learners
DOI:
https://doi.org/10.61173/6nk1h734Keywords:
Artificial Intelligence, ESL Curriculum Design, Mandarin-Speaking Learners, Intelligent Teaching Resources, Automated Assessment, Real-time Interaction, Self-directed Learning, Online Interactive Games, Virtual Reality Simulations, Personalized Learning Paths, Multimodal Language Learning, Collaborative Learning Spaces, Lifelong Learning, Career DevelopmentAbstract
learners, with a focus on its potential to significantly enhance teaching effectiveness and improve learning outcomes. It meticulously defines ESL and elucidates its fundamental principles, underlining the imperative of providing comprehensible language input, fostering interactivity, contextualization, and personalization. By delving into four pivotal aspects, namely provision of intelligent teaching resources, automated assessment and feedback mechanisms, real-time interaction platforms, and support for self-directed learning, this paper showcases how AI-driven technologies can adeptly customize learning experiences to suit individual learner requirements. Furthermore, it expounds on the challenges and considerations entailed in this integration, encompassing ethical, privacy, and equity concerns, while also delineating future prospects for personalized learning and lifelong learning support. This comprehensive examination underscores AI’s potential to revolutionize language education for Mandarin-speaking learners, creating a more personalized, interactive, and effective learning environment.
References
[1] American Heritage Dictionary of the English Language, Fifth Edition. (2016). Houghton Mifflin Harcourt Publishing Company.
[2] Krashen, S. (1985). The Input Hypothesis: Issues and Implications. Addison-Wesley.
[3] Ellis, R. (2003). Task-based Language Learning and Teaching. Oxford University Press.
[4] Brown, H. D. (2007). Principles of Language Learning and Teaching. Pearson Education.
[5] Nunan, D. (1999). Second Language Teaching and Learning. Heinle & Heinle Publishers.
[6] Lave, J., & Wenger, E. (1991). Situated Learning: Legitimate Peripheral Participation. Cambridge University Press.
[7] Warren, M. A., Burr, C., & Green, D. P. (2020). Privacy and civil liberties concerns and attitudes about government surveillance: A systematic review. *Journal of Cybersecurity*
[8] Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In *Conference on Fairness, Accountability and Transparency* PMLR.
[9] Warschauer, M. (2004). Technology and social inclusion: Rethinking the digital divide. *The MIT Press*.
[10] Blikstein, P. (2011). Using learning analytics to assess students’ behavior in open-ended programming tasks. *Proceedings of the 1st International Conference on Learning Analytics and Knowledge*
[11] Kress, G., & Van Leeuwen, T. (2006). *Reading images: The grammar of visual design*. Routledge.
[12] Dillenbourg, P. (1999). What do you mean by “collaborative learning”?. In *Collaborative-learning: Cognitive and computational approaches* Elsevier.
[13] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. *Pearson*.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.

This work is licensed under a Creative Commons Attribution 4.0 International License.
