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Article published In: Decolonising or Recolonising?: Generative AI through the eyes of applied linguists, language teachers, and learners
Edited by Toni Dobinson, Julian Chen and Carly Steele
[Australian Review of Applied Linguistics 47:3] 2024
► pp. 284–308

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Beyond borders or building new walls?

The potential for generative AI in recolonising the learning of Vietnamese dialects and Mandarin varieties

ORCiD logo with link | University of Queensland
 | University of Queensland
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Open Access publication of this article was funded through a Transformative Agreement with University of Queensland.

Published online: 7 January 2025
DOI logo with link
https://doi.org/10.1075/aral.24135.tra
Abstract
Generative Artificial Intelligence (GenAI) has been offering unprecedented opportunities for language education. However, its capacity to embrace linguistic diversity, particularly for learners of dialect-rich languages like Vietnamese and Mandarin, remains underexamined. Without careful consideration, GenAI risks reinforcing language hegemonies, thereby contributing to the recolonization of language learning landscapes by marginalizing minority dialects in favour of preferred standards. Adopting sociolinguistics interview, this study explores GenAI’s (namely ChatGPT’s) ability to recognize and generate dialect-specific content in discussing several pre-determined questions in both Vietnamese dialects (i.e., Northern, Southern, and Central) and Mandarin varieties (i.e., Mainland Standard Mandarin, Taiwanese Mandarin, and Singaporean Mandarin). A multi-stage role prompt, focusing on the topic of food, was used for both Vietnamese dialects and Mandarin varieties to generate responses. Our study reveals major inconsistencies in the representation of Vietnamese dialects and Chinese varieties within AI-generated output, raising critical questions about generative AI’s role in perpetuating linguistic hierarchies. We conclude by emphasizing the need for tailored language learning approaches that leverage generative AI’s capabilities to not only accommodate but also celebrate the rich tapestry of global dialects and languages, ensuring equitable access to language education for all learners.
Article outline
  • 1.Introduction
  • 2.The capacity of GenAI in for language learning
  • 3.AI and language hegemonies on minority dialects
  • 4.Methodology
    • 4.1Vietnamese dialects and mandarin varieties
      • 4.1.1Vietnamese dialects
      • 4.1.2Mandarin varieties
    • 4.2Sociolinguistic interviews with AI
      • Step 1.Establishing dialogue context in opening prompt
      • Step 2.Presenting structured open-ended questions as prompts
    • 4.3Data analysis
  • 5.Results
    • 5.1Northern, central and southern vietnamese dialects
    • 5.2Mandarin varieties
  • 6.Discussion
  • 7.Conclusion
  • References
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Cited by (3)

Cited by three other publications

Stell, Annita, Hao Tran & Peter Crosthwaite
2025. Conducting sociolinguistic interviews via generative AI: A methods tutorial. Research Methods in Applied Linguistics 4:3 ► pp. 100223 ff. DOI logo
Dobinson, Toni, Julian Chen & Carly Steele
2024. Decolonizing or recolonizing?. Australian Review of Applied Linguistics 47:3 ► pp. 253 ff. DOI logo
Dovchin, Sender
2024. Artificial Intelligence in Applied Linguistics. Australian Review of Applied Linguistics 47:3 ► pp. 410 ff. DOI logo

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