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A Systematic Review of Machine-Translation-Assisted Language Learning for Sustainable Education

Author

Listed:
  • Xinjie Deng

    (Faculty of Foreign Studies, Beijing Language and Culture University, Beijing 100083, China)

  • Zhonggen Yu

    (Faculty of Foreign Studies, Beijing Language and Culture University, Beijing 100083, China)

Abstract

With the rapid development of artificial intelligence, machine translation (MT) has gained popularity in recent years. This study aims to present a systematic review of literature on MT-assisted language learning in terms of main users, theoretical frameworks, users’ attitudes, and the ways in which MT tools are integrated with language teaching and learning. To this end, relevant peer-reviewed articles ( n = 26) were selected through the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) for further analysis. The findings revealed that the main MT users were undergraduate and graduate students. Both teachers and students held mixed attitudes for different reasons. It was also found that MT integration followed four steps, i.e., introduction, demonstration, task assignment, and reflection. The procedures of MT integration could be updated and perfected by introducing other features in the future.

Suggested Citation

  • Xinjie Deng & Zhonggen Yu, 2022. "A Systematic Review of Machine-Translation-Assisted Language Learning for Sustainable Education," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7598-:d:844924
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    References listed on IDEAS

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    1. Xinyi Huang & Di Zou & Gary Cheng & Haoran Xie, 2021. "A Systematic Review of AR and VR Enhanced Language Learning," Sustainability, MDPI, vol. 13(9), pages 1-28, April.
    2. Emily Hellmich & Kimberly Vinall, 2021. "FL Instructor Beliefs About Machine Translation: Ecological Insights to Guide Research and Practice," International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), IGI Global, vol. 11(4), pages 1-18, October.
    3. Ming Li & Zhonggen Yu, 2022. "Teachers’ Satisfaction, Role, and Digital Literacy during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
    4. Zhonggen Yu, 2022. "Sustaining Student Roles, Digital Literacy, Learning Achievements, and Motivation in Online Learning Environments during the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(8), pages 1-14, April.
    5. Vassil Kirov & Bagryan Malamin, 2022. "Are Translators Afraid of Artificial Intelligence?," Societies, MDPI, vol. 12(2), pages 1-14, April.
    6. Ramunė Kasperė & Jolita Horbačauskienė & Jurgita Motiejūnienė & Vilmantė Liubinienė & Irena Patašienė & Martynas Patašius, 2021. "Towards Sustainable Use of Machine Translation: Usability and Perceived Quality from the End-User Perspective," Sustainability, MDPI, vol. 13(23), pages 1-17, December.
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    Cited by:

    1. Carlos Cacciuttolo & Yaneth Vásquez & Deyvis Cano & Fernando Valenzuela, 2023. "Research Thesis for Undergraduate Engineering Programs in the Digitalization Era: Learning Strategies and Responsible Research Conduct Road to a University Education 4.0 Paradigm," Sustainability, MDPI, vol. 15(14), pages 1-27, July.
    2. Inês Carvalho & Ana Ramires & Montserrat Iglesias, 2023. "Attitudes towards machine translation and languages among travelers," Information Technology & Tourism, Springer, vol. 25(2), pages 175-204, June.
    3. Wenhe Min & Zhonggen Yu, 2023. "A Bibliometric Analysis of Augmented Reality in Language Learning," Sustainability, MDPI, vol. 15(9), pages 1-15, April.

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