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

Xinjie Deng and Zhonggen Yu
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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

Sustainability, 2022, vol. 14, issue 13, 1-15

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.

Keywords: machine translation (MT); MT tools; language learning; users’ attitudes; systematic literature review (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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