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Machine translation and language teaching and learning

Published online by Cambridge University Press:  04 March 2025

Jason Jolley  and
Luciane Maimone
Jason Jolley*
Affiliation:
Missouri State University, Springfield, MO, USA
Luciane Maimone
Affiliation:
Missouri State University, Springfield, MO, USA
*
Corresponding author: Jason Jolley; Email: jasonjolley@missouristate.edu
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Extract

Decades before educators were forced to confront the disruption posed by widely accessible generative artificial intelligence (AI) tools such as ChatGPT, language learners, instructors, and researchers began dealing with its game-changing predecessor: machine translation (MT). Researchers began assessing MT systems and proposing language teaching applications for them as soon as universities and schools gained access to them in the mid-1980s (*Anderson, 1995*; Ball, 1989*; Corness, 1985; French 1991; Lewis, 1997; Richmond, 1994*). These inquiries accelerated in the early 2000s, when internet-enabled computer labs and increasingly smarter devices put free online MT services such as Babel Fish and Google Translate (GT) at students' fingertips, triggering concerns over output quality, academic dishonesty, and the short-circuiting of actual learning. In recent years, there has been a veritable explosion of research on MT's role in and impact on language teaching and learning, with many dozens of peer-reviewed articles published in the past five years alone, as documented in a handful of comprehensive literatures reviews (Gokgoz-Kurt, 2023; Jiang et al., 2024; Jolley & Maimone, 2022; Klimova et al., 2023; Lee, 2023). The present article provides a timeline of this rapidly expanding research domain.

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Type
Research Timeline
Information
Language Teaching , Volume 58 , Issue 2 , April 2025 , pp. 173 - 191
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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Footnotes

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