Language Instructors’ Beliefs and Coping Mechanisms on the Use of Machine Translation: Cautions and Concerns

Machine translation (MT) is widely used in second or foreign language learning and is one of the most used language learning applications among tertiary learners. However, problems arise when learners are unable to utilise machine translation effectively, necessitating important instructor guidance. The present study aimed to explore university instructors’ beliefs about learners' use of machine translation in second or foreign language writing tasks and assessments and to investigate the extent to which teaching strategies and assessments are adapted in view of its widespread use. Quantitative and qualitative data were collected from 127 instructors at an institution of higher learning via questionnaire and semi-structured interview. Findings revealed that instructors concur with the use of machine translation as a useful auxiliary learning tool, as long as it is used with care and caution within clear guidelines and framework. Although the instructors have also made some adjustments to their teaching, their coping mechanisms are not sustainable owing to lack of clear guidelines on the use of machine translation in language learning. It is suggested that learner guidance and clear institutional guidelines be provided for optimal use of machine translation in language learning.

Authors

Institutions

Publication Details

Journal
Theory and Practice in Language Studies
Published
2025-02-01
DOI
https://doi.org/10.17507/tpls.1502.05
Citations
1
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Language Instructors’ Beliefs and Coping Mechanisms on the Use of Machine Translation: Cautions and Concerns

Ya Ling Lew, Naginder Kaur, Huzaifah A Hamid, Foo Terng Hoe
1 citations
Theory and Practice in Language Studies
Natural Language Processing Techniques
2.48
article

Language Instructors’ Beliefs and Coping Mechanisms on the Use of Machine Translation: Cautions and Concerns

Ya Ling Lew, Naginder Kaur, Huzaifah A Hamid, Foo Terng Hoe
article en
1 citations

Abstract

Machine translation (MT) is widely used in second or foreign language learning and is one of the most used language learning applications among tertiary learners. However, problems arise when learners are unable to utilise machine translation effectively, necessitating important instructor guidance. The present study aimed to explore university instructors’ beliefs about learners' use of machine translation in second or foreign language writing tasks and assessments and to investigate the extent to which teaching strategies and assessments are adapted in view of its widespread use. Quantitative and qualitative data were collected from 127 instructors at an institution of higher learning via questionnaire and semi-structured interview. Findings revealed that instructors concur with the use of machine translation as a useful auxiliary learning tool, as long as it is used with care and caution within clear guidelines and framework. Although the instructors have also made some adjustments to their teaching, their coping mechanisms are not sustainable owing to lack of clear guidelines on the use of machine translation in language learning. It is suggested that learner guidance and clear institutional guidelines be provided for optimal use of machine translation in language learning.

Theory and Practice in Language StudiesVol. 15(2)
Universiti Teknologi MARA (MY)
Universiti Teknologi MARA
Openalex Percentile: Top 12%
Natural Language Processing Techniques
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.