“Please Let me Use Google Translate”: Thai EFL Students’ Behavior and Attitudes toward Google Translate Use in English Writing
Bibliographic record
Abstract
The present study aims to investigate how Thai EFL university students use Google Translate (GT) in English writing, how they post-edit (PE) its outputs, and how they view GT use in English writing. The participants were 15 third-year non-English major students from three universities in Thailand. The data collection tools were an interview and two writing assignments. After the data analysis, the findings revealed the students’ behavior of GT use and their output PE as well as their attitudes toward GT use in English writing. The results reported the students always used GT in completing writing tasks at both sentence and paragraph levels, and most students did PE the outputs before applying them. However, a few students used the outputs with no PE because they trusted in GT more than they did in themselves. Regarding the PE level, the students intended to address lexical and syntax errors, so their correcting covered the light level. The results also revealed mixed messages in their attitudes toward GT use in English writing. Most students viewed GT as a helpful, reliable assistant enhancing their writing quality, but some raw GT outputs of phrases, idioms, long sentences, and paragraphs were found incomprehensible. Also, the students acquired some bad habits from using GT. However, most students disagreed with not being allowed to use GT in English writing. The study recommended language teachers to provide Thai EFL students adequate instructions for the effective use of GT and its output PE.  
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".