Shifting Roles: Employing AI-driven Translation Engines to Enhance the Writing Proficiency of EFL Learners
Bibliographic record
Abstract
The simulation of human intelligence processes by computer software and internet MT engines has become apparent in education recently. Neural MT engines manipulate artificial intelligence to produce comprehensive results in translation. Thus, the regular role of such MT engines is prominent in translation among languages. Differently, the present study shifts the regular role of neural MT engines from translation to developing writing proficiency among EFL learners. A sample of EFL learners at Qassim University used neural MT engines that manipulate artificial intelligence to develop their writing proficiency during the academic year 2024. EFL learners’ writings were evaluated through electronic proofreading software. Gains in writing skills like spelling, construction, concordance, and meaning are documented in the present study. The pre-post comparison of the writings of the study group had significant differences in favor of implementing artificial intelligence-based MT engines. The present study recommends implementing neural MT engines in writing classrooms to develop EFL learners’ writing proficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".