Shifting Roles: Employing AI-driven Translation Engines to Enhance the Writing Proficiency of EFL Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it