English Language Learning with AI: Proficiency Gains and Learner Experience
Bibliographic record
Abstract
This study investigates the effectiveness of an AI-assisted language learning platform compared to traditional instruction among first-year Saudi university students. Using a quasi-experimental mixed-methods design, the study assigned 147 students to either an AI-assisted group or a traditional classroom group over a six-week period. Quantitative data from pre- and post-tests (TOEFL ITP) revealed significantly greater gains in the AI group (d = 0.85), even after controlling for baseline proficiency. Regression analysis showed that time spent on the AI platform was a strong predictor of learning gains, with each additional 30 minutes of usage correlating with a 1.8-point improvement. Qualitative data from 30 post-intervention interviews highlighted the perceived benefits of immediate feedback, gamified motivation, and self-paced learning, alongside reported challenges such as streak-related anxiety, accent misclassification, and technical issues. Findings support a blended learning model that uses AI to give students consistent feedback, while keeping teachers focused on guiding learning and supporting students emotionally and socially. The study contributes practical and theoretical insights relevant to policymakers, curriculum designers, and educators seeking to implement AI in EFL contexts.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".