Exploring the impacts of an AI-driven instructional intervention on Iranian EFL learners’ pronunciation skill development
Bibliographic record
Abstract
The integration of artificial intelligence (AI) into language education is rapidly transforming instructional practices and learner engagement. Within the domain of second language acquisition, pronunciation plays a crucial role in achieving communicative competence and intelligibility. Recent advancements in AI technologies offer promising opportunities to support pronunciation instruction by providing immediate, individualized, and low-anxiety feedback. This study investigated the effectiveness of AI-driven tools, specifically ChatGPT, in improving the pronunciation accuracy of Iranian EFL learners through a randomized controlled trial. Sixty intermediate learners were randomly assigned to either an experimental group, which practiced pronunciation using ChatGPT, or a control group, which relied on electronic dictionaries. Pronunciation performance was assessed over three phases: pre-test, post-test, and delayed post-test. A repeated measures mixed ANOVA was employed to evaluate group differences and changes over time. Results indicated that the ChatGPT group demonstrated significantly greater improvements in pronunciation accuracy, with gains sustained over time. These findings highlight the potential of interactive AI tools to support both immediate learning and retention in pronunciation instruction and offer pedagogical insights into how AI tools can be meaningfully integrated into EFL pronunciation instruction to promote learner autonomy and retention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".