The Impact of Artificial Intelligence-Assisted Learning Applications on Oral English Ability: A Literature Review
Bibliographic record
Abstract
Artificial intelligence-assisted learning applications have shown significant potential in improving English speaking skills and promoted changes in traditional English teaching models. With the advancement of technology, more and more learners are relying on smart applications to improve their speaking skills. These applications can not only provide personalized learning plans, but also correct pronunciation and intonation through real-time feedback, thereby effectively improving learners’ pronunciation accuracy. This study investigates the impact of artificial intelligence-assisted learning applications on English speaking ability through a literature review. The purpose of this study is to understand the existing research and literature on the use of artificial intelligence-assisted learning applications in English speaking learning environments. This article first provides an overview of artificial intelligence-assisted learning applications. Then the relationship between artificial intelligence-assisted learning applications and learners' English speaking improvement is discussed from the theoretical basis. Finally, the impact of artificial intelligence-assisted learning applications on English speaking ability is studied through a literature review. The results of this literature review show that artificial intelligence-assisted learning applications have an overall positive impact on English speaking ability. But longitudinal studies are still needed to examine the long-term effects on learners' language 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".