Evaluating AI Applications for Enhancing Listening Comprehension among EFL students in English language in Real-World Scenarios
Bibliographic record
Abstract
This paper assesses the extent to which Artificial Intelligence technologies improve listening comprehension in English among foreign language learners in Saudi Arabia. In this study, both the quantitative assessment of the changes in the listening comprehension levels of the participants as well as qualitative feedback that focused on the use of AI applications by the learners were collected. It was established that listening comprehension improved, particularly in the contextualized area, and that learner engagement and motivation also improved. At the same time, difficulties connected with accent recognition, technical problems, and the lack of depth in the comments were also discussed. This is the main lesson from this study about AI, in applications, much as the tools are useful and provide great benefits, they should be used as a supplement rather than a substitute to traditional methods. Further works should be aimed at enhancing the AI in applications to various linguistic contexts and enhancing the feedback.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".