Utilization of Artificial Intelligence Tools in Fostering English Grammar and Vocabulary among Omani EFL Learners
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly being integrated into language teaching and learning, offering numerous benefits and opportunities, especially for enhancing learning and teaching English as a Foreign Language (EFL). Many studies have investigated the impact of AI in EFL contexts. However, few studies investigated the impact of AI tools on learning English grammar and vocabulary, especially in the Arab context. Therefore, this study investigates the impact of AI tools in fostering Omani EFL learners’ grammar and vocabulary. The data were collected from 160 Omani EFL students enrolled at a branch of Omani Government University, using a survey. They were selected from the fourth level (Foundation Program) and students of the English General Requirements (Post-Foundation Program). Findings revealed that Omani EFL learners had positive perceptions towards AI tools for fostering their English grammar and vocabulary skills. In terms of differences between the study groups and variables, there was a significant difference between the learners' views on the impact of AI tools on improving English grammar and vocabulary. There was no significant difference between the views of the students of the two levels towards the impact of AI tools in fostering English grammar and vocabulary. On the other hand, a significant difference existed between the perspectives of those learners who use AI tools and those who do not use AI tools. In terms of correlation, there was no correlation between the level of study and the learners' views about using AI tools. However, a correlation was found between the actual use of AI tools and the learners' perspectives of using them to foster English grammar and vocabulary. Overall, these findings lead to some recommendations and suggest avenues for upcoming research in this innovative area.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".