Using Artificial Intelligence for Developing English Language Teaching/Learning: An Analytical Study from University Students’ Perspective
Bibliographic record
Abstract
As time passes on, machines are becoming more and more complex, fast-processing and intelligent. Being exactly like humans deducting, inferring and making decisions is still away, however some remarkable gains in the application of Artificial Intelligence (AI) techniques and machine learning have been recently recorded. Therefore, the current study seeks to examine strategies for effectively applying artificial intelligence (AI) applications to teach/learn English according to the university students’ point of view. The study adopts the analytical descriptive approach in order to study and analyze the literature, to describe AI and the strategies of its employment for teaching/learning English. A 40-item questionnaire was used. It covers the following fields: AI strategies and its suitable applications for teaching/learning English, the effectiveness of these applications, their practical use, and the requirements for using them in the fields of teaching/learning English. Measuring the validity and reliability of the questionnaire revealed a Cronbach’s alpha of 0.931. The study sample consisted of 44 randomly selected male students from the English language stream at Northern Border University. A set of study instruments was applied. The results revealed a group of strategies suitable for employing AI for teaching/learning English. The results also indicated a very low level of employment of these strategies for teaching/learning English, and pointed out to their effectiveness if used in this field. The study has identified the training requirements from the study sample’s point of view. A suggested plan has been envisioned that includes the basics, objectives, content, processors, and evaluation methods for the employment of AI applications in the field of English education.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".