Assessing Pedagogical Strategies Integrating ChatGPT in English Language Teaching: A Structural Equation Modelling-Based Study
Bibliographic record
Abstract
This research investigates the impact of AI-powered ChatGPT technology on current pedagogical strategies, focusing on student engagement, language proficiency, and perceived usability. Data was collected through structured surveys from 301 English language learners at Ajman University (49.9%) and University of Hail (50.1%) in the United Arab Emirates and Saudi Arabia respectively. Grounded in constructivist learning theory, the results reveal strong positive outcomes. The integration of ChatGPT into pedagogical strategies significantly enhances student engagement, improves language proficiency (β=0.698, p<0.000), and increases perceived usability among English language learners in higher education institutions in the UAE and Saudi Arabia. Respondents reported that these strategies made lessons more engaging and interactive, improved their language skills, and provided a user-friendly learning interface. These findings underscore the potential of AI tools like ChatGPT to revolutionize language education by offering interactive, personalized, and effective learning experiences. Additionally, the study supports the principles of constructivist learning theory, highlighting students’ positive attitudes and perceptions towards the integration of ChatGPT-enhanced pedagogical strategies,. Practical recommendations and study limitations are also discussed.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".