Leveraging Artificial Intelligence (AI): Chat GPT for Effective English Language Learning among Thai Students
Bibliographic record
Abstract
The study aimed to 1) explore the potential of Artificial Intelligence (AI) models like Chat GPT to facilitate English language learning among Thai students and 2) compare the English language learning effectiveness among Thai students after implementing artificial intelligence (AI) like Chat GPT to facilitate English language learning. The participants involved Thai students aged 19-20 from first-year pre-service teachers in Bangkok. 120 students participated, 60 in the control and 60 in the experimental group. The selection of participants was done through stratified random sampling to ensure a diverse representation of pre-service teachers with varying levels of English proficiency. They utilized a mixed-methods approach that combined qualitative and quantitative data: Standardized English tests, Chat GPT, focus group interviews, and field notes. The research findings strongly advocated integrating AI tools like Chat GPT in educational settings to facilitate more effective language learning. The study demonstrates that students who interacted with AI significantly improved their language skills. A paired t-test revealed that this difference was statistically significant (p < 0.05). Feedback from the focus group interviews indicated that students in the experimental group, after implementing artificial intelligence (AI) like Chat GPT, found the AI-based learning experience more engaging and personalized. They reported that the real-time feedback and interactive exercises offered by Chat GPT helped them understand and apply language concepts more effectively. Lastly, the attitude changes because the students had high motivation, strong self-confidence, and a positive attitude shift.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".