Exploring the Integration of ChatGPT for Teaching English in a Malaysian Primary School
Bibliographic record
Abstract
This research investigates the integration of ChatGPT, a generative artificial intelligence (AI) tool, into English language teaching in Malaysian primary schools. The main objective is to explore how teachers perceive ChatGPT in terms of its usefulness, impact on teaching effectiveness, and influence on pupil engagement. The study also aims to identify the benefits, challenges, and concerns teachers face when using AI tools in the classroom. A qualitative research design was employed, using semi-structured interviews with 10 English teachers from different primary schools. The participants shared their experiences with using AI, particularly ChatGPT, in their teaching practices. Data were analysed to identify common themes related to perceptions, implementation, and practical challenges. The findings reveal that teachers generally view ChatGPT as a helpful tool for lesson planning, generating creative teaching materials, and supporting differentiated instruction. Many teachers believe that AI can enhance classroom interaction and motivate pupils through engaging and personalized learning activities. However, several challenges were identified, including insufficient access to technology, limited digital infrastructure, lack of teacher training, and uncertainty about AI’s role in relation to traditional face-to-face instruction. Teachers also expressed concerns about over-reliance on AI and the need to maintain human elements in teaching. The study concludes that while educators are open to adopting AI tools like ChatGPT, effective integration requires proper training, policy guidance, and cultural relevance. It recommends ongoing professional development, collaboration between AI tools and conventional methods, and the development of clear guidelines for classroom use. Future research could explore the long-term impact of AI on learning outcomes, evaluate different AI tools for language teaching, and examine AI’s potential in promoting inclusive education.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".