The Role of Artificial Intelligence Technology on English Language Learning: A Literature Review
Bibliographic record
Abstract
The word (AI) stands for artificial intelligence, a computer-based simulation of human intelligence meant to act like humans. AI is one of the driving forces behind the 4.0 industrial revolution, making teaching and learning more accessible in schools. This study aims to understand the function of AI in ELT and examine AI technologies in ELT. This is a library research project. The findings indicate that AI provides a positive learning environment for learning English. Depending on the learner's current level of English, career needs, or hobbies, it has much potential to create a customized environment where students can simultaneously use their senses to learn English. AI boosts practical abilities like writing and offers a trustworthy simulation dialogue platform like spoken English. It maximizes the teaching impact of English in ELT while increasing students' practice ability. With the advancement of technology and platforms, learning English has gotten simpler. Artificial intelligence technology provides the chance to enhance English linguistic competence. Students may comprehend English more quickly because many different learning technologies are available. Students get access to a wide variety of ELT apps that are built on AI technology. These technologies include Google Translate, Text to Speech (TTS), EnglishAble, Orai, Elsa, Chatbot, Duolingo, Neo platforms, and many others. Using a method that computers and mobile devices can use, these intelligent machines can mimic intelligence and make decisions as people do.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".