Investigating ESL Learners’ Perception and Problem towards Artificial Intelligence (AI) -Assisted English Language Learning and Teaching
Bibliographic record
Abstract
The contemporary language learning strategy, Artificial Intelligence (AI) Assisted Language Learning (AI-ALL), incorporates AI-powered applications to support learners' learning activities. Many scholars have been experimenting with AI applications concerning activities relevant to education. The major objectives of this study are 1) The ESL learners' perspectives concerning AI-assisted English language learning and teaching; 2). ESL learners’ problems concerning artificial AI-assisted English language learning and teaching. The present investigation employed a quantitative methodology utilizing survey instruments to accumulate distinct information from 81 engineering stream students including essential primary research objects. A survey with a 5-point Likert Scale was administered to collect the data. According to the study, most of the students had favorable perceptions toward using AI-powered tools, particularly while learning English. The major problem is the lack of quality in AI-powered language-learning apps on smartphones. However, it is envisaged that AI-powered apps in language learning would be deployed as one of the instructional media that might help learners learn English as a Second Language efficiently. The present study recommends further research to investigate thoroughly how experienced language instructors use AI-powered applications in their classrooms to build best practices for utilizing AI in teaching and learning in ESL environments.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".