AI as an Impediment to Linguistic Creativity in English Language Learners: A Comprehensive Review of a Literature
Bibliographic record
Abstract
Artificial Intelligence (AI) in English language learning has sparked worries about how technology can affect English as a second language (ESL) learners’ creativity and deep learning processes. With the advancement of AI tools in the learning sector, ESL learners may learn the English language in entirely new ways. These AI tools make grammar correction, content creation, and writing support incredibly simple. The main issue is that these technologies may restrict learners’ creativity, ability to think critically, and ability to explore new ideas by automating language functions. Since AI tools offer standardized, formulaic recommendations, ESL learners could be dissuaded from experimenting with language or creating distinctive voices. This paper critically examines the growing concern that AI’s propensity for standardized writing may hinder ESL learners’ capacity for critical thought, original concept development, and in-depth introspection. Although AI is adept at managing writing’s technical parts, it lacks the contextual, cultural, and emotional intelligence that encourages creative expression. This review explores how AI affects creativity, highlighting its pros and cons. Besides, it advocates a comprehensive approach that uses AI to enhance human-led instruction rather than to replace it. To ensure that AI technology enhances rather than detracts from English language learners’ creative growth, this issue must be addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".