Incorporating AI into English Language Learning: An Experimental Study Focusing on Autonomous Learning
Bibliographic record
Abstract
This study investigates the impact of integrating AI-powered tool, Plang, on English language learning among Korean EFL learners. Specifically, the study aims to examine the overall experiences of using the AI app in language learning and the impact of using the AI app on learners’ autonomous learning. The two-month study employed a qualitative data approach derived from a mixed-method study involving pre- and post-survey, reflective journals, and in-depth individual interviews. Overall, the findings have shown that the integration of the AI-powered tool into the English language learning helped learners: (i) to enhance language skills, particularly speaking proficiency; (ii) to foster learner autonomy through a personalized feedback system; and (iii) to establish a new goal that facilitates active learner engagement. The analysis also points out some challenges learners faced in the learning process. Some important implications of this study are discussed for teachers who consider integrating AI-powered tools into English language teaching. Considering that there has been little research on incorporating AI tools into classrooms, it is recommended that further research should highlight more dynamic classroom cases by developing a flipped classroom model utilizing AI tools in English language teaching contexts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".