The AI-Integrated English Learning of Chinese University Students: Preference, Effectiveness and Challenge
Bibliographic record
Abstract
With the wider application of artificial intelligence (AI) in education, English as a Foreign Language (EFL) learning has experienced a dramatic transformation. To better integrate new technologies with language learning, it’s crucial to conduct a thorough survey of learners’ perceptions. This study examines the perceptions, experiences, and expectations of Chinese college students regarding AI-integrated English learning methods. The study implements a mixed-methods approach. Quantitative data was collected through a questionnaire, and semi-structured interviews helped to gain qualitative data to know about students’ perceptions and preferences. Results show that most of the students hold positive attitudes towards AI-integrated learning methods, regarding AI tools as effective in improving learners’ language proficiency. However, challenges also emerged during actual application. This study also highlights students’ expectations of more advanced and personalized AI tools. This research bears significance in exploring and maximizing the effects of AI-integrated learning methods.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".