AIED in English Preparatory Courses: A Comparative Study of UK and North American Universities and Its Impact on Chinese International Student Mobility
Bibliographic record
Abstract
With the development of AI and other computer technologies, AIED has begun to cause new reforms in the international education. At present, most Higher education institutions in European and American countries adopt AIED to varying degrees to assist and improve the teaching environment and teaching experience of international students. This article will mainly compare the application of AIED in universities in the United States, the United Kingdom, and Canada, interview Chinese students and faculty at each university through questionnaires, and analyze the interviewers’ attitudes towards AIED. This article will also collect reviews from international students and international education practitioners on some commonly used learning assistance platforms based on AI technology, such as MOOCS and Grammarly. This article decoded the collected interview comments and coded and integrated similar information as well. The final conclusion of this article is that the vast majority of educational professionals and international students believe that the benefits of AIED’s application outweigh the disadvantages, but ethical consideration could be drastically significant.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".