The Current Status, Development Bottlenecks and Future Prospects of the Application of Artificial Intelligence in English Teaching at Basic Period from the Perspective of "Internet+"
Bibliographic record
Abstract
English teaching should be incorporated with modern information technology, especially artificial intelligence technology, so as to foster the innovative, composite and application-oriented talents with an international outlook in a more favorable manner. In the wake of the speedy development of science and technology, artificial intelligence has exhibited explosive growth in various fields, which has also presented opportunities and challenges to English teaching in basic period. On the one hand, it has exploited diversified learning paths, while on the other hand, there are also some development bottlenecks owing to the lagging technology and shortage of talents. By conducting a mixed-methods study on a sample of 4,085 students from School Q in City J, this paper presents a more comprehensive compendium and analysis of the specific application of AI technology in English teaching in basic period, in addition to researching and summarizing the merits and dilemmas of AI technology in English teaching in basic period. Moreover, it also puts forward strategies such as incorporating interdisciplinary research teams and cultivating composite talents to address the existing development bottlenecks, in an attempt to provide theoretical references for future research on the application of AI technology to the new model of English education and teaching in basic period.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| 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".