Development of Digital English Education in the Context of Artificial Intelligence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital English education has entered a new era before the emergence of artificial intelligence (AI). The adoption of AI teaching into China's teaching reform has become inevitable. AI has effectively integrated and shared educational resources in China. This paper aimed to study how to analyze and research the development of digital English education based on AI. At the same time, Analytic Hierarchy Process (AHP) was used to evaluate AI's promotion of digital English education. After the questionnaire survey was distributed to 500 students, this paper analyzed 488 valid questionnaires. Among the tendencies in English education, 85.04% of students chose to use multimedia equipment to assist teaching. It can be seen that multimedia assisted instruction has become the first choice for most students. In addition, a questionnaire survey was conducted among teachers of English majors. Among 200 valid questionnaires, 59.00% of teachers chose to show their learning content for the purpose of using multimedia materials. It can be seen from this that most of the teachers in the school are still at the stage of displaying the content. To sum up, in order to enable students to better accept digital teaching, their initiative and enthusiasm to better use and manage digital resources and other related information technologies and carry out independent and effective learning in a digital environment has been enhanced, so that they can better use and manage digital resources and other related information technologies to understand their own learning in a digital environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it