Research on the Current Situation of Artificial Intelligence Literacy of Teacher Trainees and Strategies to Improve It
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
The era of artificial intelligence has put forward new requirements for the necessary literacy of teachers, and teacher trainees are the backbone of future teaching positions, so strengthening the cultivation of their AI literacy is an intrinsic need to realize the development of high quality in education. This study mainly adopts the questionnaire survey method and interview method to investigate 430 teacher trainees from H Teachers College, to understand the current status of the development of teacher trainees' artificial intelligence literacy, to clarify the existing problems and to propose corresponding enhancement strategies. The results of the study show that the level of teacher students' artificial intelligence literacy needs to be improved, specifically, the teacher students' artificial intelligence knowledge reserve is insufficient, the artificial intelligence ability is weak, and there is room for strengthening the artificial intelligence awareness. Based on this, the article puts forward the enhancement strategy of teacher trainees' artificial intelligence literacy, aiming to provide a certain reference basis for strengthening the cultivation of teacher trainees' artificial intelligence literacy.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it