Development of Educational Management Concepts and Models in the Era 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
The development of artificial intelligence (AI) technology has provided new ideas and methods for educational management (EM). Studying the development of EM concepts and models in the era of AI helps to explore innovative paths in EM, promote the modernization and intelligent development of EM, provide guidance for educational managers, and promote innovation in EM concepts and models. This article first analyzes the comparative development of educational concepts, selecting personalization, educational resources, and educational evaluation to describe. Then, it analyzes the development of educational models, selecting management methods, participating in management, and comparing and explaining the construction of educational informatization. It then highlights the application of AI in EM, and finally conducts experimental analysis on different educational concepts and models. The results indicate that compared to traditional EM concepts and models, the EM concepts and models in the era of AI have significantly improved students' academic performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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