Research on Excellent Cases of "Artificial Intelligence + Higher Education" Application Scenarios in Chinese Universities
Bibliographic record
Abstract
Currently, digital technology is becoming a leading force driving global education reform. The integration of artificial intelligence and education has brought opportunities for innovation and improvement in education. The level of AI ability of teachers and students determines the level of digitalization and intelligence in the development of higher education. This study applied the UNESCO “AI competency framework for teachers” and “AI competency framework for students” to analyze 18 excellent cases of "Artificial Intelligence + Higher Education" announced by The Ministry of Education of the People’s Republic of China. The case reflects the comprehensive integration of artificial intelligence technology into the development of higher education. This study analyzes the specific application scenarios of artificial intelligence in the process of higher education and examines the AI proficiency levels of university teachers and students. This study points out the direction for the development of teachers and students' abilities, and provides suggestions for the development framework of AI abilities for future university students.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".