Exploration and Research on the New Ecosystem of Artificial Intelligence + Security Education
Bibliographic record
Abstract
The development of artificial intelligence has brought unprecedented opportunities for security education in universities. The author combines artificial intelligence with security education to explore the feasibility and security measures of building a new ecosystem of Artificial Intelligence + Security Education. Artificial intelligence has unique advantages in developing intelligent security education courses, improving teaching methods, and enhancing teaching evaluation. While leveraging its advantages, there are also many challenges. One of them is the low degree of integration between education and information technology, mainly manifested in the large gap between teaching concepts and artificial intelligence basic technologies, and technological development is at a bottleneck stage. Additionally, as data security and privacy protection issues become increasingly prominent, it is necessary to establish sound data security protection measures and privacy protection systems to securely and effectively protect learners' personal privacy and behavioral data. Based on the above issues, the author proposes the following measures for improvement. First, the security education faculty team should be improved and the cultivation of educational work philosophy should be strengthened. Second, the security education system construction should be continually improved in accordance with data security and privacy protection requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| 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 | 0.001 |
| 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".