MAINSTREAMING OF LEGAL ISSUES OF THE USE OF ARTIFICIAL INTELLIGENCE IN THE EDUCATION SYSTEM OF UZBEKISTAN
Bibliographic record
Abstract
The main target of this research is an attempt to update the issues of legal support for the use of artificial intelligence in education in Uzbekistan. Artificial intelligence can positively influence the development of society, but also cause serious harm if a system of legal and regulatory rules is not established in a timely manner. In this regard, it is necessary to develop specific recommendations, especially those related to the educational sphere, and strictly take into account several important aspects: understanding of artificial intelligence, consideration of ethical standards, data protection mechanisms, training of teaching staff, as well as responsibility for wrong decisions, and protecting the rights of students. Attention is drawn to the study of worldwide experience in the preparation of a law on artificial intelligence in developed countries: the United States, the European Union, Japan and Canada. It is emphasized that the successful development of artificial intelligence is the state support and civil society.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".