Prospects for using an artificial intelligence model as an educational platform for training microbiologists
Bibliographic record
Abstract
Problem statement. Artificial intelligence (AI) has great potential in various fields of medicine, including microbiology, but AI and educational platforms using AI are not yet sufficiently used in professional training. The research problem is relevant optimized the existing methods of training microbiologists at a university using AI models to make the student learning process more efficient, personalized and profound. Methodology . Russian and foreign studies on the use of AI in medicine and medical education were analyzed, approaches to training microbiologists to conduct high-quality laboratory research based on the use of AI as an educational platform were modeled. The authors applied advanced machine learning methods, including segmentation clustering algorithms for processing images of microbiological samples. Results . A training course has been developed and implemented Application of Artificial Intelligence in Microbiological Practice for students of additional professional education programs and students - future microbiologists, in order to equip them with knowledge and practical skills in integrating AI computing technologies into the process of analyzing microbiological samples. Theoretical and practical classes in the laboratory, an approach to sample preparation and mask creation using AI are offered. The implementation of the training course showed a high level of student’ readiness to work with AI, the relevance of the proposed educational materials and the possibility of practical application in a wide range of laboratory studies. Conclusion . The training course for students of additional professional education and students - future microbiologists developed and described in the article is a promising basis for training for a qualitative change in practical research in microbiological laboratories using AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".