The Use of Artificial Cognitive Systems in Education and Science
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 article is devoted to the use of artificial cognitive systems (ICS), artificial intelligence (AI), neural networks, artificial intelligent systems (AIS) in the field of education and science. The types and types of AI technologies are presented, and specific examples from world and Russian educational practice are considered. The paper also provides an overview of existing solutions using ICS in the scientific field in the fields of physics, medicine, astronomy, ecology, and historical research. In addition to describing new opportunities and prospects for the development of artificial intelligence technologies, the article analyzes the practice of their application in order to identify shortcomings or negative impacts on the subject, including: the use of outdated data, imitation of real people, lack of responsibility, unreliable information, copyright infringement, complex algorithms. The article also discusses the threats of using artificial intelligence for humanity. The cultural and philosophical aspects of the development of information cognitive systems are analyzed separately in the context of the theory of Canadian researcher M. McLuhan about the creation of various technologies by mankind and their impact on society.
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.
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 | 0.001 |
| 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 it