Implementation of an oral dialogue based on a neural network of artificial intelligence in the online platform in secondary school
Bibliographic record
Abstract
Today in society there is a need for artificial intelligence that teaches schoolchildren through oral communication, questions and answers. In the first quarter of the 2020-2021 academic year, secondary school teachers in Kazakhstan spent more time checking students' written responses compared to traditional teaching in previous years. At this time, the teachers noted that there was not enough time to verbally explain the lessons and provide high-quality oral feedback to all students. To solve this problem, we propose to use an artificial intelligence neural network to verbally explain the topic of the lesson in an online learning platform. The proposed article analyses and investigates the methodology for the implementation of oral teaching of schoolchildren using artificial intelligence neural networks in online learning in general education schools. The neural network recognises a school textbook and processes it in a text and audio editor. oral explanation to the student of the theoretical material from the textbook alternates with the assessment of the student's voice response. An online demonstration of ways to solve programming problems is carried out in an online editor installed on an online platform. The proposed online platform can be effectively used in traditional face-to-face education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".