Framework of Artificial Intelligence Learning Platform for Education
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
Nowadays, Information Technology is an integrated as a part of our life activities. It does not affect only teaching and learning methods at all levels, but also the teaching styles of each teacher with suitable for the digital age. Therefore, the standardized platform should create for all teachers to effectively serve the future education policy. This research aims to synthesize and develop a framework of an artificial intelligence learning platform for education and estimate the framework’s suitability. The research is discussed into three phases: 1) synthesizing an intelligent learning platform by using Artificial Intelligence (AI), 2) developing a framework of an artificial intelligence learning platform for education, and 3) evaluating the suitability of the framework by 15 experts. The result found that the suitability evaluation of the framework of an artificial intelligence learning platform for education was very good. The results showed that this framework could develop a learning platform for preparing transformation to the digital age.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it