DCIRV Based on AI For Ed-comm In Asia and Europe
Bibliographic record
Abstract
The development and utilization of VCDLN-TVUPI products have been successful in Indonesia, Japan, and Korea through experts from Bordeaux, France. Furthermore, research from 2025 to 2026 will build a Digital Innovation Center for the Development of VCDLN Robots (DCIRV) based on artificial intelligence to strengthen and expand future innovation. The focus of this research will include DCIRV Network Updates to the VCDLN Database Center and API; Content Recommendation Chatbot and Localization. Thus, the method that will be used is the ADDIE model. The location where DCIRV will be developed is at the Indonesian Smart Robot International Center on the UPI as host of this research, the Laboratory Study Program of Educational Technology Universitas Negeri Malang and Universitas Negeri Jakarta. To ensure the quality of research products, which include prototypes, automation systems, content development, and other delivery systems, they will be supported by experts from Bordeaux and user samples of students and teachers from France, Uzbekistan, NDHU, McGill, and Indonesia. Research products, both the DCIRV Robotics system and the resulting content products, will become learning resources that have the power of modern Pentahelix learning re-sources in supporting mobile open and distance learning services more broadly.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".