The Inquiry Based Learning Platform with Generative Artificial Intelligence to Promote Remembering and Understanding Skills for Dental Public Health Students
Bibliographic record
Abstract
The objective of this research is to develop the architecture of the inquiry based learning platform with Generative AI (IBL platform with Gen-AI) in order to promote remembering and understanding skills for dental public health students. The platform developed in this research is based mainly on the principles of inquiry based learning, which consists of five steps (i.e., engagement, exploration, explanation, elaboration, and evaluation), combined with the technology of Generative AI. Thus, the platform herein is capable of creating new and unprecedented contents by means of the learning style that focuses mainly on participatory learning. It is expected that this method of learning assists learners in the enhancement of the skills related to thinking, remembering, and understanding, which can further promote intelligence quotient in terms of cognitive domain, highly necessary in the learning society in this digital era. The suitability of the architecture of the IBL platform with Gen-AI was assessed by nine experts with specialized in the dental anatomy, and information technology. The research results show that the overall suitability of the elements towards the architecture of the IBL platform with Gen-AI is at highest level. It can be summarized that the guideline to further develop the IBL platform with Gen-AI to promote remembering and understanding skills for dental public health students through web applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".