The Inquiry Based Learning Platform with Generative Artificial Intelligence to Promote Remembering and Understanding Skills for Dental Public Health Students
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 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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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 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