The Development of Artificial Intelligence Competency on the Flipped Classroom with Demonstration Learning Platform
Bibliographic record
Abstract
The development of artificial intelligence competency on the flipped classroom with demonstration learning platform is the study that was conducted with the ideas to enhance the quality of Thai youths so that it would align with the instruction management in the age of artificial intelligence (AI), in which a number of AI applications have been widely applied in education and learning as well as the creation of useful works. One of the most important things to be taken into account when using this modern technology is the data security and the ethical use of AI. The main objective of this research is to study the results of the development of artificial intelligence competency in terms of artificial intelligence skills and awareness of the impact of using artificial intelligence, and the satisfaction of the research participants after learning with the flipped classroom with demonstration learning platform; thereby the said participants are 32 students of Rajinibon School, derived by means of cluster sampling, all of whom are studying at grade 12 enrolled in the course “Computer for Career”. The research results show that (1) the artificial intelligence skills after learning with the flipped classroom with demonstration learning platform are higher than 80% (percentage = 81.25), (2) the awareness of the impact of using artificial intelligence after learning with the flipped classroom with demonstration learning platform is at very high level (mean = 4.60, SD = 0.04), and (3) the satisfaction after learning with the flipped classroom with demonstration learning platform is at very high level (mean = 4.66, SD = 0.02).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".