The Development and Demonstration of Creative Education Programs Focused on Intelligent Information Technology
Bibliographic record
Abstract
To appropriately react to the swift development and changes of technologies these days, the need for creative teaching and learning has been increased. Making learners equip digital literacy of intelligent information has become necessary. This paper focused on three promising technologies that artificial intelligence humanities, forensic science, and digital therapeutics from intelligent information technology. We designed educational programs and applied the programs to 596 elementary and secondary school students in Korea. The objective of these programs was to promote the creativity of learners by using numerous techniques in thinking creatively and exploring newly emerging careers in the fields of intelligent information technology. To find out the educational effect, we tested the study's subjects for their satisfaction with education and their creativity. As a result of the study, the scores regarding the satisfaction of students engaged in the programs was high (M=4.18, SD=0.48), and the score on their creativity was also high (M=4.05, SD=0.38). These educational programs also showed high satisfaction and creativity scores regardless of school level. Accordingly, we suggest that the learning contents and concepts of intelligent information technology might be worthy of being applied across elementary and secondary school practices. From the result that the satisfaction, we found that it was necessary to improve quality of the artificial intelligence humanities program. Also, supplementary and advanced related activities are needed toward enhancing learner motivation and satisfaction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".