Fostering Innovator Skills through Integration of Creative Learning and Generative AI
Bibliographic record
Abstract
This research focuses on developing innovator skills in a group of innovators in the higher education level in the field of business administration, emphasizing the use of creative learning processes to promote innovator skills. The purpose of this research is to 1) Studying the characteristics of higher education innovators. 2) Develop a conceptual framework and learning activities and 3. Examine the results of implementing the developed learning activities. This research is experimental research using pre-test and post-test research methodology with a total of 62 participants, consisting of Group A and Group B. The statistical methods used to analyze the research data consisted of descriptive statistics to describe the general characteristics of the data, including means, standard deviations, and percentages. Inferential statistics were used to compare the learners’ creative performance and innovation skills between Group A and Group B. A paired t-test was used to compare the scores before and after learning within Group A and Group B. An independent t-test was used to compare the innovation skill scores between Group A and Group B. Cohen’s d was used to calculate the effect size to assess the difference in the influence of the creative learning model combined with Generative AI on the development of innovation skills. The results showed that the average difference between the two groups, with a total mean difference of 0.073, indicated a small variance. In addition, the technology innovator skills of both groups increased statistically significantly.
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How this classification was reachedexpand
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".