The Game-based Learning (GbL) Platform with Generative AI to Enhance Digital and Technology Literacy Skills
Bibliographic record
Abstract
The GbL platform, or game-based learning platform, with generative AI is a research tool initiated from the combination of game-based learning concepts and generative artificial intelligence technology; thereby, this platform is intended to be used as a guideline for the instruction management, in which learners can respond and interact with the real-time activities by means of gamification. The objectives of this research are (1) to study and synthesize the conceptual framework of the GbL platform with generative AI to enhance digital and technology literacy skills, (2) to develop the architecture of the GbL platform with generative AI to enhance digital and technology literacy skills, and (3) to study the results of the development of the GbL platform with generative AI to enhance digital and technology literacy skills. The results of this research show that (1) the overall elements suitability of the architecture of the GbL platform with generative AI is at the highest level (Mean = 4.51, SD = 0.48), and (2) the overall suitability of the architecture of the GbL platform with generative AI is at the highest level (Mean = 4.59, SD = 0.41). Nevertheless, there are still some research gaps in this study; that is, this study was conducted with quite a small sample group and it focuses mainly on the results of evaluation on the architecture of the GbL platform. Therefore, this research is regarded merely as a pilot study designated for feasibility study to further develop the GbL platform that can be put in practical use in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".