Architecture of the Computational Thinking Platform with Gamified Using Artificial Intelligence Prompt Engineering
Bibliographic record
Abstract
The architecture of the computational thinking with gamified using artificial intelligence prompt engineering, or architecture of the CT platform with gamified, is a learning tool intended to promote activity-based learning that focuses on problem-solving by doing. This platform is fabricated with the combination of computational thinking process and gamification, which results in a new teaching method using Thailand’s popular prompt engineering “AIThaiGen”. The main target of this development is to create the learning tool that not only aligns with the Ministry of Education’s Basic Education Core Curriculum but can also promote students’ problem-solving skills and computational thinking skills, which can encourage them to produce new inventions or innovations that are beneficial to humans’ life in terms of computer science and technologies. Not only that, the learning tool initiated in this study is expected to help students solve problems in real-life and meanwhile promote their digital literacy so that they will be well equipped with bodies of knowledge and skills related to the utilization of digital media. The main objectives of this study are to promote problem-solving skills and digital literacy through the CT platform with gamified and explore the perspectives on its design in order to find out to what extent the architecture of the CT platform with gamified can improve users’ problem-solving skills and digital literacy. The research findings support this study and correspond to research hypothesis. It is found that the architecture of the CT platform with gamified comprises the process and structure that can be put in practical use in order to promote problem-solving skills and digital literacy through computational thinking processes integrated with gamification. These skills are derived from self-directed learning coupled with hands-on practices, in which learners are able to interact and have conversation with others anywhere and anytime.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".