Development and Application of Elementary School AI Education Program Using the International Baccalaureate (IB) Primary Years Programme (PYP) Approach
Bibliographic record
Abstract
The objective of this study is to enhance elementary school students' foundational understanding of artificial intelligence (AI) and to foster their Computational thinking. This goal was realized through the creation of an AI education program integrating the ADDIE model and the International Baccalaureate (IB) Primary Years Programme (PYP) teaching methodology. Before developing the educational program, we conducted a preliminary needs analysis with 60 fifth-grade students from IB World School P Elementary and 36 staff members, aligning with the stages of the ADDIE model. Drawing from the outcomes of this preliminary needs analysis, we opted for the transdisciplinary theme 'How the world works,' as it resonated most aptly with AI-related content, as determined by participating educators. Real-life AI-based concepts were seamlessly woven into the educational material. Throughout the program, students actively engaged in exploratory activities centered on the chosen transdisciplinary theme and central concept. Collaborating on team projects, they collectively tackled problem-solving processes, completing activities and assignments aimed at fostering self-directed learning. To assess the effectiveness of the developed educational program on students' computational thinking, pre- and post-tests were administered. Validation results underscored that the program made a significant contribution to the enhancement of Computational Thinking among the participating students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".