Development and Application of Elementary School AI Education Program Using the International Baccalaureate (IB) Primary Years Programme (PYP) Approach
Why this work is in the frame
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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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it