Innovation and Reconstruction of Early Childhood Education Models Driven by Artificial Intelligence Technology
Bibliographic record
Abstract
The rapid advancement of artificial intelligence (AI) is fundamentally reshaping global educational ecosystems, with early childhood education—the cornerstone of lifelong learning—undergoing unprecedented structural transformations. This study employs sociotechnical theory and educational ecology frameworks to analyze AI's innovative applications in preschool settings, revealing its profound impacts on pedagogical restructuring, teacher-child relationship evolution, and value system shifts. Key findings demonstrate that intelligent educational robots, virtual reality (VR) learning environments, and adaptive learning systems transcend traditional spatiotemporal boundaries, enabling data-driven personalized education. However, challenges such as algorithmic bias exacerbating educational inequity, privacy risks in child data management, and emotional interaction deficits demand urgent resolution. The proposed "technology-education-ethics" collaborative governance framework emphasizes child-centered values, advocating for legislative safeguards, teacher competency enhancement, and multi-stakeholder engagement to ensure sustainable development in AI-integrated preschool ecosystems.
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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 | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".