LLM-Driven Multi-Agent Architecture for Automated Physical Education Instruction
Bibliographic record
Abstract
This study proposes an LLM-based architectural framework tailored for physical education, enabling a fully automated computational workflow through a multi-agent system design. The framework integrates four specialized processing components: a Retriever module that performs adaptive knowledge extraction using dynamic weighting strategies, a Reflector module conducting multidimensional confidence assessment to ensure contextual accuracy, parallel Answerers employing structured reasoning techniques to preserve logical coherence in generated outputs, and an Improver module responsible for multi-constraint optimization to verify output quality. Experimental results demonstrate improved processing efficiency and higher-quality outputs attributable to the integrated pipeline architecture. The framework achieves greater content relevance and sustains robust performance under dynamic educational conditions. By integrating retrieval-augmented generation with iterative refinement processes, the system establishes a self-correcting mechanism that significantly enhances the reliability and operational effectiveness of AI-supported platforms for psychomotor skill development.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".