Intelligent Physical Education: Utilizing Artificial Intelligence to Improve Learning Effectiveness
Bibliographic record
Abstract
Intelligent physical education represents a novel pedagogical approach leveraging artificial intelligence advancements to enhance the learning journey in physical education. With the burgeoning interest in applying AI technology within sports-related domains, educational institutions have increasingly prioritized elevating the standards of physical education instruction. In this interaction, artificial intelligence has injected new vitality into school sports, but it has also brought a series of challenges and opportunities. Among them, potential risks that require special attention include imbalanced teacher status, student intelligence dependence, and alienation of teacher-student communication. In order to effectively address these challenges, we have proposed some development strategies: Firstly, it is necessary to enhance the intelligent information literacy of physical education teachers, so that they can flexibly use artificial intelligence technology for teaching; Secondly, students should strengthen their advanced deep learning abilities, cultivate their independent thinking and problem-solving abilities, rather than relying too much on intelligent technology; Finally, it is necessary to strengthen classroom emotional interaction between teachers and students, ensuring humanized care and communication during the teaching process. By conducting in-depth research on intelligent physical education, we can better understand how to use artificial intelligence technology to improve physical education, thereby enhancing students' learning experience and effectiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".