Actionable companion · Assessment and evidence of learning
From evidence to district action: Augmentation or substitution: defining role of large language model in physical education
AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.
Evidence and context
Confirm the study population, setting and task before applying its findings to your district.
Original authors’ abstract, reproduced under the linked source license:
Abstract Introduction Large language model has seen rapid uptake in education alongside advances in artificial intelligence. Its capabilities in areas such as healthcare planning and question answering suggest strong potential for supporting personalised instruction in physical education. At the same time, studies have raised concerns regarding safety, accuracy, and contextual appropriateness. This study examines the role of large language model in physical education and evaluates its suitability as an assistive tool or possible alternative to human instruction. Methods This study conducted a questionnaire—based evaluation comparing three generative approaches for producing physical education lesson plans and teacher responses: (1) plans written directly by a large language model, (2) plans produced by a large language model after being provided with domain resources, and (3) plans created collaboratively by physical education teachers together with the large language model. Feasibility, practicality, safety, adaptability, and content quality were assessed across these approaches. Large language model—generated responses in typical physical education scenarios were further evaluated in terms of response accuracy, safety, clarity, adaptability, guidance quality, acceptability to learners, and perceived potential to enhance teacher response efficiency. Results Lesson plans produced collaboratively by physical education teachers and the large language model outperformed those produced solely by the large language model—both with and without additional resources—across all evaluated dimensions. In addition, responses generated by the large language model in physical education scenarios were rated highly for clarity, guidance, adaptability, and support for teacher efficiency. Discussion The large language model demonstrates clear value in physical education, particularly as a means to support and augment instructional design and responsiveness. However, it does not yet replace the pedagogical judgment, contextual awareness, and safety oversight of human instructors. Overall, this study concludes that the large language model is best positioned as an assistive tool to optimise teaching practice rather than as a standalone replacement for human teachers. Keywords: large language model, physical education, artificial intelligence, assistive tools, sustainability teaching
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Redesign one low-stakes assessment: require a first attempt, an AI-assisted revision, a source-check log and a short oral explanation. Use the same rubric for the comparison task. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Blind-score reasoning, factual accuracy and unaided transfer on a fresh task. Record suspected misconduct separately from demonstrated evidence. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Huijun Yang; Bingyi Huang; Xu Li. Augmentation or substitution: defining role of large language model in physical education. Frontiers in Sports and Active Living (2025-11-12). https://doi.org/10.3389/fspor.2025.1662056
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.