Generative AI · Education leadership

Actionable companion · Instructional improvement

From evidence to district action: AI-assisted learning in exercise physiology: a quasi-experimental study using PhysioExercise GPT

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.

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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 This study evaluated the applicability and effectiveness of PhysioExercise GPT, an artificial intelligence tool based on the ChatGPT architecture, developed to support the teaching of exercise physiology to undergraduate physical education students. A longitudinal quasi-experimental study was conducted involving a total of 64 students. Thirty-two students used the AI tool through a structured educational approach that incorporated personalized support, immediate feedback, and learning activities based on Bloom’s Taxonomy, while a control group of 32 students received traditional instruction. The results showed that both groups improved over time; however, the AI-assisted group achieved significantly greater learning gains (p = 0.002). The intervention group demonstrated a mean improvement of 3.38 points compared with 1.97 points in the control group, corresponding to a large effect size (Hedges’ g = 0.80). Analysis of covariance (ANCOVA) confirmed that the superiority of the AI-assisted group remained significant after adjusting for baseline performance. The findings indicate that the structured use of PhysioExercise GPT functions as an effective digital tutor, promoting active learning and enhancing the retention of complex concepts. Although the study has limitations, including the absence of randomization, the results suggest that AI represents a robust complementary educational tool capable of improving academic performance in health education. Keywords: artificial intelligence, educational technology, exercise physiology, health education and awareness, teaching

A 90-day district pilot

  1. 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.
  2. Days 16–30 · Design. Choose one instructional unit and a specific teacher-supervised AI use. Compare it with the existing approach using a common learning objective, equivalent tasks and a shared rubric. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. 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.
  4. Days 61–90 · Decide. Measure unaided learning on a new task, factual errors, student workload and teacher review time. Disaggregate results where group sizes permit responsible interpretation. 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

Roque Ribeiro Da Silva Junior; Larissa Nayara de Souza; Gilson Aquino Cavalcante; Fernando Libralino Fernandes; José Antônio da Silva Júnior; Rackel Gurgel Hipólito; Fausto Guzen; Edson Fonseca Pinto; Maria Irany Knackfuss; Thales Allyrio Araújo De Medeiros Fernandes. AI-assisted learning in exercise physiology: a quasi-experimental study using PhysioExercise GPT. Frontiers in Physiology (2026-09-08). https://doi.org/10.3389/fphys.2026.1894046

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.