Generative AI · Education leadership

Actionable companion · Assessment and evidence of learning

From evidence to district action: Innovative practice research of empowerment of artificial intelligence into blended teaching in exercise physiology

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 Exercise Physiology is a core course in the physical education major at higher education institutions. In the context of the modern era, leveraging generative artificial intelligence (AI) to reform its teaching framework is crucial for cultivating specialized professionals and advancing quality-oriented education. Addressing three major challenges in first-year university exercise physiology instruction—complex and interconnected teaching mechanisms that hinder systematic understanding, insufficient intuitive connections between microscopic mechanisms and macroscopic manifestations, and inadequate development of creativity-driven problem-solving skills grounded in physiological principles—the study employed AI agents and related technologies to innovate teaching philosophies, content delivery, instructional processes, and assessment methods. An innovative “three-phase, six-cycle” blended learning model was implemented. After one semester of practical implementation(16 weeks), evaluation through questionnaires and exam scores demonstrated that empowerment of Artificial Intelligence into blended teaching in Exercise Physiology significantly enhanced first-year students’ critical thinking, interdisciplinary competencies, and academic performance. These findings indicated promising applications of AI in exercise physiology education. With well - designed guidance strategies and human supervision, AI agents can serve as effective teaching assistants in higher education, freeing instructors from repetitive tasks to focus on more innovative and personalized interactive instruction. Keywords: blended teaching, exercise physiology, generative artificial intelligence, practice research, teaching innovation

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. 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.
  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. 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

Jiao Cao; Miaomiao Xu; Xiongyong Zhu; Qiusheng Long. Innovative practice research of empowerment of artificial intelligence into blended teaching in exercise physiology. Frontiers in Physiology (2026-07-29). https://doi.org/10.3389/fphys.2026.1853479

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.