Evidence map›Paper›PMID 42591087›Full record

ArticleFrontiers in physiology2026

Innovative practice research of empowerment of artificial intelligence into blended teaching in exercise physiology.

Jiao Cao, Miaomiao Xu, Xiongyong Zhu, Qiusheng Long

Abstract read
In one paragraph

Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiao CaoCenter of Teacher's Teaching Development, Guangdong University of Education, Guangzhou, Guangdong, China.
Miaomiao XuSchool of Physical Education, Guangdong University of Education, Guangzhou, Guangdong, China.
Xiongyong ZhuSchool of Computer Science and Artificial Intelligence, Guangdong University of Education, Guangzhou, Guangdong, China.
Qiusheng LongSchool of Physical Education, Guangdong University of Education, Guangzhou, Guangdong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

blended teachingexercise physiologygenerative artificial intelligencepractice researchteaching innovation

Identifiers

PMID42591087
PMCPMC13461403

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.