Evidence map›Paper›PMID 42774481›Full record

ArticleFrontiers in public health2026

Artificial intelligence for university physical education: a data-knowledge synergy digital-intelligent sports platform.

Jiacheng Feng, Meijie Yao, Xueying Feng, Chenrui Wang, Hongmiao Chen, Dong Li, Qimeng Niu

Abstract read
In one paragraph

Article in Frontiers in public health, 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

7 authors.

Jiacheng Feng *Department of Physical Education, Tianjin University of Technology, Tianjin, China.
Meijie Yao *Physical Education Teaching and Research Department, Nanqiao Middle School, Jinjiang, China.
Xueying FengSchool of Innovation and Technology, The Glasgow School of Art, Glasgow, United Kingdom.
Chenrui WangSchool of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin, China.
Hongmiao ChenCollege of Physical Education, Huaqiao University, Quanzhou, China.
Dong LiSchool of Physical Education and Health, Zhaoqing University, Zhaoqing, China.
Qimeng NiuDepartment of Physical Education, Tianjin University of Technology, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents the design, construction, and preliminary evaluation of a university-oriented digital-intelligent sports platform integrating a large language model with retrieval-augmented generation (RAG), campus data services, and domain knowledge resources. The platform architecture comprises technical, knowledge, operational-data, and user-application components supporting teaching assistance, exercise guidance, competition organization, and health-related information services. A formative campus deployment involving 428 students and six instructors documented implementation feasibility and user-facing application scenarios. To provide a more rigorous empirical supplement, an independent six-week, non-randomized, intact-class quasi-experiment was conducted with 196 students in four physical-education classes. Two classes received platform-supported instruction and two received conventional instruction from the same instructor. Baseline-adjusted analyses indicated higher posttest basic psychological need satisfaction in the platform-supported group (adjusted difference = 0.618, 95% CI 0.506-0.730,

Indexed as

Artificial IntelligencePhysical Education and TrainingSportsFemaleGenerative Artificial IntelligenceHumansMaleStudentsUniversitiesYoung Adultartificial intelligencebasic psychological needsdigital-intelligent sportsretrieval-augmented generationuniversity physical education

Identifiers

PMID42774481
PMCPMC13593897

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.