Technology acceptance mediates associations between pre-service physical education teachers’ AI-TPACK and study engagement and behavioral intentions in digital teaching
Abstract
Against the backdrop of artificial intelligence (AI) reshaping the educational landscape, the digital transformation of physical education has placed greater demands on preservice physical education teachers’ (PSPETs’) capacity to integrate technology. However, research remains limited on the effects of pres-ervice physical education teachers’ artificial intelligence technological pedagogical content knowledge (PSPETs AI-TPACK) on their study engagement (SE) and behavioral intentions (BI) in digital teaching, as well as on the role played by technology acceptance (TA) in these relationships.This study involved 1,144 undergraduate and postgraduate students enrolled in physical education, physical education teaching, or physical education and training programmes at five higher education institutions in Guangzhou, Guangdong Province. Using adapted scales, data were collected on PSPETs AI-TPACK, TA, SE, and BI. Structural equation modelling (SEM) was then employed to examine the relationships among these variables and the mediating effects. The findings showed that PSPETs AI-TPACK significantly and positively predicted both SE and BI in digital teaching. TA played a significant partial mediating role in the relationships between PSPETs AI-TPACK and SE, and between PSPETs AI-TPACK and BI. These results indicate that PSPETs AI-TPACK can promote PSPETs’ SE and BI both directly and indirectly by enhancing their TA. This study extends the theoretical understanding of the relationships between PSPETs AI-TPACK and SE and BI in digital physical education teaching, while also providing empirical evidence for improving preservice physical education teacher education in higher education institutions. Higher education institutions should incorporate AI-based instructional design and practice into physical education teacher education curricula, implement tiered and progressive technology training, create authentic digital teaching contexts, and enhance PSPETs’ TA through resource provision and exemplary applications. These measures can, in turn, strengthen PSPETs’ SE and BI in using AI technologies to support physical education teaching.
Related
- Inconclusive prospective effects between technology acceptance, classroom distraction, and academic engagement among Chinese high school students: A reassessment of Li and Chen (2026)
- Beyond engagement: Interactive response systems, student attitudes, and technology acceptance in University EFL classrooms
- A Multi-Regional Study Comparing Perceived Risks between Face-to-Face versus Digital Mental Health Services - Associations with Differences in Preferences and Behavioral Intentions and the Moderation of Self-Construal: A Cross-Sectional Study
- Mega-Event Appraisals, Support, and Engagement Intentions Beyond Host Regions: Pre-Event Evidence from Two Non-Host Cities
- Immersive Technologies in Sport Spectatorship: A Scoping Review of Psychological Responses and Behavioral Intentions
BibTeX
@article{pan2026technology,
title = {{Technology acceptance mediates associations between pre-service physical education teachers’ AI-TPACK and study engagement and behavioral intentions in digital teaching}},
author = {Zhenhui Pan and Yaqin Ren and Xinglong Lin},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-78337-0},
url = {https://www.nature.com/articles/s41598-026-78337-0},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Technology acceptance mediates associations between pre-service physical education teachers’ AI-TPACK and study engagement and behavioral intentions in digital teaching
AU - Zhenhui Pan
AU - Yaqin Ren
AU - Xinglong Lin
PY - 2026
DA - 2026/10/31
JO - Scientific Reports
DO - 10.1038/s41598-026-78337-0
UR - https://www.nature.com/articles/s41598-026-78337-0
AB - Against the backdrop of artificial intelligence (AI) reshaping the educational landscape, the digital transformation of physical education has placed greater demands on preservice physical education teachers’ (PSPETs’) capacity to integrate technology. However, research remains limited on the effects of pres-ervice physical education teachers’ artificial intelligence technological pedagogical content knowledge (PSPETs AI-TPACK) on their study engagement (SE) and behavioral intentions (BI) in digital teaching, as well as on the role played by technology acceptance (TA) in these relationships.This study involved 1,144 undergraduate and postgraduate students enrolled in physical education, physical education teaching, or physical education and training programmes at five higher education institutions in Guangzhou, Guangdong Province. Using adapted scales, data were collected on PSPETs AI-TPACK, TA, SE, and BI. Structural equation modelling (SEM) was then employed to examine the relationships among these variables and the mediating effects. The findings showed that PSPETs AI-TPACK significantly and positively predicted both SE and BI in digital teaching. TA played a significant partial mediating role in the relationships between PSPETs AI-TPACK and SE, and between PSPETs AI-TPACK and BI. These results indicate that PSPETs AI-TPACK can promote PSPETs’ SE and BI both directly and indirectly by enhancing their TA. This study extends the theoretical understanding of the relationships between PSPETs AI-TPACK and SE and BI in digital physical education teaching, while also providing empirical evidence for improving preservice physical education teacher education in higher education institutions. Higher education institutions should incorporate AI-based instructional design and practice into physical education teacher education curricula, implement tiered and progressive technology training, create authentic digital teaching contexts, and enhance PSPETs’ TA through resource provision and exemplary applications. These measures can, in turn, strengthen PSPETs’ SE and BI in using AI technologies to support physical education teaching.
SN - 2045-2322
ER -