Scholarlink

Background <p>In the age of Artificial Intelligence (AI), physical education is undergoing tremendous changes. For ethnic students, it is even more important to adapt to challenges, maintain mental health, and improve their digital resilience (DR) in the complex digital environment. However, research on the DR of university students in ethnic minority areas is still very limited.</p> Methods <p>In this study, the PLS-SEM method was used to reveal the factors influencing the use of Generative artificial intelligence (GenAI) in university students’ behavioural intention (BI) based on the UTAUT model and their interrelationship with the mediating variable DR. A total of 803 valid data were collected and analyzed using SmartPLS 4 software. </p> Results <p>The study found that performance expectancy (PE) and effort expectancy (EE) had a positive and significant impact on the DR of ethnic students in the physical education classroom using GenAI, and DR had a very strong positive impact on BI.</p> Conclusions <p>This study offers practical implications for course design and student support, providing empirical references for integrating GenAI into university physical education courses and fostering students’ DR within regional higher education contexts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

How does digital resilience affect the behavioral intention to use generative AI among university students in physical education courses? A quantitative study in Chinese ethnic regions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月9日

查看全文
  • He Liu,
  • Long Chen,
  • Jiong Zheng,
  • Jiarui Liu,
  • Yingxuan Li,
  • Ying Ma

摘要

Background

In the age of Artificial Intelligence (AI), physical education is undergoing tremendous changes. For ethnic students, it is even more important to adapt to challenges, maintain mental health, and improve their digital resilience (DR) in the complex digital environment. However, research on the DR of university students in ethnic minority areas is still very limited.

Methods

In this study, the PLS-SEM method was used to reveal the factors influencing the use of Generative artificial intelligence (GenAI) in university students’ behavioural intention (BI) based on the UTAUT model and their interrelationship with the mediating variable DR. A total of 803 valid data were collected and analyzed using SmartPLS 4 software.

Results

The study found that performance expectancy (PE) and effort expectancy (EE) had a positive and significant impact on the DR of ethnic students in the physical education classroom using GenAI, and DR had a very strong positive impact on BI.

Conclusions

This study offers practical implications for course design and student support, providing empirical references for integrating GenAI into university physical education courses and fostering students’ DR within regional higher education contexts.