Scholarlink

<p>Generative artificial intelligence (GAI) is emerging as a promising tool in teacher education, with the potential to become a powerful aid to pre-service teachers in their daily learning and teacher preparation. However, despite this potential, little literature has explored the acceptance of GAI by pre-service teachers considering technology major and trust implications, hindering its effective integration into teacher education with China. Therefore, this study sought to understand the factors that drive pre-service teachers’ intention to use GAI by employing an extended Technology Acceptance Model framework, incorporating subjective norm, facilitating conditions, AI self-efficacy, and AI-trust, and analyzing data from 486 Chinese pre-service teachers via Partial Least Squares Structural Equation Modeling and Artificial Neural Network analysis. The analysis reveals that the tested relationships are statistically significant. Major moderates the relationship between subjective norm and behavioral intention. This study provides valuable insights for teacher educators, policy-makers, and AI developers on how to promote GAI adoption among future teachers.</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

Determinants of Chinese Pre-service Teachers’ Acceptance towards Generative Artificial Intelligence: A Multi-Group SEM-ANN Analysis

  • 期刊论文

  • 发表日期：

    2026年3月25日

查看全文
  • Qing Wu,
  • Jingwen Sun,
  • Yongbin Hu

摘要

Generative artificial intelligence (GAI) is emerging as a promising tool in teacher education, with the potential to become a powerful aid to pre-service teachers in their daily learning and teacher preparation. However, despite this potential, little literature has explored the acceptance of GAI by pre-service teachers considering technology major and trust implications, hindering its effective integration into teacher education with China. Therefore, this study sought to understand the factors that drive pre-service teachers’ intention to use GAI by employing an extended Technology Acceptance Model framework, incorporating subjective norm, facilitating conditions, AI self-efficacy, and AI-trust, and analyzing data from 486 Chinese pre-service teachers via Partial Least Squares Structural Equation Modeling and Artificial Neural Network analysis. The analysis reveals that the tested relationships are statistically significant. Major moderates the relationship between subjective norm and behavioral intention. This study provides valuable insights for teacher educators, policy-makers, and AI developers on how to promote GAI adoption among future teachers.