Gong Yulin, Xu Chengshu, Luo Suwen, Lin Jiaxin
China Institute of Rural Education Development, Northeast Normal University, Changchun City, 130024, China.
Stanford Center on China's Economy and Institutions, Stanford University, Palo Alto, 94305, USA.
Sci Rep. 2025 Sep 1;15(1):32208. doi: 10.1038/s41598-025-03298-9.
With the deepening integration of artificial intelligence (AI) technologies in the education sector, large language models (LLMs) have become essential tools for supporting writing tasks. As the future backbone of the teaching profession, the acceptance of these technologies by teacher education students not only influences their professional development but also plays a critical role in the digital transformation of future educational practices. However, existing research has yet to fully uncover the underlying mechanisms and influencing factors driving technology adoption behaviors within this group. This study extends the Technology Acceptance Model (TAM) by incorporating key variables such as learning motivation, perceived risks, self-efficacy, and usage experience. Using structural equation modeling (SEM), we analyzed survey data from 552 fourth-year teacher education students in China to test the proposed hypotheses. The empirical findings reveal that subjective norms are the strongest predictor of behavioral intention, while perceived ease of use significantly and positively influences attitudes toward using LLMs. Among the risk dimensions, perceived time risk exerts a significant negative effect on perceived usefulness, whereas perceived privacy risk negatively impacts perceived ease of use. Additionally, usage experience fosters technology adoption behaviors by enhancing learning motivation. These findings not only extend the application boundaries of the TAM within the field of educational technology but also provide empirical evidence for educational institutions to design technology training programs and for model developers to optimize user experiences. Furthermore, they offer a theoretical framework for building digital literacy training systems for teacher education students.
随着人工智能（AI）技术在教育领域的深度融合，大语言模型（LLMs）已成为支持写作任务的重要工具。作为未来教师职业的中坚力量，教师教育专业学生对这些技术的接受程度不仅影响他们的职业发展，而且在未来教育实践的数字化转型中也起着关键作用。然而，现有研究尚未充分揭示驱动该群体技术采用行为的潜在机制和影响因素。本研究通过纳入学习动机、感知风险、自我效能感和使用经验等关键变量，扩展了技术接受模型（TAM）。我们使用结构方程模型（SEM）分析了来自中国552名大四教师教育专业学生的调查数据，以检验所提出的假设。实证结果表明，主观规范是行为意向的最强预测因素，而感知易用性对使用大语言模型的态度有显著的正向影响。在风险维度中，感知时间风险对感知有用性有显著的负向影响，而感知隐私风险对感知易用性有负向影响。此外，使用经验通过增强学习动机促进技术采用行为。这些发现不仅扩展了技术接受模型在教育技术领域的应用边界，而且为教育机构设计技术培训项目以及模型开发者优化用户体验提供了实证依据。此外，它们还为构建教师教育专业学生的数字素养培训系统提供了理论框架。
