Zhao Guolong, Li Mao, Long Zhiyi, Fan Hongming
School of Teacher Education, Xinyang Normal University, Xinyang, China.
School of Curriculum, Teaching and Inclusion, Faculty of Education, Monash University, Melbourne, Australia.
Acta Psychol (Amst). 2025 Oct;260:105558. doi: 10.1016/j.actpsy.2025.105558. Epub 2025 Sep 13.
This study investigates the integration of artificial intelligence (AI) tools in Chinese primary mathematics education, focusing on the factors shaping their adoption and application. Guided by the Technological Pedagogical Readiness (TPR) framework and employing an explanatory sequential mixed-methods design, the research quantitatively examines relationships among teachers' Technological Pedagogical Content Knowledge (TPACK), practices, attitudes, and AI utilization in a sample of 1205 primary mathematics teachers across three cities in Southwest China. Qualitative interviews with six teachers provide in-depth, contextual explanations for the quantitative patterns observed, illuminating how and why AI tools, such as generative AI and adaptive learning systems, are incorporated into classroom teaching. The results demonstrate that teachers' TPACK and attitudes toward AI are decisive for successful AI integration, while external factors, including educational challenges, parental and community involvement, and students' technology literacy, have limited direct influence in this well-supported context. Four main approaches to AI integration were identified: contextual visualization to clarify abstract concepts, personalized instruction, interactive and collaborative learning, and data-driven instructional optimization. These mixed-methods findings not only expand understanding of AI's pedagogical applications but also underscore the importance of teacher psychological readiness (e.g., attitudes, confidence) alongside professional knowledge. The study advocates for targeted professional development and strategic institutional support as critical to maximizing the educational potential of AI, and highlights the conditional nature of contextual factors for future research and policy considerations. Further discussion of these findings and their implications is provided in the main text.
本研究调查了人工智能（AI）工具在中国小学数学教育中的整合情况，重点关注影响其采用和应用的因素。在技术教学准备度（TPR）框架的指导下，采用解释性序列混合方法设计，该研究对中国西南三个城市的1205名小学数学教师样本中教师的技术教学内容知识（TPACK）、实践、态度和人工智能使用之间的关系进行了定量研究。对六位教师的定性访谈为观察到的定量模式提供了深入的、情境化的解释，阐明了生成式人工智能和自适应学习系统等人工智能工具如何以及为何被纳入课堂教学。结果表明，教师的TPACK和对人工智能的态度是人工智能成功整合的决定性因素，而外部因素，包括教育挑战、家长和社区参与以及学生的技术素养，在这种得到充分支持的背景下直接影响有限。确定了人工智能整合的四种主要方法：情境可视化以澄清抽象概念、个性化教学、互动与协作学习以及数据驱动的教学优化。这些混合方法的研究结果不仅扩展了对人工智能教学应用的理解，还强调了教师心理准备（如态度、信心）与专业知识同样重要。该研究主张有针对性的专业发展和战略性的机构支持对于最大化人工智能的教育潜力至关重要，并强调情境因素对未来研究和政策考虑的条件性质。正文将对这些发现及其影响进行进一步讨论。
