Unlocking AI potential in primary mathematics: Teachers' technological pedagogical readiness in China

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.