AIGC-Enabled Design of Interdisciplinary Project-Based Learning for Primary School Mathematics - Li, Zemeng; Zhou, Huali (Education Insights, 2026)
The rapid development of generative artificial intelligence (AIGC) has created unprecedented opportunities for pedagogical innovation, particularly within the foundational domain of primary mathematics education. Traditional instructional methods often struggle to engage young learners and bridge the gap between abstract mathematical concepts and real-world applications. Guided by the comprehensive STEAM (Science, Technology, Engineering, Arts, and Mathematics) education framework, this study develops an innovative, AIGC-enabled interdisciplinary thematic learning model tailored specifically for primary mathematics. By utilizing interdisciplinary thematic learning as the core organizational approach and project-based learning (PBL) as the primary implementation strategy, this research seeks to transform conventional classroom dynamics. To rigorously examine the practical effectiveness and feasibility of the proposed model, a detailed teaching case was systematically designed, deployed, and evaluated in a real-world educational setting. Quantitative and qualitative analyses of the implementation results indicate that the AIGC-integrated model effectively promotes students' comprehensive cognitive competencies. Furthermore, it significantly enhances their practical problem-solving performance, facilitates deeper knowledge integration across multiple disciplines, and robustly supports the development of overall digital and mathematical literacy. These compelling findings demonstrate the profound educational value of integrating AIGC technologies into STEAM-oriented interdisciplinary PBL frameworks, offering a scalable and highly effective paradigm for modernizing primary mathematics curricula and preparing students for future technological landscapes.