Designing a generative AI enabled learning environment for mathematics word problem solving in primary schools: Learning performance, attitudes and interaction

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Mathematics word problem solving is a critical component of elementary education, yet many students encounter persistent difficulties in this area due to the combined cognitive demands of linguistic comprehension and mathematical reasoning. While previous studies have explored various pedagogical strategies to enhance problem-solving skills, the integration of generative artificial intelligence (GenAI) in this domain remains underexplored. This study introduces the ChatGPT-supported Mathematics Problem-Solving System (ChatGPT-MPS), a GenAI-enabled learning environment designed to support primary students in developing problem-solving strategies and deepening conceptual understanding. To evaluate its effectiveness, a quasi-experimental design was employed involving 104 fifth-grade students, assigned to an experimental group (using ChatGPT-MPS) and a control group (traditional instruction). Both groups completed a pre- and post-tests of MPS and interests scale to evaluate their word problem-solving proficiency and learning interests. Quantitively data analysis and its results showed that the experimental group exhibited significantly greater improvements in post-test performance compared to the control group. In addition, student feedback revealed increased interest, perceived value, and motivation when engaging with the ChatGPT-MPS learning environment. These findings provide empirical support for the use of GenAI in mathematics education and demonstrate the potential of ChatGPT-MPS to enhance students' mathematical thinking and engagement in problem-solving tasks. The study contributes to the growing body of research on AI-driven personalized learning in primary education, offering insights into the design and implementation of effective GenAI-enabled learning environments.