Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Computers and Education Artificial Intelligence

DOI: 10.1016/j.caeai.2025.100438

Authors: Jingxi Liu, Daner Sun, Jin Sun, Jingyun Wang, Philip Leung Ho Yu

Institutions: Education University of Hong Kong, University of Macau, Durham University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,500