Modelling with AI
Despite their potential to foster critical thinking, modelling tasks remain underrepresented in mathematics classrooms. Fermi problems (FPs), as open estimation tasks, are well-suited for introducing modelling in primary education. Given the growing presence of artificial intelligence (AI) in educat...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:uabarcelona_::056ae86a8c5bef6fcf8512790b886bb0 |
| Acceso en línea: | https://ddd.uab.cat/record/327563 https://dx.doi.org/urn:doi:10.29333/ejmste/18263 |
| Access Level: | acceso abierto |
| Palabra clave: | Modelling Fermi problems Artificial intelligence Prompt engineering Problem solving setting |
| Sumario: | Despite their potential to foster critical thinking, modelling tasks remain underrepresented in mathematics classrooms. Fermi problems (FPs), as open estimation tasks, are well-suited for introducing modelling in primary education. Given the growing presence of artificial intelligence (AI) in education, it is essential to understand how pre-service teachers (PSTs) engage with tools like ChatGPT in modelling contexts. This study analyses the use of ChatGPT by 133 PSTss solving FPs and examines how this use is shaped by problem complexity and prior experience. Through qualitative and quantitative analysis, three distinct profiles of AI use emerged-expert, assistant, and support-reflecting varying degrees of autonomy and delegation. Results show greater delegation to AI in more complex problems, while prior experience with outdoor problem-solving or ChatGPT fosters more autonomous engagement. These findings provide insights for integrating AI into mathematics education to support reflective, independent, and critical modelling practices. |
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