Assessment of Evolving Large Language Models in Upper Secondary Mathematics
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915336096317440 |
|---|---|
| author | Setälä, Mika Sikström, Pieta Heilala, Ville Kärkkäinen, Tommi |
| author_facet | Setälä, Mika Sikström, Pieta Heilala, Ville Kärkkäinen, Tommi |
| contents | Large language models (LLMs) have shown increasing promise in educational settings, yet their mathematical reasoning has been considered evolving. This study evaluates the mathematical capabilities of various LLMs using the Finnish matriculation examination, a high-stakes digital test for upper secondary education. Initial tests yielded moderate performance corresponding to mid-range grades, but later evaluations demonstrated substantial improvements as the language models evolved. Remarkably, some models achieved near-perfect or perfect scores, matching top student performance and qualifying for university admission. Our findings highlight the rapid advances in the mathematical proficiency of LLMs and illustrate their potential as underlying tools to support learning and teaching in a variety of ways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_12347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessment of Evolving Large Language Models in Upper Secondary Mathematics Setälä, Mika Sikström, Pieta Heilala, Ville Kärkkäinen, Tommi Computation and Language Artificial Intelligence Computers and Society K.3; I.2 Large language models (LLMs) have shown increasing promise in educational settings, yet their mathematical reasoning has been considered evolving. This study evaluates the mathematical capabilities of various LLMs using the Finnish matriculation examination, a high-stakes digital test for upper secondary education. Initial tests yielded moderate performance corresponding to mid-range grades, but later evaluations demonstrated substantial improvements as the language models evolved. Remarkably, some models achieved near-perfect or perfect scores, matching top student performance and qualifying for university admission. Our findings highlight the rapid advances in the mathematical proficiency of LLMs and illustrate their potential as underlying tools to support learning and teaching in a variety of ways. |
| title | Assessment of Evolving Large Language Models in Upper Secondary Mathematics |
| topic | Computation and Language Artificial Intelligence Computers and Society K.3; I.2 |
| url | https://arxiv.org/abs/2504.12347 |