Assessment of Evolving Large Language Models in Upper Secondary Mathematics
This demonstrates the potential of LLMs to support learning and teaching in upper secondary mathematics, though it is incremental as it builds on existing model evaluations.
The study evaluated the mathematical capabilities of large language models using the Finnish matriculation examination, finding that initial moderate performance improved to near-perfect scores matching top student performance.
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.