MathTutorBench: A Benchmark for Measuring Open-ended Pedagogical Capabilities of LLM Tutors
Jakub Mačina, Nico Daheim, Ido Hakimi, Manu Kapur, Iryna Gurevych, Mrinmaya Sachan
Abstract
Evaluating the pedagogical capabilities of AIbased tutoring models is critical for making guided progress in the field.Yet, we lack a reliable, easy-to-use, and simple-to-run evaluation that reflects the pedagogical abilities of models.To fill this gap, we present MATH-TUTORBENCH, an open-source benchmark for holistic tutoring model evaluation.MATHTU-TORBENCH contains a collection of datasets and metrics that broadly cover tutor abilities as defined by learning sciences research in dialogbased teaching.To score the pedagogical quality of open-ended teacher responses, we train a reward model and show it can discriminate expert from novice teacher responses with high accuracy.We evaluate a wide set of closed-and open-weight models on MATHTUTORBENCH and find that subject expertise, indicated by solving ability, does not immediately translate to good teaching.Rather, pedagogy and subject expertise appear to form a trade-off that is navigated by the degree of tutoring specialization of the model.Furthermore, tutoring appears to become more challenging in longer dialogs, where simpler questioning strategies begin to fail.We release the benchmark, code, and leaderboard openly to enable rapid benchmarking of future models. 1 github.