Research suggests that tutors should adopt a strategic approach when
addressing math errors made by low-efficacy students. Rather than drawing
direct attention to the error, tutors should guide the students to identify and
correct their mistakes on their own. While tutor lessons have introduced this
pedagogical skill, human evaluation of tutors applying this strategy is arduous
and time-consuming. Large language models (LLMs) show promise in providing
real-time assessment to tutors during their actual tutoring sessions, yet
little is known regarding their accuracy in this context. In this study, we
investigate the capacity of generative AI to evaluate real-life tutors'
performance in responding to students making math errors. By analyzing 50
real-life tutoring dialogues, we find both GPT-3.5-Turbo and GPT-4 demonstrate
proficiency in assessing the criteria related to reacting to students making
errors. However, both models exhibit limitations in recognizing instances where
the student made an error. Notably, GPT-4 tends to overidentify instances of
students making errors, often attributing student uncertainty or inferring
potential errors where human evaluators did not. Future work will focus on
enhancing generalizability by assessing a larger dataset of dialogues and
evaluating learning transfer. Specifically, we will analyze the performance of
tutors in real-life scenarios when responding to students' math errors before
and after lesson completion on this crucial tutoring skill.

该研究调查两个生成模型在评估真实教师在应对学生数学错误方面的表现能力，发现 GPT-3.5-Turbo 和 GPT-4 都能够熟练评估与学生犯错有关的标准，但在识别学生错误的情况下存在局限性。未来的研究将致力于通过评估更多对话数据集和评估学习转化来提升泛化能力，进一步分析教师在真实场景中应对学生数学错误的表现。