统一AI导师评估：面向LLM驱动AI导师教学能力评估的评估分类体系
Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors
AI总结：
本文针对LLM驱动AI导师的教学能力评估问题，提出基于学习科学原理的8维度统一分类体系，发布含192段对话、1596条回应的MRBench基准，评估了Prometheus2等模型的评估可靠性并分析导师能力，为AI导师发展提供支撑。
摘要：
本文研究当前最先进的大型语言模型（LLMs）是否能有效作为AI导师，以及它们在教育对话中是否展现出优质AI辅导所需的教学能力。此前的评估工作局限于主观协议和基准。为填补这一空白，我们基于关键学习科学原理提出包含8个教学维度的统一评估分类体系，旨在评估LLM驱动AI导师针对数学领域学生错误或困惑生成的回应的教学价值。我们发布MRBench——一个新的评估基准，包含来自7个最先进LLM基导师和人类导师的192段对话及1596条回应，并提供8个教学维度的黄金标注。我们评估了流行的Prometheus2和Llama-3.1-8B LLMs作为评估器的可靠性，分析了每个导师的教学能力，指出哪些LLMs是优秀导师、哪些更适合作为问答系统。我们认为，所提出的分类体系、基准和人类标注标签将简化评估流程，助力追踪AI导师的发展进度。
英文摘要：
In this paper, we investigate whether current state-of-the-art large language models (LLMs) are effective as AI tutors and whether they demonstrate pedagogical abilities necessary for good AI tutoring in educational dialogues. Previous efforts towards evaluation have been limited to subjective protocols and benchmarks. To bridge this gap, we propose a unified evaluation taxonomy with eight pedagogical dimensions based on key learning sciences principles, which is designed to assess the pedagogical value of LLM-powered AI tutor responses grounded in student mistakes or confusions in the mathematical domain. We release MRBench - a new evaluation benchmark containing 192 conversations and 1,596 responses from seven state-of-the-art LLM-based and human tutors, providing gold annotations for eight pedagogical dimensions. We assess reliability of the popular Prometheus2 and Llama-3.1-8B LLMs as evaluators and analyze each tutor's pedagogical abilities, highlighting which LLMs are good tutors and which ones are more suitable as question-answering systems. We believe that the presented taxonomy, benchmark, and human-annotated labels will streamline the evaluation process and help track the progress in AI tutors' development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
