代理是否准备好教学？一个多阶段基准用于现实世界教学工作流程
Are Agents Ready to Teach? A Multi-Stage Benchmark for Real-World Teaching Workflows
AI总结：
本文提出EduAgentBench基准，用于评估教学代理的全面能力，发现当前模型在教学任务中的表现有限，但仍为开发未来教学代理提供了测量基础。
摘要：
语言代理越来越多地部署在复杂的专业工作流程中，辅导能力作为高风险功能，目前在现有基准中仍未得到充分衡量。有效的辅导代理需要超越产生正确答案或执行准确工具调用：一个稳健的辅导代理必须诊断学习者状态、随时间适应支持、做出基于教育证据的决策，并在现实的学习管理系统中执行干预。我们引入EduAgentBench，一个源驱动的基准，用于全面评估辅导代理在教学工作范围内的能力。它包含150个经过质量控制的任务，涵盖三个能力表面：专业教学判断、情境多轮辅导和Canvas式教学工作流程完成。任务通过教学洞察驱动的流程构建，并通过互补的验证信号和人工审查进行评估。在对前沿模型的全面评估中，我们的发现表明，当前模型在有限的教学判断方面表现良好，但在情境辅导和自主教学工作流程执行方面仍无法达到专业教学标准。据我们所知，EduAgentBench是第一个理论基础和现实的基准，用于评估辅导代理的全面教学能力，为开发未来能够支持现实教学工作的辅导代理提供了测量基础。
英文摘要：
Language agents are increasingly deployed in professional workflows, yet tutoring remains a high-stakes capability that existing evaluations only partially capture. Effective tutor agents require more than producing correct answers or executing accurate tool calls: they must infer a warranted teaching decision from evidence, adapt support as learner state changes, and carry an instructor's request through a learning-management system (LMS) to a completed, verified intervention. We introduce TeachArena, a source-grounded benchmark that jointly evaluates three complementary surfaces of teaching work: professional pedagogical judgment, situated multi-turn tutoring, and end-to-end LMS teaching workflows. Its 354 audited tasks are each built around a pedagogical insight, grounded in evidence, and evaluated with matched verifiers over observable turn-level responses, tutoring trajectories, and persistent artifacts or environment states. Across a comprehensive evaluation of frontier models, our findings reveal that current models are generally capable of bounded pedagogical judgment, but still fall short of professional teaching standards in situated tutoring and end-to-end teaching-workflow execution. By unifying teacher judgment, adaptive tutoring, and institutional action in one auditable benchmark, TEACHARENA provides a measurement foundation for developing tutor agents that can support realistic teaching work.
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。
