发表机构
University of Illinois Chicago（伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种教育者引导的LLM教学代理，集成于ERD编辑器，通过四阶段工作流提供支架式反馈，实验显示71.1%的修改采纳了隐藏诊断目标，并支持延迟披露与学生自主性。
AI 中文摘要
我们提出了一种教育者引导的LLM教学代理，用于概念数据库设计中的支架式反馈。该系统集成到实体-关系图（ERD）编辑器中，将反馈基于学生作品、作业要求、教育者编写的评分标准和教学资源。其架构将隐藏的、基于工件的诊断与控制学生面对支持的形式和披露级别的工作流程分开。我们将该架构实例化为一个四阶段工作流，从概念检查和引导应用逐步过渡到低细节反馈和局部澄清。每个反馈请求都会创建一个有状态的情节，与版本化的ERD状态相关联。在涵盖三个ERD环境和383个反馈情节的部署中，71.1%的观察到的目标级更改完全或部分包含了隐藏的诊断目标，其中许多发生在第1-2阶段之后。定性分析表明，分阶段披露有时会隐瞒不准确的细节，支持选择性采纳，或允许后期恢复，尽管一些错误仍然影响了修订。来自自选样本的调查回应倾向于延迟披露和学生自主性，但指出了间接性和重复性。
英文摘要
We present an educator-guided LLM pedagogical agent for scaffolded feedback in conceptual database design. Integrated into an entity--relationship diagram (ERD) editor, the system grounds feedback in the student artifact, assignment requirements, educator-authored rubrics, and instructional resources. Its architecture separates hidden, artifact-grounded diagnosis from the workflow that controls the form and disclosure level of student-facing support. We instantiate the architecture as a four-stage workflow progressing from concept checks and guided application to low-detail feedback and localized clarification. Each feedback request creates a stateful episode linked to versioned ERD states. In a deployment spanning three ERD environments and 383 feedback episodes, 71.1\% of observed target-level changes fully or partially incorporated the hidden diagnostic target, including many after Stages~1--2. Qualitative analysis showed that staged disclosure sometimes withheld inaccurate details, supported selective uptake, or allowed later recovery, though some errors still shaped revisions. Survey responses from a self-selected sample favored delayed disclosure and student agency but noted indirectness and repetition.
