FAMULUS：用于诊断推理教学的交互式注释和反馈生成
FAMULUS: Interactive Annotation and Feedback Generation for Teaching Diagnostic Reasoning
摘要：
我们提出的系统FAMULUS帮助学生学习诊断的基础上自动反馈的虚拟病人模拟，它支持教师标记训练数据。诊断是一项非常困难的技能，但对许多不同的职业至关重要（例如，医生、教师）。以前的案例模拟系统仅限于多项选择题，因此不能对学生的诊断推理过程提供建设性的个性化反馈。由于最初只有有限的数据，我们利用（可替换的）NLP模型来支持专家进一步的数据注释和自动建议，并为学生提供自动反馈。我们认为，由于中央模型不断改进，我们的互动方法鼓励学生和教师经常使用的工具，从而加快数据创建和注释的速度。我们展示了两项关于医学和教师教育中诊断推理的用户研究结果，并概述了我们的系统如何扩展到进一步的用例。
英文摘要：
Our proposed system FAMULUS helps students learn to diagnose based on automatic feedback in virtual patient simulations, and it supports instructors in labeling training data. Diagnosing is an exceptionally difficult skill to obtain but vital for many different professions (e.g., medical doctors, teachers). Previous case simulation systems are limited to multiple-choice questions and thus cannot give constructive individualized feedback on a student's diagnostic reasoning process. Given initially only limited data, we leverage a (replaceable) NLP model to both support experts in their further data annotation with automatic suggestions, and we provide automatic feedback for students. We argue that because the central model consistently improves, our interactive approach encourages both students and instructors to recurrently use the tool, and thus accelerate the speed of data creation and annotation. We show results from two user studies on diagnostic reasoning in medicine and teacher education and outline how our system can be extended to further use cases.
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。
