  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.
