A Fuzzy Supervisor Agent Design for Clinical Reasoning Assistance in a Multi-Agent Educational Clinical Scenario Simulation
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Assisting medical students with clinical reasoning (CR) during clinical
scenario training remains a persistent challenge in medical education. This
paper presents the design and architecture of the Fuzzy Supervisor Agent (FSA),
a novel component for the Multi-Agent Educational Clinical Scenario Simulation
(MAECSS) platform. The FSA leverages a Fuzzy Inference System (FIS) to
continuously interpret student interactions with specialized clinical agents
(e.g., patient, physical exam, diagnostic, intervention) using pre-defined
fuzzy rule bases for professionalism, medical relevance, ethical behavior, and
contextual distraction. By analyzing student decision-making processes in
real-time, the FSA is designed to deliver adaptive, context-aware feedback and
provides assistance precisely when students encounter difficulties. This work
focuses on the technical framework and rationale of the FSA, highlighting its
potential to provide scalable, flexible, and human-like supervision in
simulation-based medical education. Future work will include empirical
evaluation and integration into broader educational settings. More detailed
design and implementation is~\href{https://github.com/2sigmaEdTech/MAS/}{open
sourced here}.
