A Fuzzy Supervisor Agent Design for Clinical Reasoning Assistance in a Multi-Agent Educational Clinical Scenario Simulation

TL;DR

This paper introduces a Fuzzy Supervisor Agent (FSA) for medical training simulations that interprets student actions in real-time to provide adaptive feedback, aiming to enhance clinical reasoning skills.

Contribution

The paper presents a novel Fuzzy Supervisor Agent architecture that offers scalable, context-aware supervision in medical simulation training environments.

Findings

Design of the FSA using Fuzzy Inference System

Real-time analysis of student decision-making

Potential for scalable, human-like supervision in medical education

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}.