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

2025-07-03

AI Summary

In medical education, effective technological support for dynamic assessment and feedback on students’ clinical reasoning (CR) skills has long been lacking. To address this, we propose the Fuzzy Supervisor Agent (FSA), a multi-agent educational platform designed for clinical simulation. This work introduces fuzzy inference systems (FIS) into the CR training loop for the first time: leveraging a predefined four-dimensional fuzzy rule base—covering professionalism, medical relevance, ethical conduct, and contextual interference—the FSA analyzes student–clinical-agent interactions in real time and generates human-like, adaptive, context-aware feedback to enable dynamic instructional intervention. We fully implement the FSA’s technical architecture and operational mechanism, demonstrating high scalability and contextual precision. The source code is publicly released, establishing a reproducible foundation for future empirical studies.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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}.

Problem

• Assisting medical students with clinical reasoning during training
• Designing a fuzzy supervisor agent for real-time feedback
• Enhancing simulation-based medical education with adaptive assistance

Innovation

• Fuzzy Inference System interprets student interactions
• Real-time adaptive feedback for decision-making
• Scalable human-like supervision in simulations