SOURCE-LINKED INTELLIGENCE

Evaluating Scaffolding-Oriented Multi-Agent Large Language Model System for Clinical Interview Training

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Clinical education must prepare medical students to conduct safe and coherent patient interviews under conditions of uncertainty. Traditional standardized patient (SP) training is resource-intensive and difficult to scale. We developed a scaffolding-oriented multi-agent Large Language Model (LLM) AI Standardized Patient (AI-SP) training platform1. The system includes a patient agent for simulated dialog, a tutor agent providing Socratic prompts without disclosing diagnostic information, and a turn-level evaluator agent that monitors clinical progress without revealing summative scores. In a ra

Read original source ↗ Open in workspace

recordType

paper

region

Global