An Agentic AI Framework for Training General Practitioner Student Skills

TL;DR

This paper presents an agentic AI framework that improves virtual simulated patients for medical training by integrating customizable scenarios, controlled dialogues, and standards-based feedback, leading to more realistic and educationally effective interactions.

Contribution

The paper introduces a novel agentic framework unifying vignette generation, persona-driven dialogue, and assessment for VSPs, demonstrated in a spoken consultation setting with positive student feedback.

Findings

Participants found the system realistic and faithful to scenarios.

The framework provided useful, example-rich feedback.

Usability of the system was rated as excellent.

Abstract

Advancements in large language models offer strong potential for enhancing virtual simulated patients (VSPs) in medical education by providing scalable alternatives to resource-intensive traditional methods. However, current VSPs often struggle with medical accuracy, consistent roleplaying, scenario generation for VSP use, and educationally structured feedback. We introduce an agentic framework for training general practitioner student skills that unifies (i) configurable, evidence-based vignette generation, (ii) controlled persona-driven patient dialogue with optional retrieval grounding, and (iii) standards-based assessment and feedback for both communication and clinical reasoning. We instantiate the framework in an interactive spoken consultation setting and evaluate it with medical students ($\mathbf{N{=}14}$). Participants reported realistic and vignette-faithful dialogue, appropriate difficulty calibration, a stable personality signal, and highly useful example-rich feedback, alongside excellent overall usability. These results support agentic separation of scenario control, interaction control, and standards-based assessment as a practical pattern for building dependable and pedagogically valuable VSP training tools.