# An Agentic AI Framework for Training General Practitioner Student Skills

arXiv: https://arxiv.org/abs/2512.18440 · HTML: https://mlsanity.com/paper/2512.18440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 20 Dec 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 14 of 14)

- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [Towards Conversational Diagnostic AI](https://mlsanity.com/paper/2401.05654/) (2024)
- [LLMs Can Simulate Standardized Patients via Agent Coevolution](https://mlsanity.com/paper/2412.11716/) (2024)
- [Beyond Profile: From Surface-Level Facts to Deep Persona Simulation in LLMs](https://mlsanity.com/paper/2502.12988/) (2025)
- [Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training](https://mlsanity.com/paper/2506.00386/) (2025)
- [Benchmarking Generative AI for Scoring Medical Student Interviews in Objective Structured Clinical Examinations (OSCEs)](https://mlsanity.com/paper/2501.13957/) (2025)
- [From ChatGPT to DeepSeek: Can LLMs Simulate Humanity?](https://mlsanity.com/paper/2502.18210/) (2025)
- [Modeling Challenging Patient Interactions: LLMs for Medical Communication Training](https://mlsanity.com/paper/2503.22250/) (2025)
- [CUPCase: Clinically Uncommon Patient Cases and Diagnoses Dataset](https://mlsanity.com/paper/2503.06204/) (2025)
- [Activation-Space Personality Steering: Hybrid Layer Selection for Stable Trait Control in LLMs](https://mlsanity.com/paper/2511.03738/) (2025)
- [Automated Generation of High-Quality Medical Simulation Scenarios Through Integration of Semi-Structured Data and Large Language Models](https://mlsanity.com/paper/2404.19713/) (2024)
- [CLiVR: Conversational Learning System in Virtual Reality with AI-Powered Patients](https://mlsanity.com/paper/2510.19031/) (2025)

## Abstract (arXiv)

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 (mathbfN=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.