Previously submitted to: JMIR Medical Education (no longer under consideration since Mar 23, 2026)
Date Submitted: Dec 13, 2025
Open Peer Review Period: Dec 16, 2025 - Feb 10, 2026
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Reshaping Psychiatric Education in the AI Era: A Conceptual Framework for Multimodal Intelligent Simulation
ABSTRACT
Background:
The rising global burden of mental disorders contrasts sharply with the shortage of qualified psychiatrists. Traditional psychiatric education relies heavily on clinical apprenticeships and Standardized Patients (SPs). However, these methods face limitations regarding scalability, cost, and the inability to objectively quantify student performance in capturing subtle non-verbal cues. The Framework: We propose a novel educational paradigm: the AI-empowered Intelligent Diagnosis and Treatment Simulation System. This conceptual framework integrates Large Language Models (LLMs) with Multimodal Emotion Recognition (facial and speech analysis) to create high-fidelity Intelligent Virtual Patients (IVPs). Unlike static simulators, this system functions as a "dual-loop" learning tool: it provides realistic patient interaction while simultaneously analyzing the student's diagnostic precision through objective algorithms. Implications: By facilitating high-frequency "Deliberate Practice" in a risk-free environment, this model addresses the gap between theoretical knowledge and clinical intuition. It enables "Precision Education" via data-driven profiling of student competencies. Outlook: While promising, the integration of AI requires navigating ethical complexities, including algorithmic bias and the preservation of human empathy. We advocate for a "Human-AI Synergy" model to cultivate the next generation of mental health professionals.
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