Designing and Evaluating an AI-driven Immersive Multidisciplinary Simulation (AIMS) for Interprofessional Education

TL;DR

Interprofessional education faces limitations in cost, scalability, and capturing real-world complexity with traditional case studies and standardized patients. The authors propose AIMS, an AI-enhanced immersive simulation that links a multimodal LLM (Gemini-2.5-Flash), a Unity-based environment, and a character creation pipeline to deliver real-time, multimodal interactions with a virtual patient across ED and primary care settings. Preliminary usability testing shows AIMS can support profession-specific conversations but reveals issues in audio input, synchronization, and embodied feedback that require iterative refinements. The work demonstrates a path toward scalable, AI-driven interprofessional simulations that can be deployed at large events to strengthen collaborative clinical reasoning and teamwork.

Abstract

Interprofessional education has long relied on case studies and the use of standardized patients to support teamwork, communication, and related collaborative competencies among healthcare professionals. However, traditional approaches are often limited by cost, scalability, and inability to mimic the dynamic complexity of real-world clinical scenarios. To address these challenges, we designed and developed AIMS (AI-Enhanced Immersive Multidisciplinary Simulations), a virtual simulation that integrates a large language model (Gemini-2.5-Flash), a Unity-based virtual environment engine, and a character creation pipeline to support synchronized, multimodal interactions between the user and the virtual patient. AIMS was designed to enhance collaborative clinical reasoning and health promotion competencies among students from pharmacy, medicine, nursing, and social work. A formal usability testing session was conducted which participants assumed professional roles on a healthcare team and engaged in a mix of scripted and unscripted conversations. Participants explored the patient's symptoms, social context, and care needs. Usability issues were identified (e.g., audio routing, response latency) and used to guide subsequent refinements. Findings in general suggest that AIMS supports realistic, profession-specific and contextually appropriate conversations. We discussed both technical and pedagogical innovations of AIMS and concluded with future directions.

Figures (6)

• Figure 1: The technical architecture of AIMS
• Figure 2: The patient expressing confusion with a frowning facial expression
• Figure 3: The patient looking ashamed/embarrassed when answering awkward questions
• Figure 4: Formal interaction in Scene I the emergency department
• Figure 5: Formal interaction in Scene II the primary care office