Large Language Model-Powered 3D Avatars to Train Shared Decision-Making in Undergraduate Medical Students: Design and Mixed Methods Acceptance Study.
Abstract
Background
Shared decision-making (SDM) is a core competency in patient-centered care, yet it is challenging for physicians. Simulation-based education with standardized patients (SPs) offers valuable practice opportunities for undergraduate medical education (UME), but is limited by cost, scalability, and variable feedback quality. Advances in large language model (LLM) technology and virtual patient (VP) design present a promising pathway toward accessible, realistic, and high-quality SDM skill training.
Objective
This study aimed to develop LLM-powered, real-time interactive 3D avatars for SDM training in UME and to evaluate their acceptance among undergraduate medical students.
Methods
We conducted a descriptive, cross-sectional mixed methods study at KU Leuven, Belgium (November-December 2025). The avatar system was developed through an iterative, interdisciplinary collaboration between medical educators, SDM experts, software developers, user experience designers, and AI engineers, grounded in prior work on physicians' educational needs and SDM learning mechanisms. Development comprised prototype case authoring, usability testing with medical students, and iterative refinement. The final system is a web-based platform featuring real-time, LLM-powered spoken dialogue with diverse, photorealistic 3D patient avatars; layered prompts configuring avatar personality, clinical scenario, and difficulty; an educator panel for case authoring; and automated AI-generated formative feedback structured around 6 phases of SDM. After using the system for 1 month, a convenience sample of UME students completed a 9-item, 5-point Likert scale questionnaire grounded in the technology acceptance model (TAM), assessing perceived usefulness, perceived ease of use, and intention to use. Optional open-text responses were narratively analyzed to triangulate these findings.
Results
Of 25 recruited participants, 24 were included in the analysis. Internal consistency was acceptable for perceived usefulness (α=0.76) and intention to use (α=0.78), but not for perceived ease of use (α=0.69). All construct means exceeded the scale midpoint of 3, suggesting high overall acceptance. Students rated perceived usefulness highest (mean 4.43, SD 0.50), followed by intention to use (mean 4.42, SD 0.59), and perceived ease of use was the lowest (mean 3.81, SD 0.68). Fourteen participants provided open-text responses. These highlighted the value of a safe, low-pressure environment for practicing communication without patients or peers present; reported mixed experiences with avatar realism, with reservations directed at behavioral rather than physical realism; and identified technical difficulties (microphone issues, freezing, and crashes) as the most frequent area for improvement.
Conclusions
LLM-powered, real-time interactive 3D avatars are feasible and well-accepted tools for SDM training in UME. Students found the system educationally valuable and intended to use it again. Technical reliability and behavioral realism should be prioritized in future development. The modular architecture and automated feedback pipeline support scalable deployment across disciplines, institutions, and languages. Larger, multi-institutional studies with objective learning outcome measures are needed to establish efficacy.
