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Currently submitted to: JMIR Serious Games

Date Submitted: Sep 17, 2026

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Bridging the Gap: 3D, AI-Integrated Virtual Patients Prepare Pharmacy Students for Home Care Practice

  • Jayne Pawasauskas; 
  • Michael Dey; 
  • Christopher Hemme; 
  • Roy Bergstrom; 
  • Daniel Negalha

ABSTRACT

Artificial Intelligence (AI) and immersive simulation offer new opportunities to provide pharmacy students with realistic, standardized patient-care experiences. We developed a three dimensional (3D), AI-enabled virtual patient application designed to simulate a home-based patient encounter and allow students to practice symptom assessment, communication, and clinical reasoning. The application integrates an interactive 3D home environment with an AI-driven virtual patient capable of responding dynamically to student questions and decisions. The application was implemented with pharmacy students during spring 2026 and evaluated for feasibility, student performance, and learner perceptions. Students were also evaluated on their achievement of learning outcomes related to patient assessment. Student performance showed improvement in identifying symptom-management needs, and increased confidence in approaching patients with complex, multiple symptom burdens. Students also reported value in this opportunity to practice patient assessment in a realistic, low-stakes environment. These findings suggest that combining generative AI with an immersive 3D environment can provide a feasible and engaging approach to developing pharmacy students' patient assessment skills.


 Citation

Please cite as:

Pawasauskas J, Dey M, Hemme C, Bergstrom R, Negalha D

Bridging the Gap: 3D, AI-Integrated Virtual Patients Prepare Pharmacy Students for Home Care Practice

JMIR Preprints. 17/09/2026:112298

DOI: 10.2196/preprints.112298

URL: https://preprints.jmir.org/preprint/112298

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