Currently accepted at: JMIR Medical Education
Date Submitted: Jul 7, 2026
Date Accepted: Nov 5, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/106479
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited.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.
Enhancing Empathy in Nursing Students Through a Multimodal AI-Supported Simulation: A Mixed-Methods Study
ABSTRACT
Background:
Empathic communication is a core competency in nursing practice, yet providing nursing students with realistic opportunities to develop this competency remains challenging. Recent advances in artificial intelligence (AI) have enabled the integration of behavioural and physiological data into simulation-based education.
Objective:
To evaluate the effectiveness of a multimodal AI-supported simulation in improving empathy, communication self-efficacy, and learning engagement among undergraduate nursing students.
Methods:
A quasi-experimental pretest–posttest study was conducted with 134 undergraduate nursing students (experimental group, n = 68; control group, n = 66). The intervention integrated conversational AI, facial emotion recognition, speech analysis, and heart rate variability (HRV) monitoring. Outcomes were analysed using ANCOVA, and qualitative interview data were analysed using thematic analysis.
Results:
After adjustment for baseline scores, the experimental group showed significantly greater improvements in empathy, communication self-efficacy, and emotional, behavioural, and cognitive engagement than the control group (all p < .001). HRV also differed significantly between groups (p < .001), with the greatest physiological response observed during the chemotherapy scenario. Qualitative findings indicated enhanced empathy awareness, communication confidence, and reflective learning.
Conclusions:
Multimodal AI-supported simulation improved empathy, communication self-efficacy, and learning engagement by providing integrated behavioural and physiological feedback. This approach may serve as a complementary educational strategy to support empathic communication training in undergraduate nursing education.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.