Supporting empathic understanding through AI-generated backstories in nursing simulation: A mixed-methods study.

AIM/

OBJECTIVE

To develop AI-generated backstory videos grounded in the Simulation Artificial Empathy Framework (SAEF) and to examine their effects on nursing students' empathic processes in nursing simulation.

BACKGROUND

Enhancing empathic engagement and reflective learning in simulation-based nursing education remains challenging. Generative AI-based narrative approaches may provide structured support for empathy development by integrating cognitive and affective processes.

DESIGN

A quasi-experimental mixed-methods study using a nonequivalent control group pretest-posttest design.

METHODS

A total of 105 senior nursing students participated. The experimental group received SAEF-based AI-generated backstory videos for presimulation preparation, whereas the control group used conventional text-based materials. SAEF draws on complementary elements of the Artificial Empathy Framework and the Prehospital Journey concept of the Simulated Empathy Framework. Data were analyzed using χ2-tests, independent t-tests, and content analysis of reflective journals.

RESULTS

No statistically significant between-group differences were found in posttest scores for empathy, emotional intelligence, or learning flow. However, qualitative findings revealed meaningful differences in learning processes. The experimental group demonstrated greater emphasis on emotional identification, inference of patient meaning, and perspective-taking.

CONCLUSION

Although short-term quantitative effects were not statistically significant, SAEF-based AI-generated backstories demonstrated potential to support empathic understanding and patient perspective-taking. Integrating generative AI with theory-driven narratives may serve as an effective presimulation strategy. Further longitudinal studies with objective performance measures are warranted.