Innovation in Aging - Issue S2 - REFRAMING AGING THROUGH AI-GENERATED PATIENT SIMULATIONS IN PRE-MEDICAL EDUCATION

Sep 28, 2026

Uploaded by Angel

Pre-medical students’ perceptions of aging shape how they interact with older patients, yet medical education often sustains misconceptions and implicit biases that reinforce stereotypes. This study tests an AI-video learning approach to help undergraduate health sciences and pre-medical learners reframe aging through a three-phase design: baseline surveys, AI-generated patient simulations, and post-intervention attitude measures. A final component uses AI to create “aged” self-avatars from student photos, embedding them into further simulations to promote empathy and reduce ageist assumptions. Preliminary results compare perspectives before and after the intervention, supporting more age-inclusive gerontology education.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages1

Reading Time3 min

AccessFree to download

Study overview

Three-phase intervention design

AI-generated simulations and aged self-avatars

Expected impact and preliminary evaluation

FAQs

How does the study assess students’ baseline perceptions of aging?

Students complete an initial survey measuring their baseline perceptions of aging and older adults before any AI-based activities.

What happens during the AI-generated video simulation phase?

Students view AI-generated video simulations depicting aging patients with diverse health conditions and life experiences to challenge stereotypes.

How does the “aged” self-avatar component work, and what is its purpose?

Students upload their own photos to an AI system that generates “aged” versions of themselves. The resulting personalized avatars are integrated into additional simulations to help students reflect on their potential aging experiences and increase empathy.

Sep 28, 2026

Uploaded by Angel