Authors
Joseph M Walter, Lucio Volino, Laura Knockel, Erika L Kleppinger, Jarod Parrish, Rachel Stafford
Published in
Journal of the American College of Clinical Pharmacy : JACCP. Volume 9. Issue 7. Pages e70238.
Abstract
Traditional methods to practice patient interviewing skills for student pharmacists include the use of actors and role-playing. While effective, they can be difficult to manage with large class sizes, cost, and time to train. Artificial intelligence (AI) and large language models (LLMs) are readily accessible to offer students a scalable solution to practice collecting information from a controlled, dynamic simulated patient (SP).
Five programs collaborated to create multiple educational activities that utilized AI as an SP for student pharmacists to practice their patient interviewing skills. Each activity included patient cases focused on self-care related topics. Student participants ranged from professional year one to three (P1-P3). A voluntary, anonymous survey was developed to assess baseline demographics and student perceptions of the activities.
Out of 602 students across the five programs, 304 (50.5%) voluntarily completed the survey. Most students were born in Generation Z (1997-2012), identified as white/Caucasian, and had previously used AI. The majority of the students used the written feature to complete the activity. Overall, students reported that the AI activity was easy to use and considered it useful to practice patient interviewing skills. Students from two programs favored the use of the AI activity, while the other three programs favored traditional role-playing with partners. Students stated they missed the real-life human component of practicing with a partner.
AI can be utilized as an SP to assist students in practicing their patient interviewing skills with a variety of topics. Students found the LLM easy to use, helped with their interviewing skills, and wanted to see it utilized more in the pharmacy curriculum. Future activities should find ways to incorporate more real-life components.
PMID:
42322107
Bibliographic data and abstract were imported from PubMed on 20 Jun 2026.
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