Ai-powered Virtual Patients to Enhance Learner Engagement and Application of Knowledge Among Health Professional Students

Preparing future health professionals for effective patient communication and interprofessional collaboration remains a central challenge in health education. The authors developed an AI-powered virtual patient platform with support from an NIH R41 STTR grant, aiming to provide scalable, lifelike simulation experiences that foster clinical interviewing and teamwork skills in interprofessional educational settings.
This presentation shares findings from two key components of our research and development process. First, we describe outcomes from the R41-funded Phase I study, which involved iterative co-design with faculty and students, technical development of the AI simulation engine, and usability testing with 60 interprofessional students. Results demonstrated statistically significant higher learner engagement, improved learning outcomes, and improvements in confidence related to patient interviewing skills.
We also report on the deployment of the platform in a required Behavioral Sciences course for 80 dental students taught by Clinical Psychologists and Internal Medicine Physicians. Through structured virtual encounters and reflective debriefing, students interacted with responsive virtual patients portraying challenging clinical and behavioral scenarios. Evaluation data from pre-post surveys, qualitative feedback, and instructor observations revealed increased student preparedness for real-world patient encounters and appreciation for the psychological realism of the AI-driven responses.
Together, these findings highlight the potential of virtual patient technologies powered by generative AI to support competency-based, interprofessional learning at scale. Implications for broader implementation and ongoing Phase II development efforts will be discussed.

Summit theme alignment: This seminar highlights the potential for emerging technologies like generative AI to serve as scalable, flexible teaching tools that address persistent access, cost, and equity barriers in interprofessional education. The study directly informs how institutions can implement novel approaches to foster foundational communication and empathy skills across learner professions and populations.

Learning Objectives:
After attending this session, participants will be able to:
1) Describe how AI-generated patient simulations can be used to teach and evaluate communication and behavioral health competencies among health professional learners.
2) Understand AI-implementation of qualitative and quantitative methods for assessing learner performance in digital clinical simulations.
3) Identify considerations for addressing unconscious bias and inclusive communication in virtual patient design and feedback mechanisms.

Participants will leave with:
1) A demonstration of Figment Learning Lab's AI-powered interprofessional clinical simulation platform.
2) Design considerations for building interprofessional scenarios that include patient health complexity and equity-sensitive features.
3) Insights into how learner performance data can inform curricular revisions focused on empathy, professionalism, and interprofessional collaboration.

Active Learning Strategies:
1) Attendees will review anonymized student excerpts using an empathy/professionalism rubric and compare results with the original AI-generated evaluation.
2) Attendees will review research and technology design considerations as well as educational outcomes data from our preliminary studies.
3) Audience participation in a live demonstration of AI-virtual patient generation and clinical interviewing

Priority Criteria Fulfillment:
This seminar reviews modern and novel applications of artificial intelligence and digital innovation that support interprofessional collaboration, and inclusive pedagogy. It provides a replicable model for evaluating digital tools that can enhance equity and access to high-quality training across health disciplines.