Our research focused on developing an AI virtual patient capable of "adaptive responses" for more effective communication training.

I'm excited to share that our research team's paper "Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training" has been accepted to ACL 2025 Findings! #ACL2025

Adaptive-VP addresses these limitations through real-time adaptation based on trainee communication performance: dynamic patient behavior that escalates with poor communication and de-escalates with effective responses

Excited to present our work at #NAACL2025 in Albuquerque! We tackle a core question: What fundamental information is needed to effectively simulate human identity in AI agents?

But for real-world individuals? The full SPC combination significantly outperformed using any single component. Real human identity requires a more comprehensive representation approach than fictional characters.

How effective is each component? Through automated tests with popular drama characters and human evaluations, we found that Personal Life Context (C) alone worked almost as well as the full SPC combination in capturing their identity.

Building on established self-concept research, We break down identity into three key components:
🔹 S: Social Identity (group affiliations and demographics)
🔹 P: Personal Identity (personality traits & values)
🔹 C: Personal Life Context (daily routines & preferences)

Ever wondered how much data it takes to "clone" yourself as an AI? Our #NAACL2025 paper, "SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agents," tackles this head-on by asking: What's the minimum data needed to reflect a complex self-concept?