{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adaptive-vp-a-framework-for-llm-based-virtual","title":"Adaptive-VP: A Framework for LLM-Based Virtual Patients that Adapts to Trainees' Dialogue to Facilitate Nurse Communication Training","arxiv_id":"2506.00386","date":"2025-05-31","proceeding":null,"authors":["Keyeun Lee","Seolhee Lee","Esther Hehsun Kim","Yena Ko","Jinsu Eun","Dahee Kim","Hyewon Cho","Haiyi Zhu","Robert E. Kraut","Eunyoung Suh","Eun-mee Kim","Hajin Lim"],"abstract":"Effective communication training is essential to preparing nurses for high-quality patient care. While standardized patient (SP) simulations provide valuable experiential learning, they are often costly and inflexible. Virtual patient (VP) systems offer a scalable alternative, but most fail to adapt to the varying communication skills of trainees. In particular, when trainees respond ineffectively, VPs should escalate in hostility or become uncooperative--yet this level of adaptive interaction remains largely unsupported. To address this gap, we introduce Adaptive-VP, a VP dialogue generation framework that leverages large language models (LLMs) to dynamically adapt VP behavior based on trainee input. The framework features a pipeline for constructing clinically grounded yet flexible VP scenarios and a modular system for assessing trainee communication and adjusting VP responses in real time, while ensuring learner safety. We validated Adaptive-VP by simulating challenging patient conversations. Automated evaluation using a corpus from practicing nurses showed that our communication skill evaluation mechanism reflected real-world proficiency levels. Expert nurses further confirmed that Adaptive-VP produced more natural and realistic interactions than existing approaches, demonstrating its potential as a scalable and effective tool for nursing communication training.","url_abs":"https://arxiv.org/abs/2506.00386v1","url_pdf":"https://arxiv.org/pdf/2506.00386v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adaptive-vp-a-framework-for-llm-based-virtual","repo_url":"https://github.com/keyeun/adaptive-vp","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"dialogue-generation","task_name":"Dialogue Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}