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Coexistence of AI Nurse and AI-Simulated Patient in Nursing Education Using a Bidirectional Fine-Tuning Pipeline: Development and Content Analysis Study
- Truvace record
- TRV-2026-2458raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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- ac53b309cc44…check it
Background Simulated patient systems are essential in nursing education but face actor scarcity, instructor burden, cost, and disrupted continuity where in-person simulation cannot be sustained. Existing large language model (LLM)-based systems address these issues but treat the dialogic partner-the AI nurse-as a static prompt or unspecialized model, compromising data collection quality and pedagogical applicability. Objective We report (1) the construction of 2 specialized fine-tuned models, NURSE-ONE (AI nurse…
The gain
In an exploratory evaluation, ten nursing faculty rated a fine-tuned AI-simulated patient at 90.3%, the pairing of AI patient and AI nurse at 84.8%, and the AI nurse at 69.0%, with all three modes responding within 3 seconds on one consumer graphics card.
What this doesn’t fix
The faculty evaluation was exploratory with ten evaluators, the reported asymmetry between the two models is a preliminary pattern pending replication, and learner outcomes have not been studied.
Sources
1 on file- JMIR Biomedical Engineeringdoi:10.2196/100778
Cite this record
v1Truvace Impact Record TRV-2026-2458, v1: “Coexistence of AI Nurse and AI-Simulated Patient in Nursing Education Using a Bidirectional Fine-Tuning Pipeline: Development and Content Analysis Study.” Truvace, 2026-11-10. /record/TRV-2026-2458 (accessed on the day you copy it). sha256 ac53b309cc44c093…
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