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A large language model-powered reflective AI agent for evidence-based nursing education: Design and evaluation
Shuqi Yang1, Manfei Shi1, Yuhang Qian2
1School of Nursing, Fudan University, Shanghai, China.
Nurse Education in Practice
|January 11, 2026
Summary
A new Evidence-Based Nursing Expert (EBN-Expert) system significantly outperformed general large language models in nursing education assessments. This AI tool enhances reflective learning and supports evidence-based practice training for nursing students.
Area of Science:
- Artificial Intelligence in Education
- Nursing Education Technology
- Domain-Specific AI
Background:
- Evidence-based practice (EBP) is crucial in nursing but challenging for students to master.
- Traditional methods struggle to develop critical thinking for EBP.
- General large language models (LLMs) offer limited support for EBP's specific needs.
Purpose of the Study:
- To develop and evaluate the Evidence-Based Nursing Expert (EBN-Expert), a specialized AI agent.
- To support reflective learning in evidence-based nursing education.
- To assess EBN-Expert's performance against general LLMs.
Main Methods:
- A comparative evaluation study using standardized nursing exam questions.
- Developed EBN-Expert based on Evidence-Based Nursing textbook content.
- Compared EBN-Expert against ChatGPT-o1, DeepSeek-R1, and Kimi using 124 test items.
Main Results:
- EBN-Expert significantly outperformed general LLMs (P < 0.001), achieving the highest score.
- EBN-Expert excelled in methodological and interpretive reasoning questions.
- Perfect accuracy in true/false and strong performance in multiple-choice questions were noted.
Conclusions:
- Domain-specific AI tools like EBN-Expert show promise for nursing education.
- EBN-Expert's curriculum alignment and accuracy support EBP training.
- The system offers a scalable and trustworthy approach to advancing nursing education.
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