eISSN 2097-6054 ISSN 1672-9234 CN 11-5289/R
Responsible Institution:China Association for Science and Technology
Publishing:Chinese Nursing Journals Publishing House Co.,Ltd.
Sponsor:Chinese Nursing Association
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Chinese Journal of Nursing Education ›› 2026, Vol. 23 ›› Issue (7): 814-821.doi: 10.3761/j.issn.1672-9234.2026.07.008

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Construction and evaluation of a multi-agent collaborative nursing inquiry teaching mini-program

WANG Miaoli1(), HUANG Chongmei2, XIAO Lin3, CHEN Qirong1, ZHANG Haojie4, TAN Minghui1, TAO Hongliang1, LIU Wei1, DING Jinfeng1,*()   

  1. 1 Xiangya School of NursingCentral South UniversityChangsha 410013, China
    2 School of Computer ScienceCentral South UniversityChangsha 410013, China
    3 School of NursingNingxia Medical UniversityYinchuan 750004, China
    4 School of NursingSouthern Medical UniversityGuangzhou 510515, China
  • Received:2026-01-16 Online:2026-07-15 Published:2026-07-17
  • Contact: *DING Jinfeng,E-mail:jinfeng.ding@csu.edu.cn E-mail:1227830455@qq.com;jinfeng.ding@csu.edu.cn
  • Supported by:
    Major Undergraduate Education and Teaching Reform Project “Open Competition for Task Assignment”(NYJXJBGS-2024015)

Abstract:

Objective To develop an intelligent nursing inquiry teaching mini-program integrating knowledge-based question answering,simulated clinical inquiry,and feedback evaluation,and to evaluate its usability. Methods Based on a collaborative framework of artificial intelligence(AI) Partner and AI Tutor agents,inquiry agents representing teaching assistant,patient,and expert roles were constructed. The base models DeepSeek-R1-Distill-Qwen-7B,MMed-Llama-3-8B,and DeepSeek-V3 were selected,and specialized datasets covering inquiry knowledge,nurse-patient dialogue,and inquiry evaluation were constructed. The agents were trained using supervised fine-tuning,prompt engineering,or a combination of both,producing seven candidate versions for each agent type. Following preliminary evaluation,experts selected the optimal version of each role based on the QUEST assessment framework. Agent optimization and deployment were performed on the Coze platform,and the mini-program was launched on the WeChat platform. Usability was evaluated by twenty nursing students and five experts using an overall usability questionnaire and a heuristic evaluation,respectively. Results The DeepSeek-V3 model trained with prompt engineering was selected as the optimal version and was applied to develop the nursing inquiry teaching mini-program. Usability evaluation showed that the scores of 20 nursing students for system quality,information quality,interface quality,and overall quality were 1.41 ± 0.42,1.66 ± 0.57,1.75 ± 0.70,and 1.56 ± 0.46,respectively. In the heuristic evaluation conducted by five experts,the median scores for all items were ≤1.00. Conclusion The nursing inquiry teaching mini-program shows favorable usability and offers intelligent support for nursing inquiry education.

Key words: Artificial Intelligence, Large Language Models, Nursing Inquiry, Usability, Mini-Program