Objective To construct a nursing evidence dataset，develop a generative artificial intelligence-based nursing evidence question-answering model，and evaluate its usability，with the aim of providing real-time decision support for clinical nurses. Methods Domestic and international nursing literature was comprehensively retrieved and classified according to the “5S” evidence pyramid. A training dataset was constructed，comprising more than 6，000 evidence summaries，over 15，000 guidelines and expert consensuses，and more than 500，000 systematic reviews. A domestically developed open-source large language model with strong performance was selected as the base model. Based on the training dataset，over 800，000 instruction fine-tuning sample pairs and more than 700，000 nursing evidence were generated. Supervised fine-tuning and retrieval-augmented generation were employed to train and develop a nursing evidence question-answering model. In September 2025，23 clinical nurses were recruited via convenience sampling to test the model. The usability of the model was evaluated using a self-developed model usability evaluation questionnaire. A lower score indicates better usability. Results All 23 nurses completed the model trial. In the model usability evaluation questionnaire，the scores were as follows：model quality dimension（1.99±0.85） points，information quality dimension（2.59±1.03） points，interface quality dimension（2.28±1.25） points，user satisfaction dimension（2.76±1.00） points，intention to use dimension（2.19±1.32） points，and net benefits dimension（2.03±0.78） points. Conclusion The overall usability of the nursing evidence question-answering model was good，and its use may help clinical nurses obtain evidence-based nursing information more efficiently. Future work could further expand the model’s functions and enrich the output information.
