A Meta-synthesis of nursing students' experiences with generative artificial intelligence-assisted learning

To systematically evaluate the real experiences of nursing students participating in generative artificial intelligence-assisted learning.

Electronic searches were conducted in the China National Knowledge Infrastructure (CNKI), VIP Database, Scopus, Wanfang Data, and the China Biomedical Literature Database (CBM), Web of Science, PubMed, Cochrane, Embase, and CINAHL databases for qualitative studies on the experiences of nursing students with generative AI-assisted learning from the establishment of the databases to March 2026. Qualitative studies on nursing students' experiences with generative AI-assisted learning were screened, appraised, and synthesized using thematic synthesis.

Eighteen studies were included in the synthesis. A total of 49 themes were identified and organized into 13 categories, leading to four integrated findings: (1) dual experience of empowerment and challenges, (2) internal conflict between technology and nursing humanism, (3) user experience differentiation amid Practical Constraints, (4) general demand for supporting systems and educational reform.

This study found that nursing students' experiences with generative AI are shaped by both the opportunities and challenges associated with its use in learning. These findings highlight the need for nursing educators to strengthen students' AI literacy, critical thinking, and ethical awareness, for curriculum designers to integrate AI-related competencies into nursing curricula while maintaining a strong emphasis on humanistic care, and for policymakers to establish clear governance frameworks and educational guidelines to support the responsible use of AI in nursing education.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261367837, identifier CRD420261367837.