Work

Exploring nursing students’ acceptance of RAG-enhanced GenAI through the AIDUA model: A qualitative study

Year: 2025

Type: article

Abstract: AIM: This study examined undergraduate nursing students' experiences and acceptance of NurCaseAI, a GenAI-based interactive case study system powered by retrieval-augmented generation (RAG) technology. The investigation was guided by the Artificially Intelligent Device Use Acceptance (AIDUA) model. BACKGROUND: Clinical reasoning is a critical skill for undergraduate nursing students. The emergence of generative AI (GenAI) offers opportunities to support clinical learning; however, concerns about content accuracy and conversational quality remain. Retrieval-augmented generation (RAG) has been proposed to enhance GenAI systems by grounding outputs in validated nursing knowledge, improving reliability for educational use. DESIGN: A qualitative exploratory design was adopted. METHODS: 16 third-year nursing students from a Chinese university were purposively selected after completing three thyroid perioperative nursing case scenarios using NurCaseAI. Semi-structured interviews were conducted, transcribed and analyzed using thematic analysis. RESULTS: Five key themes were identified: 1) Perceived system capabilities and limitations as students viewed NurCaseAI as an effective learning aid with analytical constraints; 2) Learning enhancement benefits where RAG content aligned well with coursework and guidelines; 3) Interaction efficiency challenges including initial barriers in system adaptation; 4) Limited emotional engagement as students viewed the system as task-oriented rather than empathetic; 5) Strong system acceptance with willingness to continue using NurCaseAI despite limitations. CONCLUSION: NurCaseAI demonstrates promise as a supplementary GenAI tool for nursing education, particularly in perioperative and case-based learning. The AIDUA model offered a valuable lens to understand students' acceptance patterns. Future improvements should enhance affective interaction and support more complex clinical communication.

Source: Nurse Education in Practice

DOI: 10.1016/j.nepr.2025.104612

Authors: Mingyan Shen, Yanping Shen, Zuxin Shi

Institution: Zhejiang Shuren University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Quality education

Open Access status: hybrid

Estimated APC paid: $3,970