Artificial intelligence in nursing education: Student perspectives on benefits, risks, and readiness for NCLEX-RN

Abstract

Background and objectives: Artificial intelligence (AI) is increasingly embedded in higher education, yet little qualitative research has examined how undergraduate nursing students perceive and use AI in their learning. This study explored students’ experiences with AI for personalized learning, ethical decision making, and National Council Licensure Examination for Registered Nurses (NCLEX-RN) preparation.

Methods A descriptive qualitative phenomenological design was used to capture the perspectives of baccalaureate nursing students at a four year Midwest university. Fifty seven students completed an open ended survey and one student participated in an interview. Data were analyzed using qualitative thematic analysis.

Results Across the four research questions, students described a wide range of AI supported learning practices and concerns. For general learning use, students reported leveraging AI for personalized learning support, study guide creation, practice questions and exam preparation, and engagement through interactive tools. When describing challenges, students emphasized accuracy and reliability limitations, academic integrity and ethical risks, loss of critical thinking and clinical preparedness. Ethical perceptions are centered on trust, reliability, and patient safety, and accountability. For NCLEX-RN preparation, students highlighted AI’s role in exam focused question generation, content summarization, personalized study plans, and noted skepticism regarding AI’s alignment with evolving exam standards.

Conclusions Overall, students viewed AI as a multifaceted learning tool that enhances personalization and exam readiness while simultaneously raising concerns about accuracy, ethics, and the preservation of critical thinking and clinical reasoning skills.

Keywords

Academic integrity / Artificial intelligence / Ethical perceptions / NCLEX-RN preparation / Nursing students / Personalized learning

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