Nursing students' critical engagement with generative AI in evidence-based practice education: A qualitative...

The rapid integration of generative artificial intelligence (GenAI) into nursing education presents both opportunities and challenges, yet empirical evidence on students' critical engagement with AI-generated content within assessment contexts remains limited.

To examine undergraduate nursing students' reflections when comparing their own evidence-based summaries with AI-generated outputs in response to the same clinical research questions.

A qualitative descriptive design was employed using retrospective analysis of 497 assessment submissions from an undergraduate nursing cohort at an Australian university. Students formulated a research question, synthesised peer-reviewed evidence, submitted the same question to an AI tool, and critically reflected on the comparison. Data were analysed using qualitative content analysis and thematic analysis.

Four themes were identified: 1. Credibility, quality of evidence and academic rigour. Students identified fabricated references, outdated information, and absence of peer-reviewed sourcing as key limitations. Additionally, students reflected on algorithmic limitations and the challenge of verifying AI outputs without prior topic knowledge; 2. Critical thinking, depth of analysis, and human intelligence. AI was perceived as unable to replicate contextual reasoning or multi-source synthesis; 3. Efficiency, accessibility, and practical utility. AI's speed and clarity were valued for brainstorming and initial scoping; and 4. Student identity, learning, and professional development were shaped by the view that engaging in manual, hands-on research was integral to forming a safe, evidence-informed nursing identity.

This study suggests that structured AI-comparison tasks offer the opportunity to develop AI and digital health literacy in nursing students. Students are neither naively accepting of AI nor reflexively dismissive but are actively working to understand its place within the ethical frameworks of nursing education. These findings contribute to AI integration in nursing education and offer practical guidance for educators seeking to support graduates to be AI-critical and well-equipped to leverage the efficiencies of these tools.