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Generative AI use among medical and nursing students across educational levels

Source article: Differences in perceived influences and educational support needs of generative AI among medical and nursing students across educational levels: a descriptive qualitative study

Generative artificial intelligence (GAI) has been extensively integrated into nursing and medical education, significantly transforming the academic practices of students. Nonetheless, the varying impacts of GAI on core competency of student across educational stages remain ambiguous, with limited empirical evidence on hierarchical educational support needs. Drawing on cross-sectional comparisons of GAI-related experiences among undergraduate, master’s, and doctoral students, this study aimed to explore differen…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1832· cite & verify
Differences in perceived influences and educational support needs of generative AI among medical and nursing students across educational levels: a descriptive qualitative study
USAID, USACE team up to renovate nursing school in Georgia (5138984843) by U.S. Army Corps of Engineers Europe District from Wiesbaden, Germany. Public domain
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P 70The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

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G 72The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

Researchers interviewed 44 students at a medical university in western China, drawn from undergraduate, master's and doctoral levels, to learn how they use generative AI and how it affects their studies. Undergraduates mostly used it for coursework, while postgraduates used it for literature reviews, experimental design and data analysis.

The findings suggest that universities training health professionals may need support tailored to each stage rather than a single approach to AI literacy. The evidence rests on students' own perceptions from a qualitative sample at one institution, so it does not measure actual changes in competence, and the proposed conceptual model still needs validation elsewhere.

Main points

  1. The sample was 16 undergraduate, 16 master's and 12 doctoral students from one western Chinese medical university.
  2. Undergraduates used generative AI mainly for coursework; postgraduates used it for literature reviews, experimental design and data analysis.
  3. Perceived benefits for critical thinking and knowledge acquisition were offset by concerns about over-reliance, academic integrity and cognitive offloading.
  4. Students at all levels wanted teacher guidance and institutional training.
  5. The authors propose a three-level extended Technology Acceptance Model as a preliminary framework.

The gain

Students perceived generative AI as benefiting critical thinking and knowledge acquisition, with research competency gains largely confined to postgraduates.

The problem

Students perceived generative AI as bringing risks of over-reliance, academic integrity breaches and cognitive offloading in their studies.

The rundown

The study used semi-structured interviews analysed through inductive content analysis, with the Technology Acceptance Model and AI Literacy frameworks informing the discussion. It compared perceived effects on critical thinking, research competence and academic integrity across educational stages.

Research competency gains were largely confined to postgraduate students, indicating that the benefits of generative AI were perceived differently by degree level. Concerns about over-reliance and cognitive offloading were noted alongside the perceived gains.

A pronounced demand for systemic support, including teacher guidance and institutional training, appeared at every level. The authors suggest this could inform stratified AI literacy education and stage-specific curricula in health professions education.

What this doesn’t fix

The authors say their proposed model is preliminary and needs further validation in diverse educational contexts; the sample is also from a single western Chinese medical university.

Sources

  1. Peer-reviewedBMC Medical Education2026-10-17

The debate