The study used a descriptive qualitative design, drawing on semi-structured interviews with students from a single western Chinese medical university. Each participant completed a brief paper-based background form and attended a one-on-one in-person interview, providing first-hand accounts of how they actually use tools such as large language models in their daily academic work. The researchers then applied inductive content analysis to the transcripts, allowing themes to emerge from the data rather than testing predetermined hypotheses. Two established frameworks—the Technology Acceptance Model, or TAM, and AI Literacy frameworks—informed the discussion of the findings, giving the team a theoretical vocabulary for describing why students accept, reject, or reflect on the technology.

What emerged was a clear split in usage patterns across academic levels. Undergraduates reported turning to generative AI primarily for coursework: drafting assignments, clarifying concepts, and completing routine academic tasks. Postgraduates, by contrast, described a far more sophisticated repertoire, deploying the technology for literature reviews, experimental design, and data analysis. This distinction matters because it suggests the technology is not simply amplifying existing academic effort—it is being woven into fundamentally different kinds of cognitive work at different stages of training. The tools that help a first-year student understand a concept are the same tools that help a doctoral candidate design an experiment, but the cognitive stakes and the potential for harm are entirely different.

Students across all levels perceived real benefits for critical thinking and knowledge acquisition. Interview accounts suggested that engaging with generative AI could push learners to articulate questions more precisely, evaluate the quality of AI-generated responses, and consolidate their understanding of complex material. For undergraduates, this often meant using the technology as a kind of Socratic partner—asking it to explain a concept, then probing the explanation for gaps. For postgraduates, the perceived benefits extended into research competence, with master’s and doctoral students reporting that generative AI helped them navigate vast literatures, refine experimental logic, and interpret datasets more efficiently than they could alone.

But the perceived gains came with a counterweight. Students described a cluster of risks that they believed could undermine the very competencies the technology was supposed to strengthen. Over-reliance emerged as a central concern: the fear that habitual use of generative AI would erode the capacity for independent reasoning rather than enhance it. Academic integrity was another recurring theme, with students wrestling with the boundary between legitimate assistance and misconduct. Perhaps most striking was the concept of cognitive offloading—the technical term for delegating mental work to an external system. Students recognized that when they offload tasks such as summarizing, synthesizing, or problem-solving to generative AI, they may be sacrificing the cognitive effort that produces durable learning.

The perceived benefits were not distributed evenly across the educational spectrum. Research competency gains, the study found, were largely confined to postgraduates. Undergraduates reported benefits primarily for knowledge acquisition and critical thinking, while master’s and doctoral students described advantages that extended into the technical machinery of research itself. This asymmetry suggests that generative AI functions differently depending on the cognitive maturity of the user: for early-stage learners, it is a comprehension aid; for advanced learners, it is a research instrument. The distinction has direct implications for how AI literacy education should be designed, because the skills needed to use the technology safely as a comprehension aid are not the same as those needed to use it rigorously as a research tool.

A pronounced demand for systemic support pervaded every academic level. Students across all stages called for teacher guidance and institutional training, indicating that they do not feel equipped to navigate generative AI on their own. This demand was not limited to technical instruction—students wanted help with the ethical, epistemic, and practical dimensions of AI use. The consistency of this finding across educational levels suggests that the need for structured support is not a transitional problem that disappears with experience, but a persistent feature of the technology’s integration into health professions education. Students at every stage recognized that the risks of unguided use—over-reliance, integrity violations, cognitive offloading—require deliberate institutional intervention rather than individual trial and error.

Based on the qualitative findings, the research team proposed an extended three-level TAM model as a preliminary conceptual framework for capturing students’ different generative AI use patterns. The model describes three distinct orientations: a perceived ease-of-use-driven orientation, a perceived usefulness-driven orientation, and a critical reflection orientation. The framework is designed to explain why students at different stages engage with the technology in fundamentally different ways—undergraduates may be drawn primarily by how easy the tools are to use, postgraduates by how useful they are for research, and advanced learners increasingly by their capacity for critical reflection on the technology’s outputs and limitations. The researchers emphasized that the model requires further validation in diverse educational contexts before it can be considered a robust theoretical tool.

The study’s implications extend beyond the classroom. As generative AI becomes embedded in clinical decision support, diagnostic reasoning, and patient communication, the competencies that medical and nursing students develop during training will shape how they use the technology in practice. The finding that perceived risks shift across educational levels suggests that AI literacy education may need to be adaptive rather than uniform—tailored to the specific cognitive work that learners are performing at each stage. For undergraduates, this might mean structured exercises that use generative AI to strengthen comprehension while guarding against over-reliance. For postgraduates, it might mean training in the rigorous evaluation of AI-assisted literature reviews, experimental designs, and data analyses. For doctoral students, it might mean developing the critical reflection needed to interrogate the technology’s outputs and limitations in the context of original research.

The researchers acknowledge that their study has constraints. It drew on students from a single western Chinese medical university, and the sample of 44 participants, while sufficient for qualitative analysis, cannot capture the full diversity of experiences across institutions, cultures, and educational systems. The proposed three-level TAM model remains preliminary and requires further validation in varied contexts. Nonetheless, the study provides empirical evidence for constructing an AI-literacy education system tailored to learners at different levels, and it highlights a question that educators worldwide are only beginning to confront: not whether generative AI belongs in health professions education, but how its integration should be calibrated to the distinct developmental needs of students at every stage of training. The answer, this study suggests, lies in recognizing that the technology’s perceived influences are not uniform—they are as differentiated as the learners themselves.

Subject of Research: Perceived influences and educational support needs of generative AI among medical and nursing students across educational levels

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

Article References: Zhou, G., Li, B., Wen, T., Wen, H., Sun, T., Zhang, H., Li, X., Wang, L., & Wang, Y. (2026). Differences in perceived influences and educational support needs of generative AI among medical and nursing students across educational levels: a descriptive qualitative study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10637-5

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10637-5

Keywords: generative AI, medical education, nursing education, AI literacy, qualitative research, critical thinking, academic integrity, cognitive offloading, Technology Acceptance Model, health professions education, undergraduate students, postgraduate students