Cross-sectional survey identifies two LLM-dependence profiles among 709 Chinese nursing students

A cross-sectional survey of 709 nursing students at two Chinese universities identified two response profiles associated with large language model dependence, according to the study abstract. The students had a mean score of 61.77 ± 9.77 on the Large Language Model Dependence Scale. Of the participants, 51.76% were classified as Functional-Dominant and 48.24% as Global-Dependent.

The survey was conducted from May through June 2026 using a general information questionnaire and the Large Language Model Dependence Scale. The researchers used latent profile analysis to classify students’ response patterns. Gaussian and Bayesian network analyses were then applied to characterize relationships among scale items, while computer-simulated perturbations assessed potential educational targets.

In the Gaussian network analysis, “Discomfort” was a central item overall and within the Functional-Dominant profile, whereas “Reliable results” was central in the Global-Dependent profile. The statistically upstream Bayesian nodes were “Work efficiency” and “Reluctant to start” overall; “Discomfort,” “Honesty,” and “Predictability” in the Functional-Dominant group; and “Excellent assistant” and “Discomfort” in the Global-Dependent group. These network results describe modeled relationships rather than causal effects.

The largest simulated increases and decreases were associated with “Reluctant to start” and “Discomfort” overall, “Spend more time” and “Boring tasks” in the Functional-Dominant group, and “Work efficiency” and “Excellent assistant” in the Global-Dependent group. The abstract describes these perturbations as candidate educational targets, not as tested interventions or demonstrated improvements. Because the available evidence is an abstract-level report, the study’s broader limitations, including sample representativeness and the implications of its cross-sectional design, cannot be evaluated in full from the supplied information.

Subject of Research: Large language model dependence profiles and candidate educational targets among nursing students at two Chinese universities

Article Title: Profiles and candidate educational targets of large language model dependence among Chinese nursing students: a cross-sectional study

Article References: Deng, Y., Liao, L., Wang, X., Wu, S., Chen, F., & Ye, Z. (2026). Profiles and candidate educational targets of large language model dependence among Chinese nursing students: a cross-sectional study. BMC Nursing. https://doi.org/10.1186/s12912-026-05370-9

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05370-9

Keywords: large language models, nursing students, dependence, cross-sectional survey, latent profile analysis, Bayesian networks, Gaussian networks, computer simulation, China, nursing education, Profiles, candidate