Agentic education · Learning science

Actionable companion · Motivation & engagement

Learning with agency: Artificial intelligence literacy among nursing students and its association with learning engagement

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background Current research does not examine how distinct AI literacy profiles are differentially associated with learning engagement, thereby impeding the development of stratified and precise training plans for nursing students. Objective To identify latent profiles of artificial intelligence literacy among undergraduate nursing students, characterize their distributional features, and examine the relationship between distinct AI literacy profiles and learning engagement. Methods The study included 479 Chinese undergraduate nursing students who finished the Utrecht Work Engagement Scale-Student Version and the Artificial Intelligence Literacy Scale. Latent profile analysis was conducted using item-level AI literacy scores as manifest indicators. Results Three distinct profiles of AI literacy were identified: low literacy—ethically cautious, medium literacy—balanced development, and high literacy—fully mature. Non-parametric test results demonstrated significant differences in learning engagement and its dimensions across the three AI literacy profiles. After controlling for relevant confounding factors in multilevel linear regression analyses, AI literacy profile remained significantly associated with learning engagement, accounting for an additional 31.2% of the variance. Students in the medium and high AI literacy groups demonstrated significantly higher levels of learning engagement compared to those in the low literacy group. Conclusion Undergraduate nursing students’ AI literacy is heterogeneous and markedly related to learning engagement. These findings provide valuable insights for improving student engagement in AI-supported learning environments. Keywords: artificial intelligence literacy, latent profile analysis, learning engagement, technology acceptance model, undergraduate nursing students

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This multicenter cross-sectional study was conducted from February to March 2026. A convenience sampling approach was utilized to enroll undergraduate nursing students from three universities located in Zhejiang Province, China. The selected institutions are geographically distributed across the southern, eastern, and northern regions of the province, providing a degree of regional representativeness. The inclusion criteria comprised: (1) Full-time undergraduate nursing students; (2) Those who have given informed consent and voluntarily participated. Exclusion criteria included: (1) Those with mental or psychological disorders; (2) Individuals who have been absent from their studies for a duration exceeding 1 month due to medical or personal leave.

Limitations and uncertainty · source passage

This study has several limitations. First, the cross-sectional design captures data at a single time point and cannot establish causal relationships among variables; longitudinal or interventional studies are needed for verification. Second, although this study adopted a multi-center convenience sampling design, all participants were recruited from a single province in China. Consequently, the generalizability of the findings to nursing students in other provinces or regions should be interpreted with caution. Future research should adopt larger-scale, multi-center designs based on probability sampling frameworks, such as stratified random sampling. Third, the dependency on self-reported measures of AI literacy and learning engagement may introduce bias. Subsequent research could incorporate objective metrics, such as AI skill practice assessments or digital learning platform analytics. Fourth, LPA categorization depends on model fit indices and may vary across samples; the stability of these classifications should be tested in independent cohorts. Finally, cultural context may influence findings, as participants were exclusively Chinese nursing undergraduates. It remains to be determined whether these conclusions apply across other cultural or educational settings.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Min Li; Yue Cao; Ruilin Zhang; Jingquan Gao; Yuqian Ma; Xuefen Lan. Artificial intelligence literacy among nursing students and its association with learning engagement. Frontiers in Public Health (2026-08-14). https://doi.org/10.3389/fpubh.2026.1928543

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.