The study, led by researchers affiliated with the Second Xiangya Hospital of Central South University, was conducted between August and December 2025. Teachers at 16 medical colleges across central, eastern, and western China voluntarily forwarded a standardized anonymous survey link to nursing students. Eligible participants were full-time associate degree, undergraduate, or master’s students who had used an adaptive virtual simulation platform at least twice in their current-semester courses. After applying strict quality controls—including a minimum completion time of 120 seconds, patterned-response detection, and an embedded attention-check item—2,016 valid questionnaires remained from 2,030 returned.

The platform under study, developed by Beijing Oubeier Software Technology Development Co. Ltd., included more than 50 nursing-skill modules covering fundamentals of nursing and medical-surgical nursing, with each simulation task typically lasting 20 to 30 minutes. Importantly, the researchers describe its adaptive functions as rule-based rather than genuinely intelligent: the system compared students’ recorded operational steps, decision pathways, and response times against predefined task rules and performance thresholds to trigger branch-specific feedback and adjust scenario difficulty. No individual platform logs were linked to the survey responses.

The sample was predominantly female (83.48 percent) and aged 19 to 24 (88.39 percent), comprising 1,580 undergraduate students, 380 associate degree students, and 56 postgraduates. The researchers measured four core constructs using validated instruments: the System Usability Scale (SUS), the extraneous cognitive load subscale of Leppink’s Cognitive Load Scale, an adapted nine-item version of the Flow in Education Scale (EduFlow-2), and the nine-item Utrecht Work Engagement Scale for Students (UWES-S). All scales showed acceptable to excellent reliability, with Cronbach’s alpha values ranging from 0.749 to 0.899.

The descriptive picture was sobering for platform designers. The mean usability score translated to roughly 56.4 on the conventional 0-to-100 SUS scale—below the widely used benchmark of 68 that signals above-average usability. In other words, students on average perceived the system as only moderately usable, even as they reported moderate levels of load, flow, and engagement.

Structural equation modeling revealed a clear pattern of statistical associations. Greater perceived usability was associated with lower extraneous cognitive load (standardized coefficient β = −0.497), the load imposed by confusing interfaces and unclear instructions rather than by the learning material itself. Higher extraneous load was in turn associated with lower flow experience (β = −0.481)—the absorbing state in which learners concentrate fully under balanced challenge—and greater flow was strongly associated with higher learning engagement (β = 0.492). Usability also showed a direct positive association with engagement (β = 0.213).

Bootstrap mediation analysis with 5,000 resamples supported a sequential indirect association: usability → lower extraneous load → greater flow → higher engagement, with a standardized indirect estimate of 0.116 and a 95 percent confidence interval excluding zero. Notably, neither of the two simpler two-step pathways—usability through load alone, or usability through flow alone—reached significance, suggesting that load and flow operate as a linked sequence rather than as independent routes. Extraneous load showed no direct association with engagement once flow was accounted for, implying that its influence on engagement is realized through learners’ experiential state.

The study also examined whether students’ digital readiness—an exploratory index combining prior AI tool use and self-perceived digital competence—changed these relationships. Multi-group analysis, backed by a continuous-variable interaction test, showed that the negative association between extraneous load and flow was stronger among students with lower digital readiness (β = −0.519) than among those with higher readiness (β = −0.374). For less digitally prepared students, a clumsy interface appears to be a more serious barrier to achieving immersive, engaged learning. The authors suggest practical remedies: pre-training orientation sessions, step-by-step guided tutorials, simplified navigation, minimal operational steps, and graduated technical support during initial system use.

The researchers are careful about the limits of their design. All variables were measured concurrently, so the findings describe cross-sectional statistical associations rather than causal effects; temporal ordering and causation cannot be established from this data. Common method bias was formally tested with Harman’s single-factor test and an unmeasured latent method construct approach, both of which suggested it was not a serious concern, but the data remain self-reported. The study also lacked objective learning outcome measures such as Objective Structured Clinical Examination scores, and the purposive dissemination through 16 colleges means the sample cannot be considered nationally representative of Chinese nursing students.

Even so, the implications for nursing education are concrete. As AI-based simulation spreads—publications on AI in nursing grew from 38 in 2014 to 563 in 2024 by one count—institutions should prioritize usability testing and iterative design improvement when procuring educational technology, treat clear navigation and concise instructions as core pedagogical features rather than cosmetic details, and build differentiated support for students with varying digital readiness. The study’s message is that engagement with AI simulation is not simply a matter of supplying advanced technology; it depends on whether the interface lets students spend their limited working memory on nursing knowledge instead of on wrestling with the system itself.

Subject of Research: Cross-sectional associations among AI system usability, extraneous cognitive load, flow experience, and learning engagement in nursing simulation education.

Article Title: AI System Usability and Learning Engagement in Nursing Simulation: Cross‐Sectional Statistical Indirect Associations Through Extraneous Cognitive Load and Flow Experience

Article References: Liu, M., Zhang, P., Wu, Y., Pan, L., & Li, L. (2026). AI System Usability and Learning Engagement in Nursing Simulation: Cross‐Sectional Statistical Indirect Associations Through Extraneous Cognitive Load and Flow Experience. Nursing Open, 13(9), Article e70855. https://doi.org/10.1002/nop2.70855

Image Credits: AI Generated

DOI: 10.1002/nop2.70855

Keywords: artificial intelligence, virtual simulation, nursing education, system usability, cognitive load, flow experience, learning engagement, digital readiness, structural equation modeling, cross-sectional survey, nursing students, educational technology