Artificial intelligence-related perceptions and E-health literacy among nursing students in Palestine: A cross-sectional study
Abstract
Artificial intelligence (AI) and e-health literacy are increasingly relevant to nursing education; however, the relationship between nursing students’ perceptions and acceptance of AI and their perceived ability to locate, evaluate, and apply online health information remains insufficiently examined in Palestine. This cross-sectional study examined e-health literacy, AI-related technology-acceptance perceptions, Internet-use factors associated with these constructs, and whether overall AI-related perceptions were independently associated with e-health literacy among 113 third- and fourth-year nursing students. Participants completed the 8-item eHealth Literacy Scale (eHEALS) and a 28-item adapted technology-acceptance questionnaire covering seven AI-related perception domains, with negatively worded items reverse scored where required. Descriptive statistics, regrouped one-way analyses of variance, Pearson correlations with Holm adjustment for multiple testing, and multiple linear regression were performed. The mean eHEALS score was 3.53 (SD 0.59), and the overall AI-related perceptions score was 3.35 (SD 0.34). Overall AI-related perceptions were positively correlated with eHEALS (r = 0.387, Holm-adjusted p < .001). Facilitating conditions showed the largest domain-level correlation with eHEALS (r = 0.450, Holm-adjusted p < .001), whereas anxiety was not significantly associated with eHEALS. After adjustment for year of study, perceived Internet skills, and perceived importance of the Internet, overall AI-related perceptions remained independently associated with eHEALS (B = 0.643, 95% CI 0.289–0.998, p < .001; model R2 = 0.177). Among the regrouped Internet-use comparisons, only perceived usefulness of the Internet remained significant after Holm correction. Overall, AI-related perceptions and perceived e-health literacy were positively associated in this sample, although the cross-sectional design precludes conclusions regarding directionality or causality. Further multi-site, longitudinal, and intervention studies are warranted, and domain-level findings should be interpreted cautiously because several adapted subscales demonstrated low internal consistency.