Nursing Students in Palestine Link Confidence With AI to Sharper Digital Health Skills

Artificial intelligence is quietly reshaping the way future nurses will learn, diagnose, and deliver care, but a new study from Palestine suggests that the technology’s success in hospitals may depend less on the algorithms themselves and more on how comfortable students feel using them. A cross-sectional survey of 113 third- and fourth-year nursing students, published in PLOS Digital Health, has found that students who hold more positive perceptions of AI also report a stronger ability to find, judge, and apply health information online. The finding, drawn from a region where digital health infrastructure faces persistent challenges, offers one of the first quantitative glimpses into how the next generation of Palestinian nurses is preparing for an AI-infused clinical world.

The research team, led by Samar Thabet Jallad with Maysa Kassabry and Adel Taher Takruri, set out to answer a deceptively simple question: is there a measurable relationship between how nursing students perceive and accept AI and how confident they are in their own electronic health literacy? E-health literacy, a concept formalized in the widely used eHEALS instrument, describes a person’s perceived capacity to search for health information on the Internet, evaluate its credibility, and put it to practical use. In an era when patients arrive at clinics armed with search-engine results and clinicians rely on digital decision-support tools, that capacity has become a core professional competency rather than a bonus skill.

Methodologically, the study leaned on two validated instruments. The first was the eight-item eHealth Literacy Scale, which asks respondents to rate statements about their ability to access and appraise online health resources. The second was a 28-item questionnaire adapted from technology-acceptance research, organized into seven domains covering AI-related perceptions such as perceived usefulness, perceived ease of use, facilitating conditions, and anxiety, with negatively worded items reverse-scored where required. Together, the two scales produced a paired portrait of each student: one score capturing digital health confidence, the other capturing attitudes toward AI adoption in practice.

The headline numbers are striking for their balance. The mean eHEALS score came in at 3.53 on the scale used, with a standard deviation of 0.59, while the overall AI-related perceptions score averaged 3.35 with a standard deviation of 0.34. Both figures sit in the moderate-to-positive range, suggesting that these students neither worship nor fear the technologies arriving in their future workplaces. More revealing, however, was the statistical relationship between the two constructs. Overall AI-related perceptions correlated positively with eHEALS scores, with a Pearson correlation coefficient of 0.387 that remained significant after the researchers applied a Holm adjustment to guard against false positives across multiple tests.

Drilling down into the seven perception domains produced the study’s most technically interesting result. Facilitating conditions, a construct describing whether students believe the resources, infrastructure, and support exist to make AI usable, showed the largest domain-level correlation with e-health literacy at r = 0.450, again surviving Holm correction. In plain terms, students who felt that their environment would actually support AI use were also the ones most confident in navigating digital health information. Anxiety, by contrast, showed no significant association with eHEALS, a null result that challenges the common assumption that fear of new technology is the primary barrier to digital competence among health trainees.

To test whether the association was genuine rather than an artifact of demographics or Internet habits, the researchers built a multiple linear regression model. After adjusting for year of study, perceived Internet skills, and perceived importance of the Internet, overall AI-related perceptions remained independently associated with e-health literacy, with an unstandardized coefficient of 0.643 and a 95 percent confidence interval running from 0.289 to 0.998. The model explained roughly 17.7 percent of the variance in eHEALS scores, a modest but meaningful share for a psychological construct measured in a single sitting. Among the regrouped Internet-use comparisons, only perceived usefulness of the Internet remained significant after Holm correction, hinting that students who see the web as genuinely valuable are better positioned to build both AI acceptance and digital health literacy together.

The context of the study matters as much as its statistics. Palestine’s nursing education system operates under conditions that most Western digital-health research rarely confronts: intermittent infrastructure, restricted movement, and limited access to cutting-edge clinical technology. That 113 students in this environment report moderate e-health literacy and generally positive AI perceptions is itself a finding, suggesting that digital readiness among health trainees is not simply a function of national wealth or institutional budgets. The authors note that the relationship between AI perceptions and e-health literacy had been insufficiently examined in this setting, making the study a baseline against which future regional comparisons can be measured.

Yet the researchers are careful, almost insistently so, about what the data cannot show. The cross-sectional design captures a single moment in time, which means the correlation between AI attitudes and e-health literacy could run in either direction or reflect a third factor entirely. Students confident in their digital skills may simply feel more sanguine about AI, or positive exposure to AI concepts may build broader digital confidence, or both traits may grow from the same underlying comfort with technology. No causal claim survives the snapshot design, and the authors explicitly call for multi-site, longitudinal, and intervention studies to untangle the direction of the relationship.

There is also a measurement caveat that serious readers should weigh. Several of the adapted subscales demonstrated low internal consistency, meaning the domain-level findings, including the prominent role of facilitating conditions, should be interpreted cautiously. Internal consistency reflects how well items within a subscale hang together as a measure of a single construct, and when that cohesion is weak, domain-level correlations can be unstable. The overall AI-perceptions score and the eHEALS instrument are on firmer ground, but the granular story about which specific attitudes matter most will need replication with refined instruments before it can guide curriculum design with confidence.

Even with those limits, the study lands at a consequential moment. Nursing schools worldwide are racing to decide how much AI education to embed in their curricula, and evidence about which attitudes predict digital competence is scarce, particularly from low- and middle-income settings. If the association observed here holds up under longitudinal scrutiny, it would suggest that interventions targeting students’ perceptions of AI, especially their beliefs about whether supportive conditions exist, could pay dividends in general e-health literacy, a skill that directly affects patient education and evidence-based practice. For now, the Palestinian data offer a modest but well-adjusted signal: the students most ready to embrace intelligent tools are also the ones most confident in their ability to separate reliable health information from noise online, and that pairing may prove to be one of the most valuable competencies a twenty-first-century nurse can carry to the bedside.

Subject of Research: The association between artificial intelligence perceptions and e-health literacy among nursing students in Palestine

Article Title: Artificial intelligence-related perceptions and E-health literacy among nursing students in Palestine: A cross-sectional study

Article References: Jallad, S. T., Kassabry, M., & Takruri, A. T. (2026). Artificial intelligence-related perceptions and E-health literacy among nursing students in Palestine: A cross-sectional study. PLOS Digital Health, 5(9), e0001737. https://doi.org/10.1371/journal.pdig.0001737

Image Credits: AI Generated

DOI: 10.1371/journal.pdig.0001737

Keywords: artificial intelligence, e-health literacy, nursing students, Palestine, technology acceptance, eHEALS, cross-sectional study, digital health, nursing education, facilitating conditions, health information, PLOS Digital Health