AI Literacy and Attitudes Toward Artificial Intelligence Among Undergraduate Nursing Students: A Cross-Sectional Survey
A cross-sectional survey of undergraduate nursing students in the UAE reveals that while they hold positive attitudes toward artificial intelligence, their objective AI literacy is critically low and independent of their enthusiasm or prior exposure, highlighting a significant familiarity–competence gap that necessitates explicit curricular integration of AI education.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Artificial intelligence is no longer a futuristic concept reserved for science fiction; it is quietly weaving itself into the fabric of modern healthcare. In hospitals and clinics, these smart systems help doctors and nurses sort through vast amounts of patient data, predict health risks, and manage the heavy burden of paperwork. For the nurses of tomorrow, this shift means their daily work will increasingly involve collaborating with machines that can think, analyze, and suggest actions. But for this partnership to work safely, the people using the technology need to understand how it works. It is not enough to simply be comfortable with a tool; one must also grasp its limits and the logic behind its decisions. This is where the question of literacy arises: do the students training to become nurses actually understand the artificial intelligence they will soon rely on, or are they merely familiar with its presence?
A team of researchers at Gulf Medical University in the United Arab Emirates set out to answer this question by looking directly at the future nursing workforce. They surveyed 177 undergraduate nursing students, asking them two distinct things. First, they wanted to know how the students felt about artificial intelligence. Second, and more importantly, they wanted to measure what the students actually knew. To do this, the researchers did not simply ask the students to rate their own knowledge, a method that often leads people to overestimate their skills. Instead, they gave the students a rigorous, thirty-one-question test designed to objectively measure their understanding of how artificial intelligence works, how it learns from data, and the ethical issues surrounding it. They paired this test with a separate survey to gauge the students' attitudes, asking whether they believed the technology would improve their lives and work.
The results revealed a striking disconnect between enthusiasm and understanding. The students were overwhelmingly positive about the future of artificial intelligence. Most believed it would improve their work and were eager to use it themselves. However, when faced with the objective test, their knowledge was surprisingly low. On average, the students answered only about eleven of the thirty-one questions correctly, which amounts to a score of roughly thirty-six percent. When the researchers categorized the students based on their performance, more than ninety percent fell into a low-knowledge group. Only a tiny fraction of the group demonstrated a solid grasp of the concepts. This finding suggests that while the students are ready to embrace the technology emotionally, they are not yet equipped to understand it intellectually.
Perhaps the most revealing part of the study was how the students' prior experience with artificial intelligence affected these results. The researchers found that students who had already encountered artificial intelligence tools in their clinical training or classroom lessons held even more positive attitudes toward the technology. Yet, this familiarity did not translate into better test scores. In fact, those who had seen the technology in action during their clinical practice scored significantly lower on the knowledge test than those who had not. This suggests a gap where exposure builds confidence without building competence. It appears that seeing a machine make a decision in a hospital setting makes a student feel more comfortable with the idea of artificial intelligence, but it does not necessarily teach them how the machine arrived at that decision or what might go wrong.
The researchers concluded that the current way nursing education handles artificial intelligence may be insufficient. The study indicates that simply letting students encounter these tools as they progress through their training is not enough to ensure they understand them. The positive attitudes are a good starting point, but without deliberate, structured teaching, that enthusiasm does not turn into the necessary skills to use the technology safely and critically. The authors argue that nursing programs need to treat artificial intelligence literacy as a specific subject that must be taught and tested, just like any other core medical skill. If the goal is to produce nurses who can work confidently alongside intelligent systems, the education system must move beyond assuming that exposure alone will create understanding. The students are ready to learn, but they need a curriculum that goes deeper than the surface level of the tools they see every day.
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