Artificial intelligence literacy and self-efficacy among nursing students: A network analysis of educational stratification
This study utilizes network analysis to reveal that AI literacy and self-efficacy among nursing students form a moderately interconnected structure centered on "Assistance," with significant variations in network organization between associate and bachelor's degree students that suggest a need for differentiated educational strategies.
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 just a concept from science fiction; it is quietly reshaping how healthcare works, from helping doctors make decisions to managing patient records. For the nurses of tomorrow, understanding these tools is becoming as essential as knowing how to administer medication. But knowing that a tool exists is different from feeling capable of using it. This is where two ideas come into play: literacy and confidence. Literacy is the actual ability to understand, use, and judge artificial intelligence. Confidence, or what researchers call self-efficacy, is the inner belief that one can successfully operate that technology. While educators know these two things are linked, the precise way they influence each other inside a student's mind has remained a bit of a mystery. Does knowing how to use a tool make you feel braver, or does feeling brave make you want to learn more? And does this relationship change depending on whether a student is in a shorter, more practical training program or a longer, more theoretical university degree?
A team of researchers from the University of Electronic Science and Technology of China set out to map these connections. They gathered 776 nursing students from five different schools in Sichuan province and asked them to answer detailed questions about their knowledge of artificial intelligence and their confidence in using it. Instead of just looking at average scores, the researchers used a method called network analysis. Imagine the different parts of a student's knowledge and confidence as distinct rooms in a house. Some rooms are connected by wide, sturdy doors, while others have narrow hallways or no doors at all. The researchers wanted to see which rooms were the main hubs that held the whole house together, and which doors connected the "knowledge" part of the house to the "confidence" part. They also wanted to see if the layout of this house looked different for students in associate degree programs compared to those in bachelor's degree programs.
The study revealed that the most important room in the entire house was the one labeled "Assistance." This represents a student's belief that artificial intelligence is actually helpful and valuable for their tasks. This single belief acted as the central hub, connecting almost everything else. It was the strongest link between knowing about AI and feeling confident using it. When a student truly felt that AI was a useful helper, it boosted their entire system of skills and confidence. The second most important room was "Usage," which refers to the actual ability to operate these tools. The researchers found that the more students felt AI was useful, the more they tended to use it, and the more they used it, the more their overall competence grew. However, the study also uncovered some surprising, negative connections. For instance, students who felt very comfortable interacting with AI that sounded or acted like a human were actually less likely to feel confident in their practical usage skills. Similarly, a strong focus on technical skills sometimes came at the expense of understanding the ethical rules of AI, suggesting that students who were very good at the mechanics might be overlooking the moral implications.
The researchers then looked at how this "house" was built differently for students at different stages of their education. While the overall number of connections was similar for both groups, the most important rooms changed. For students in associate degree programs, which tend to focus more on hands-on practice, the most central rooms were "Evaluation"—the ability to judge if AI output is correct—and "Comfort with AI." These students relied heavily on their ability to critically check the work of the machine and their emotional ease with the process. In contrast, for students in bachelor's degree programs, which often involve more theoretical study, the central hubs were "Usage" and "Assistance." These students were more driven by the practical application of the tools and their perception of the tools' value. The study suggests that the way students organize their skills and confidence depends on their educational background.
These findings offer a clear path for educators who want to train the next generation of nurses. Because the "Assistance" belief is the core driver, the most effective way to build confidence is to show students exactly how AI can help them in real, tangible ways. Simply teaching the technical steps is not enough; students need to see the value first. The study also suggests that training should be tailored. For students in shorter, practical programs, educators should focus on building critical thinking skills so students can confidently judge AI outputs, while also creating a low-stress environment to build emotional comfort. For students in longer degree programs, the focus should be on deepening their operational skills and reinforcing the idea that AI is a powerful partner in their learning. By understanding exactly how these pieces fit together, schools can stop guessing and start building training programs that truly prepare nurses for a future where artificial intelligence is a standard part of their daily work.
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