Heterogeneity in Nursing Students’ Artificial Intelligence Literacy: A Study of Self-Efficacy, Attitudes Toward Technology, and Ethical Concerns Based on Latent Profile Analysis
This study utilizes latent profile analysis on 522 nursing students in Inner Mongolia to identify three distinct AI literacy subgroups and reveals that self-efficacy, attitudes toward AI, and ethical concerns are key predictors of these profiles, thereby advocating for stratified educational interventions to enhance future nurses' competence in AI-driven healthcare.
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
Imagine the world of healthcare is like a massive, bustling spaceship. For years, the crew (nurses and doctors) has run the ship using maps, compasses, and their own sharp wits. But now, a new, incredibly powerful engine has been installed: Artificial Intelligence, or AI. This isn't just a fancy calculator; it's a smart co-pilot that can help diagnose patients, monitor their vitals, and even suggest personalized care plans. But here's the catch: having a super-engine doesn't help if the crew doesn't know how to fly it. "AI literacy" is the skill set needed to understand, use, and ethically manage this new technology. It's not just about knowing how to press buttons; it's about understanding how the engine thinks, trusting it when it's right, and knowing when to double-check its work. As the future crew of this spaceship, nursing students are learning to fly, but are they all on the same flight path? Some might be expert pilots, while others are just staring at the controls, confused. This is the question researchers wanted to answer: Are all nursing students learning to fly this new ship in the same way, or are there different types of learners with different strengths and weaknesses?
This study, conducted by a team at Inner Mongolia Minzu University, decided to stop looking at nursing students as a single, blurry crowd and instead used a special statistical microscope called "Latent Profile Analysis" to see the distinct groups hiding within the class. Think of it like sorting a bag of mixed-up LEGO bricks. Instead of just counting how many red or blue bricks there are (the old way), they looked at how the bricks were actually assembled to find three distinct "models" of students.
The researchers surveyed 522 nursing students in December 2025. They asked them about their AI skills, how confident they felt using AI (self-efficacy), their attitudes toward the technology, and how worried they were about the ethical side of things (like privacy and bias). The results revealed that the students weren't all the same; they fell into three very clear groups:
- The "Foundational Weakness" Group (42.25%): These students are the ones who are a bit lost. They have the lowest scores across the board. They might understand the basic idea of AI, but when it comes to actually using it in a real nursing scenario, they struggle the most. They are the passengers who know the plane has an autopilot but are afraid to touch the controls.
- The "Development" Group (42.41%): This is the middle pack. They have a decent grasp of the basics and aren't afraid to try things out, but they aren't quite experts yet. They are the crew members who can fly the plane in good weather but might get nervous during a storm. They have room to grow.
- The "Comprehensive Development" Group (15.34%): These are the future captains. They score high in everything: they know the theory, they are confident in using the tools, and they have a strong handle on the ethical rules. They are ready to take the helm.
The study found something interesting about the "Application" part of AI literacy. Even the smartest group (the 15.34%) scored lower on actually using the tools compared to their knowledge of ethics and theory. It's like having a pilot who knows the physics of flight perfectly but hasn't had enough time in the cockpit to practice landing. The researchers suggest this is because nursing schools often teach the ideas of AI but don't give enough hands-on practice with real clinical tools.
So, what makes a student land in the "Captain" group instead of the "Confused Passenger" group? The study suggests three main ingredients:
- Confidence (Self-Efficacy): Students who believed they could use AI were much more likely to be in the high-performing group. If you think you can't do it, you probably won't try, and you won't get better.
- Attitude: Students who had a positive, open mind toward AI technology were more likely to be the experts. If you think AI is scary or useless, you won't learn to use it well.
- Ethical Concerns: This one is a bit tricky. Students who worried a lot about AI ethics (like privacy and fairness) were less likely to be in the "Confused" group. It seems that caring about the rules helps you avoid the bottom tier. However, just worrying about ethics didn't automatically make someone a "Captain." You need the confidence and the positive attitude, too, to reach the top.
The authors conclude that we can't just teach everyone the same way. Since the students are so different, the education needs to be tailored. The "Confused Passengers" need basic training and hands-on practice to build their foundation. The "Middle Pack" needs more scenario-based drills to sharpen their skills. And the "Captains" should be challenged with complex research and data analysis tasks. By matching the teaching style to the student's current "flight level," nursing schools can ensure that when these students graduate, they are all ready to safely and effectively fly the future of healthcare.
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