Nursing students' readiness for and acceptance of artificial intelligence technologies in clinical skills training: A cross-sectional study.

Student summary

Why This Research Matters

This research explores how nursing students in Saudi Arabia feel about using artificial intelligence (AI) for learning clinical skills, like those practiced in simulations or labs. The study aims to understand if these future nurses are prepared and willing to use AI technologies that could help them learn better.

The problem the authors frame is this: As healthcare changes rapidly with new technologies, especially AI, it's important to know how ready nursing students are for these tools. If they aren't prepared or don't accept using AI in their training, it might not be effective and could even cause problems later when they start working as nurses. The study specifically looks at Saudi universities because there wasn't much known about this topic from that region before.

The research used a cross-sectional design, which means data was collected once from participants to get a snapshot of their views at one point in time. They surveyed 747 undergraduate nursing students across ten different universities in Saudi Arabia using an online questionnaire. This method is good for gathering information quickly and efficiently from many people.

The survey measured two main things: 'readiness' (how prepared they feel) and 'acceptance' (how willing they are to use AI). Readiness was broken down into four parts: 1. Cognition: Understanding what AI can do in healthcare. 2. Technical Ability: Feeling confident about using the actual technology. 3. Vision: Seeing how AI could improve nursing practice and patient care. 4. Ethics: Thinking about the moral issues of using AI, like privacy or fairness. Acceptance was measured through: 1. Perceived Usefulness: Believing that AI would make their learning better. 2. Perceived Ease of Use: Finding the technology simple to use. 3. Attitude: Having a positive feeling about using AI in training. 4. Behavioral Intention: Planning to actually want to use it when they can. The survey also asked for basic information like their year in school and if they had used AI before.

Data analysis involved looking at averages, comparing groups (like different academic years), checking relationships between variables (like readiness and acceptance scores), and seeing which factors were most important using a method called multiple linear regression. This helps identify what things are strongly linked to how ready or accepting students are.

The study found that overall, nursing students had a moderate to high level of readiness and acceptance for AI in clinical training. However, there were differences within these scores: * **Readiness:** Students scored highest on 'Vision' (seeing the benefits) and 'Ethics' (understanding moral aspects). They scored lowest on 'Technical Ability' (feeling confident with the tech itself). * **Acceptance:** Within acceptance, students had high scores for 'Attitude' (positive feelings) and 'Behavioral Intention' (wanting to use it), but lower scores for 'Perceived Ease of Use'. The study also found that: 1. Students in more advanced academic years generally showed higher readiness and acceptance. 2. Those with prior experience using AI had significantly better readiness and acceptance, though the effect wasn't huge. 3. There was a moderate positive link between overall readiness and overall acceptance; students who felt ready were also more likely to accept it.

For nursing students reading this, you should appraise several things: * **Sample:** The study included many students from various universities in Saudi Arabia (747 participants). This is good because it gives a broad view. However, since they used a 'convenience sample' (students who were easily available), the results might not perfectly represent all nursing students there. * **Questionnaire Validity:** The survey questions are based on established theories about technology adoption and readiness. This means the way these concepts were measured is likely sound. * **Statistical Methods:** The authors used appropriate statistical tests for their data type (e.g., descriptive stats, t-tests, ANOVA, Pearson correlation, multiple linear regression). These methods help find patterns and relationships in the data.

Regarding source/rights cautions: This paper is published in an academic journal. While it's important to cite this work properly if you use its findings, the specific rights for reuse (like copying text or figures) depend on the journal's copyright policy. The 'source-linked' rights status means that information about how to access and potentially reuse the content can be found through the original source links provided.

A nurse would reason from this evidence by considering several points: * **Curriculum Development:** Since students show high conceptual understanding (vision, ethics) but lower technical ability, nursing education programs should focus more on hands-on training with AI tools. This could involve integrating specific software or simulations into the curriculum. * **Student Support:** Students in later academic years are more ready and accepting. Programs might need to provide targeted support for earlier-year students who may be less familiar with technology. * **Prior Exposure Matters:** Even a small amount of prior AI exposure seems beneficial. Encouraging or providing opportunities for early exposure could improve overall readiness. * **Attitude is Key:** High positive attitudes and behavioral intentions are good signs that if the technical barriers can be addressed, students will likely embrace these technologies.

In summary, this study highlights that while Saudi nursing students generally see AI as a valuable tool for learning clinical skills (they understand its potential benefits ethically and conceptually), they feel less confident about actually using the technology themselves. This suggests a need to enhance practical training with AI in nursing education programs.

Faculty notes

Educational Relevance

This cross-sectional study investigates the readiness and acceptance of artificial intelligence (AI) technologies among undergraduate nursing students in Saudi Arabia, specifically within clinical skills training contexts. The research addresses an emerging gap concerning how future nurses perceive and prepare for AI integration into their education.

The authors frame a pertinent problem: as healthcare rapidly evolves with technological advancements like AI, it is crucial to assess the preparedness of nursing students. If they are not adequately ready or accept these technologies, implementation could be suboptimal or even counterproductive in educational settings. The study focuses on Saudi universities due to limited existing evidence from this region.

Methodologically, a cross-sectional descriptive correlational design was employed. Data were collected via self-administered online questionnaires distributed to a convenience sample of 747 undergraduate nursing students across ten universities in Saudi Arabia between January and December 2025 (implied by publication date). The survey instrument measured AI readiness (comprising cognition, technical ability, vision, and ethics) and AI acceptance (including perceived usefulness, perceived ease of use, attitude, and behavioral intention), alongside demographic and educational data. Statistical analysis involved descriptive statistics, independent-tests, one-way ANOVA, Pearson correlation, and multiple linear regression.

Key findings indicate that participants demonstrated a moderate to high level of overall readiness and acceptance for AI in clinical training. Within the readiness construct, scores were highest for 'Vision' (understanding potential benefits) and 'Ethics' (considering moral implications), while 'Technical Ability' was the lowest-rated subdomain. For acceptance, 'Attitude' (positive feelings towards AI use) and 'Behavioral Intention' (desire to use AI) were most highly rated.

Several significant associations emerged: 1. Academic year: More advanced students exhibited higher levels of both readiness and acceptance. 2. Prior AI exposure: Students with prior experience showed significantly higher readiness and acceptance scores, though the effect size was noted as small. 3. Readiness-Acceptance relationship: A moderate positive correlation was observed between overall readiness and overall acceptance; those who felt more ready were also more likely to accept AI technologies.

The study concludes that Saudi nursing students possess conceptual and ethical preparedness for AI integration but report gaps in technical competence. Academic progression is linked with higher readiness and acceptance, as is prior exposure. The authors suggest a need for enhanced attention to technical training within AI education to bridge this gap between attitudinal/ethical readiness and practical skills.

For faculty, the implications are clear: while students generally see value in AI for learning (high vision and ethics scores), their confidence in using it practically is lower. Curricula should therefore prioritize hands-on experience with AI tools alongside theoretical understanding. The positive correlation with academic year suggests that targeted support or earlier exposure might benefit younger cohorts. Furthermore, the small but significant impact of prior exposure underscores the value of integrating basic AI literacy into foundational nursing courses.