Key Takeaways
- Only about one‑third of nurses at Sir Lester Bird Medical Centre recognized that AI already intersects with nursing practice.
- Most nurses learned about AI from television, radio, or social media rather than formal education or workplace training.
- Despite limited knowledge, a majority (≈53 %) believe AI will revolutionise nursing, and many see value in specific AI‑enabled tasks such as automated documentation and decision support.
- Job‑security concerns are low: ≈64 % disagree that AI will soon replace nurses, reflecting confidence in the irreplaceable human elements of care.
- Nurses with bachelor’s or master’s degrees are significantly more likely to endorse AI usefulness than those with diploma or associate qualifications (odds ratio ≈ 0.27).
- Practising nurses and faculty are expected to be competent in AI, implying an institutional obligation to provide continuing education and faculty development.
- The study’s findings align with international research but highlight unique challenges for small‑island developing states, where informal media dominate AI awareness.
- Effective AI education should move quickly from motivational content to practical, case‑based instruction, be tiered for varying educational backgrounds, and prioritize training of educators first.
Introduction and Context
Artificial intelligence is rapidly reshaping healthcare, yet the voices of registered nurses—those who spend the most time at the bedside—are often absent from the conversation. A recent study conducted at Antigua and Barbuda’s national referral hospital, Sir Lester Bird Medical Centre, offers one of the first detailed looks at how nurses in the English‑speaking Caribbean perceive AI. Led by Kadian Henry of The University of the West Indies, Five Islands Campus, the research surveyed 86 of the hospital’s 179 registered nurses (a 48.1 % response rate) to assess knowledge, attitudes, and perceived competency needs regarding AI in nursing practice. As the authors note, “the nursing workforce in at least one Caribbean nation is not resistant to artificial intelligence, it is simply uninformed about it, and that distinction matters enormously.” This opening framing sets the stage for a nuanced exploration of readiness versus resistance.
Study Design and Methodology
The investigation combined descriptive statistics with formal psychometric evaluation. Participants completed demographic questionnaires and items probing their awareness of AI, the channels through which they encountered AI‑related information, and their views on the competencies nurses should possess in an AI‑enabled workplace. Attitudes were measured using five‑point Likert‑type scales, and the internal consistency of the attitude scale was examined via Cronbach’s alpha, which yielded an excellent value of 0.891, indicating the scale reliably captured nurses’ overall disposition toward AI. To examine associations between background characteristics and specific attitudes, the team employed ordinal logistic regression, appropriate for ordered response categories such as “disagree,” “neutral,” and “agree.” Differences in perceived competency requirements across nursing students, practising nurses, and faculty were tested with Cochran’s Q and McNemar tests, suited for repeated or matched categorical data. This rigorous mixed‑methods approach provides a solid statistical foundation for the study’s conclusions.
Knowledge Gap Findings
The headline finding on knowledge was sobering: only 34.9 % of respondents indicated that they were aware of AI’s application in nursing practice, meaning roughly two out of three nurses did not recognise that artificial intelligence already intersects with their daily work. As the article states, “Only 34.9 percent of respondents indicated that they were aware of AI’s application in nursing practice, meaning roughly two out of three nurses at the hospital did not recognise that artificial intelligence already intersects with their daily work.” This substantial knowledge gap underscores a critical disconnect between the pervasive presence of AI in health‑care technology and the frontline staff’s awareness of its relevance to their roles.
Sources of AI Information
When asked where they had learned about AI, nurses reported reliance on informal media far outweighing formal channels. Television and radio topped the list, cited by 81.4 % of respondents, followed closely by social media at 80.2 %. In stark contrast, only 27.9 % named educational institutions and a mere 16.3 % identified the workplace as sources of AI information. This pattern reveals that “nurses are learning about transformative clinical technology primarily from entertainment media and social platforms rather than from the professional institutions responsible for preparing them for practice,” a situation that risks superficial or inaccurate understandings of AI’s capabilities and limitations.
Attitudes Toward AI Impact
Despite the knowledge deficit, nurses’ attitudes were strikingly positive and, in some respects, visionary. A majority—52.9 %—agreed or strongly agreed that AI would revolutionise nursing. Large proportions endorsed specific clinical applications as useful: 67 % favoured automated visit documentation, 56.0 % supported AI‑generated intervention recommendations, 55.3 % saw value in automated problem‑list generation, 50.6 % each approved of AI identifying appropriate care interventions and recommending care‑coordination strategies, and 50.5 % valued AI‑assisted assessment of social determinants of health. These endorsements suggest that once concrete use cases are described, nurses can readily see how algorithmic tools might relieve documentation burdens and augment clinical decision‑making rather than simply abstract away from their lived work.
Job Security and Excitement Perceptions
Job security emerged as a reassuring theme: when asked whether AI would soon replace nurses, 64.3 % disagreed or strongly disagreed, indicating confidence that the irreplaceable human elements of nursing—physical assessment, emotional support, advocacy, ethical judgment, and hands‑on care—remain difficult to automate. Conversely, nurses were more ambivalent about the experiential dimension of AI: 63.5 % were neutral on whether AI would make nursing more exciting, reflecting a wait‑and‑see posture rather than outright enthusiasm or dread. This nuanced stance highlights that while nurses see practical benefits, they remain uncertain about how AI will affect the intrinsic satisfaction of their work.
Education Level and Acceptance Gradient
Regression analysis uncovered a meaningful education effect. Nurses holding diploma or associate degrees were substantially less likely than those with bachelor’s or master’s degrees to believe that automated identification of care interventions would be useful, with an odds ratio of 0.27 and a 95 % confidence interval of 0.11 to 0.64. Because this interval excludes 1.0, the association is statistically significant, and the magnitude is considerable: degree‑qualified nurses had roughly a quarter to a third of the odds of their lower‑credentialed colleagues of endorsing this particular application. The finding points to a gradient of technological acceptance that tracks educational exposure, raising equity concerns for workforces where diploma‑level preparation remains common. If AI literacy concentrates among the most highly educated nurses, hospitals risk creating a two‑tier environment in which enthusiasm and competence for digital tools are unevenly distributed across shifts and units.
Competency Expectations Across Roles
The competency analysis added a normative dimension to the picture. Respondents were asked whether different groups—nursing students, practising nurses, and nursing faculty—should be competent in various technical domains of AI. Expectations were higher for practising nurses and faculty than for students, a difference that reached statistical significance at p < 0.016. As the authors observe, “faculty are expected to teach emerging content and practising nurses to apply it, while students are still acquiring fundamentals.” This pattern implies a clear institutional obligation: if practising nurses and educators are expected to be competent in AI, then hospitals and nursing schools must provide the continuing education, faculty development, and infrastructure that make such competence achievable; otherwise, the expectation becomes an unfunded mandate on clinicians already stretched by staffing pressures.
Comparison with International Literature
The Antigua findings resonate with, and extend, a growing international literature on nursing and AI. Studies conducted in North America, Europe, and Asia have similarly reported moderate awareness, generally favourable attitudes, and strong support for integrating AI content into nursing curricula. However, the Caribbean context adds distinctive weight. Small island developing states often face acute health‑workforce migration, limited continuing‑education infrastructure, and uneven access to digital health technologies. In such settings, the gap between informal information channels and formal professional training may be even wider than in resource‑rich health systems, and the cost of leaving AI literacy to television and social media is correspondingly higher. The authors argue that targeted AI education is needed, and this study supplies the baseline evidence on which such programmes can be built, establishing where knowledge currently stands and which attitudes can be leveraged in curriculum design.
Implications for Targeted AI Education
What would effective targeted education look like? The study’s findings offer concrete guidance. Because attitudes are broadly favourable but knowledge is thin, curricula should move quickly from motivational content to practical, case‑based instruction covering real applications such as automated documentation, clinical decision support, and predictive risk scoring—exactly the use cases the nurses rated most useful. Because nurses with lower academic credentials show less acceptance of some applications, training should be tiered and accessible rather than assuming a graduate‑level starting point. And because faculty are expected to be competent, investment in educator preparation must come first, since a curriculum on AI delivered by instructors unfamiliar with the technology will not survive contact with a sceptical classroom. The nurses’ neutrality about whether AI will make their work more exciting also suggests that education should emphasise not only skills but realistic framing, showing how algorithmic tools can return time to the bedside rather than adding another layer of screen‑based work.
Conclusion and Recommendations
The study arrives at a moment when healthcare systems worldwide are deciding how fast and how far to integrate AI into clinical workflows, and its central message is deceptively simple: the nursing workforce in at least one Caribbean nation is not resistant to artificial intelligence; it is simply uninformed about it, and that distinction matters enormously. Resistance requires persuasion; uninformed readiness requires only good teaching. With strong internal consistency in its attitude measurement (Cronbach’s α = 0.891), statistically grounded associations between education and acceptance, and a clear mandate from the respondents themselves, this research provides a template that other small‑island and resource‑constrained health systems can adapt. As AI tools continue their advance into documentation, decision support, and care coordination, the nurses of Antigua have signalled that they are willing partners in that transformation—provided someone finally teaches them what the technology actually is.
Nurses’ knowledge and attitudes toward artificial intelligence in Antigua hospital