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Artificial Intelligence and Nursing Values: Students’ Perceptions of Trust, Empathy, and Future Roles

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11 October 2026

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12 October 2026

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Abstract
Background: Artificial intelligence (AI) is becoming increasingly embedded in healthcare systems, creating new opportunities to enhance efficiency, optimize workflows, and strengthen patient monitoring. At the same time, its integration raises important questions about ethics, trust, and the preservation of humanistic values in nursing practice.Aim: To investigate nursing students’ awareness, attitudes, trust, and visions regarding the integration of AI in healthcare and nursing practice.Methods: A descriptive cross‑sectional design was applied, involving 140 nursing students. Data were gathered through a structured questionnaire that explored awareness, attitudes, trust, and expectations regarding AI in healthcare. The analysis combined descriptive statistics with inferential techniques, including correlation and regression, to identify relationships among the variables.Results: Seventy‑two percent of participants had heard of AI in medicine, but knowledge levels were moderate (M = 3.3). While 43% believed AI could support nurses and 66% saw potential for improved patient care, 60% expressed discomfort with AI‑led care. Empathy and human contact were rated as highly important (M = 3.6 and M = 3.7 respectively), and 83% rejected full replacement of nurses. Trust was limited, with only 32% believing AI reduces medical errors. A majority (57%) envisioned a human‑centered future, with AI serving as a supportive tool rather than a substitute.Conclusion: Nursing students recognize the potential of artificial intelligence (AI) but remain cautious, emphasizing humanistic values and expressing skepticism about safety and trust. They envision a human‑centered future where AI supports nursing practice without replacing its essential human role, highlighting the need for balanced integration through education, policy, and further research.
Keywords: 
artificial intelligence
;  
nursing education
;  
empathy
;  
human contact
;  
trust
;  
patient monitoring
;  
nursing students’ attitudes
;  
future of healthcare
Subject: 
Public Health and Healthcare  -   Nursing

Introduction

Artificial intelligence (AI) has quickly become a transformative element in healthcare, transforming clinical decision-making, enhancing patient monitoring, and reshaping nursing education. Scholars emphasize that while AI offers opportunities to improve efficiency and reduce medical errors, it also raises significant ethical and safety concerns (Martinez-Puertas et al., 2026). The integration of AI into nursing practice is particularly complex, as nursing is grounded in empathy, human contact, and holistic care—dimensions that technology cannot easily replicate (Muthukumar, 2025).
Recent studies highlight the potential of AI in nursing education and patient monitoring, suggesting that it can enhance data visibility and support chronic illness care (Wei et al., 2025; Kubota, 2026; Joo et al., 2026). Systematic reviews further demonstrate AI’s growing role in pediatric nursing, clinical decision-making, and educational frameworks (Loutfy et al., 2026; Cant et al., 2026; Alrazeeni et al., 2026). However, concerns persist regarding trust, job security, and the preservation of core nursing values (Cueto et al., 2026; Sayed et al., 2026).
The literature consistently underscores the tension between technological innovation and humanistic care. Topol (2019), in Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, argues that AI should make healthcare “more human again,” while Jiang, Jiang, and Mao (2017) trace its evolution from experimental applications to mainstream adoption. More recent work explores how resilience and attitudes mediate nurses’ perceptions of AI, emphasizing the need for balanced integration (Cura et al., 2026; Kanat et al., 2026). These findings suggest that while AI can augment nursing practice, its role must be carefully defined to avoid undermining empathy and patient-centered care.
In the Albanian context, digital health integration is already underway. The University Hospital Center “Mother Teresa” (QSUT) has implemented the Hospital Information System (SISP), which manages electronic patient records, laboratories, and radiology services (Qendra Spitalore Universitare “Nënë Tereza”, 2025). The Ministry of Health has also reported on the implementation of SISP in regional hospitals (Ministria e Shëndetësisë dhe Mbrojtjes Sociale, 2024) and launched the National Digital Health Strategy 2025–2030 to unify patient data and strengthen e-health infrastructure (Ministria e Shëndetësisë dhe Mbrojtjes Sociale, 2025). Alongside the public sector, private institutions such as Spitali Amerikan, Hygeia, Klinika Uzllova, Continental, and Salus have adopted integrated clinical management systems, offering advanced diagnostic and imaging services (author’s observation).
Against this backdrop, the present study investigates nursing students’ perceptions of AI in healthcare. By examining awareness, attitudes, trust, and visions for the future, this research contributes to ongoing debates about how AI can be integrated into nursing practice without compromising its humanistic core. The results provide insights into the readiness of future nurses to engage with AI technologies and highlight areas where education, policy, and practice must adapt.

Methods and Materials

Study Design

This study employed a descriptive quantitative design, using an electronic questionnaire administered through Google Forms. The design was chosen to capture nursing students’ perceptions regarding the potential role of Artificial Intelligence (AI) in nursing practice.
Population and Sample The study population consisted of Bachelor of Nursing students enrolled at Aldent University, Tirana, Albania. A total of 140 students participated voluntarily.
  • Inclusion criteria: active enrollment in the Bachelor of Nursing program and provision of informed consent.
  • Exclusion criteria: incomplete responses or withdrawal during data collection.
Data Collection and Procedures Data collection took place between May 1–15, 2026, through a questionnaire link shared via institutional communication channels. Participation was entirely voluntary and anonymous, with informed consent obtained from all students. Students were briefed on the study objectives and confidentiality measures before completing the survey.
Research Instrument The research instrument was a structured questionnaire consisting of 15 items.
  • Items assessed demographic characteristics, prior exposure to AI technologies, and attitudes toward AI’s role in nursing.
  • Attitudinal items were measured using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
  • Some items were binary (Yes/No) to capture direct perceptions.
  • The questionnaire was pilot tested for clarity and reviewed by faculty experts to ensure content validity.
Measures and Variables 
  • Independent variables: demographic and educational factors (age, gender, year of study, prior exposure to AI).
  • Dependent variables: attitudes toward AI in nursing, perceptions of AI as supportive or substitutive, trust in AI, and comfort with AI-led care.
Data Analysis 
Data were analyzed using SPSS version 29.
  • Descriptive statistics summarized participant characteristics and responses.
  • Inferential analyses were conducted to explore relationships between variables, including chi-square tests, Pearson correlations, linear regression, and factor analysis.
  • Statistical significance was set at p < 0.05.
  • Results were presented in tables for clarity.
Ethical Declaration 
Ethical clearance for the study was granted by the Institutional Review Board of Aldent University. Participation was entirely voluntary and anonymous, with informed consent obtained from all respondents. Strict confidentiality was maintained, and no personal identifiers were collected. The research was conducted in accordance with the ethical principles of the Declaration of Helsinki.

Results

Descriptive Analysis 
  • Total participants: 140 Bachelor of Nursing students, Aldent University.
  • Awareness of AI in medicine: 72% had heard of AI use in healthcare; 28% had not.
  • Level of information: 7% not informed at all, 17% little informed, 23% moderately informed, 46% well informed, 7% very well informed (Mean = 3.3 ± 1.1).
Attitudes toward AI 
  • 43% believed AI could support nurses in their work; 57% did not.
  • 66% believed AI improves patient care to some extent (14% not at all, 20% little, 32% moderately, 34% a lot).
  • 60% reported discomfort with AI-led care (40% comfortable, 60% uncomfortable).
  • 83% rejected the idea that AI could fully replace nurses.
  • 34% accepted partial replacement, while 66% opposed it.
Humanistic Values 
  • 91% considered human contact very important in nursing care (Mean = 3.7 ± 0.6).
  • 75% rated empathy as very important (Mean = 3.6 ± 0.7).
Trust and Safety 
  • Only 32% believed AI reduces medical errors; 68% remained skeptical.
  • Trust in AI decisions was low: 63% reported “not at all,” 28% “a little,” and only 9% “a lot” (Mean = 1.5 ± 0.7).
  • 45% believed AI could monitor patients 24/7; 55% did not.
Future of Healthcare 
  • 57% envisioned a human-centered future, 23% a hybrid model, 17% a technological model, and 3% were uncertain.
Comparative Analysis 
  • Chi-square test: A statistically significant relationship was found between level of information and belief that AI improves patient care (χ² = 12.47, p < 0.05).
  • Consensus: 83% opposed the full replacement of nurses by AI.
Correlation Analysis 
  • Positive correlation between level of information and perception of AI as supportive in patient care (r = 0.41, p < 0.01).
  • Negative correlation between skepticism about medical errors and belief that AI supports nurses (r = −0.29, p < 0.05).
Regression and Factor Analysis 
  • Predictors of trust in AI:
  • o Level of information (β = 0.34, p < 0.01).
  • o Attitude toward human contact (β = −0.27, p < 0.01).
  • Factor analysis: Two main dimensions explained 61% of total variance:
  • o Technological factor (informatization, equipment).
  • o Human factor (empathy, human contact).
Statistical Significance All analyses were interpreted at a threshold of p < 0.05, ensuring validity and reliability.
Table 1. Summary of Responses to 15 Survey Questions with Means (n = 140). 
Table 1. Summary of Responses to 15 Survey Questions with Means (n = 140). 
Q No. Survey Question Response Options Key Results (%) Mean ± SD
1 Have you heard about the use of AI in medicine? Yes / No Yes 72%, No 28% —
2 How informed do you feel about AI? 1 = Not at all → 5 = Very informed 7% / 17% / 23% / 46% / 7% M = 3.3 ± 1.1
3 Do you think AI can help nurses? Yes / No Yes 43%, No 57% —
4 Do you think AI improves patient care? 1 = Not at all → 5 = A lot 14% / 20% / 32% / 34% M = 2.9 ± 0.9
5 Would you feel comfortable if AI cared for you in hospital? Yes / No Yes 40%, No 60% —
6 Importance of human contact in nursing care 1 = Not important → 5 = Very important 6% / 14% / 80% M = 3.7 ± 0.6
7 Can AI fully replace nurses? Yes / No Yes 17%, No 83% —
8 Tasks AI can perform better Multiple choice Informatization 32%, Equipment 17%, All 6%, None 23%, Other 22% —
9 Tasks that should never be replaced Multiple choice Nursing work 68%, Surgery 11%, None 9%, Other 12% —
10 Does AI reduce medical errors? Yes / No Yes 32%, No 68% —
11 Trust in AI decisions 1 = Not at all → 5 = A lot 63% / 28% / 9% M = 1.5 ± 0.7
12 Can AI monitor patients 24/7? Yes / No Yes 45%, No 55% —
13 Will AI partially replace nurses? Yes / No Yes 34%, No 66% —
14 Importance of empathy in nursing 1 = Not important → 5 = Very important 14% / 11% / 75% M = 3.6 ± 0.7
15 Future of healthcare Human / Technological / Both / Don’t know Human 57%, Tech 17%, Both 23%, Don’t know 3%
Table 2. Interaction Table with Statistical Data. 
Table 2. Interaction Table with Statistical Data. 
Domain Questions Focus Key Findings (%) Statistical Interaction
Knowledge & Awareness Q1, Q2 Awareness & self-perception 72% had heard of AI; 23% moderately informed; 46% well informed Higher knowledge scores positively correlated with openness to AI (r = 0.42, p < 0.01).
Attitudes Toward AI in Nursing Q3, Q4, Q5 Practical acceptance 43% believed AI helps nurses; 66% saw improved care; 60% uncomfortable with AI-led care Regression shows knowledge (Q2) predicts attitudes (Q3, Q4), but comfort (Q5) remains low regardless (β = 0.18, p < 0.05).
Human Element Q6, Q14 Humanistic values Human contact rated very important by 91%; empathy rated very important by 75% Strong humanistic values negatively associated with acceptance of AI replacement (Q7, Q13).
Replacement & Role of AI Q7, Q8, Q9, Q13 Role definition 83% rejected full replacement; 34% accepted partial replacement Logistic regression: empathy (Q14) strongly predicted rejection of replacement (OR = 2.8, p < 0.01).
Trust & Safety Q10, Q11, Q12 Reliability & trust 32% believed AI reduces errors; trust low (63% “not at all”) Trust mediated the effect of knowledge on acceptance: higher knowledge → more trust (indirect effect, p < 0.05).
Future of Healthcare Q15 Vision 57% envisioned a human-centered future; 23% hybrid; 17% technological; 3% uncertain Future vision aligned with empathy scores: students with higher empathy more likely to choose “human-centered” (χ² = 12.4, p < 0.01).

Discussion

Awareness and Knowledge of AI Our findings show that 72% of nursing students had heard of AI in medicine, yet their self-rated knowledge remained moderate (M = 3.3). This aligns with broader literature indicating that while awareness is increasing, depth of understanding is limited (Almarwani & Hebeshy, 2026; Lai et al., 2026). This gap highlights the need for structured AI literacy programs within nursing curricula to strengthen confidence and competence.
Attitudes Toward AI in Nursing Practice Although 43% of students believed AI could support nurses, 60% reported discomfort with AI-led care. This ambivalence reflects ethical concerns and psychological factors. Kanat, Çetinkaya, and Durak (2026) found that anxiety among nursing students significantly influenced attitudes toward AI adoption, suggesting that emotional readiness is as important as technical knowledge in shaping acceptance.
Humanistic Values: Empathy and Contact Students overwhelmingly rated human contact (M = 3.7) and empathy (M = 3.6) as essential dimensions of nursing practice. Muthukumar (2025) argues that empathy represents a non-substitutable aspect of care. Our findings reinforce that strong humanistic values limit full acceptance of AI integration, emphasizing the irreplaceable role of interpersonal connection in nursing.
Replacement and Role Definition The rejection of full replacement (83%) and partial acceptance (34%) mirrors global debates. Kubota (2026) highlights policy challenges in defining AI’s scope in nursing, while Martinez-Puertas et al. (2026) show nurses resist AI in medication error prevention due to role identity concerns. Our data confirm that students distinguish between technical tasks (e.g., informatization, monitoring) suitable for AI, and humanistic tasks (e.g., empathy, direct care) that must remain nurse-led.
Trust and Safety Only 32% of students believed AI reduces medical errors, and trust levels were low (M = 1.5). This skepticism echoes findings by Cueto et al. (2026), who reported nurses’ reluctance to rely on AI-generated bedside information. Similarly, Cant, Ryan, and Chugh (2026) concluded that clinical decision-making remains contested due to trust deficits. Building transparency and reliability into AI systems is therefore critical for acceptance.
Future of Healthcare A majority (57%) envisioned a human-centered future, with only 17% favoring a purely technological model. This resonates with Topol’s (2019) argument that AI should make healthcare “more human again” rather than replace human roles. Our results suggest that students see AI as supportive, not dominant, consistent with Joo et al. (2026) and Loutfy et al. (2026), who emphasize AI’s role in augmenting rather than supplanting nursing practice.
In parallel, Albanian health policies are not only advancing digitalization but also emphasize strict protection of patient data. Specific legislation regulates the collection, storage, and use of medical information, ensuring that patients’ rights to privacy and confidentiality are safeguarded. These frameworks align with broader European standards and reinforce the principle that technological innovation in healthcare must coexist with strong ethical and legal guarantees.

Conclusion and Recommendations

Conclusion and Recommendations 
This study demonstrates that nursing students are increasingly aware of artificial intelligence (AI) in healthcare, yet their knowledge remains moderate and their attitudes are marked by ambivalence. While many recognize the potential of AI to support nursing tasks and patient monitoring, they consistently emphasize the irreplaceable importance of human contact and empathy. Trust deficits and skepticism about error reduction limit acceptance, and discomfort with AI-led care persists. Students foresee a human-centered future where AI supports, but does not replace, nurses.
To address these challenges:
  • Nursing education should incorporate structured programs that build AI literacy and enhance understanding of its applications, ethical implications, and safety mechanisms.
  • Curricula and policies must safeguard humanistic values, ensuring that empathy and personal contact remain central to nursing practice.
  • Developers and institutions should prioritize transparency and reliability to build trust.
  • Policymakers must clearly define the scope of AI in nursing, distinguishing technical tasks suitable for automation from those requiring human judgment and compassion.
  • Future healthcare models should promote balanced integration, where AI augments nursing practice without undermining its human essence.
  • Further research should explore evolving attitudes and resilience among nursing students across diverse contexts, ensuring that educational and policy frameworks remain adaptive.

Acknowledgments

The authors express their sincere gratitude to the nursing students of Aldent University, Tirana, Albania, for their voluntary participation and valuable insights, which made this study possible. Additionally, we extend our appreciation to the Faculty of Nursing and Physiotherapy for their administrative support in facilitating seamless data collection and maintaining a constructive academic environment throughout the research process.

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