INTRODUCTION:

The integration of Artificial Intelligence (AI) is reshaping modern healthcare by enhancing diagnostic accuracy, supporting clinical decisions, streamlining administrative tasks, and offering personalized treatment approaches.¹ Technologies like machine learning, natural language processing, and robotic automation are becoming increasingly embedded in routine clinical functions, significantly improving both efficiency and quality of care.²

As key members of the healthcare team, nurses are at the forefront of patient care and often responsible for crucial decisions in clinical practice.³ When effectively integrated, AI can enhance nursing care by providing decision-making support, automating record management, monitoring patient conditions, and minimizing clinical errors. However, beyond the development of these technologies, the success of AI in healthcare heavily depends on the readiness of healthcare providers, including nurses, to adopt and apply such innovations.⁴

Despite the growing relevance of AI in clinical practice, nursing education often lacks structured content on AI concepts, digital health tools, or ethical implications of automated decisions.⁵ Consequently, it is important to evaluate nursing students’ understanding, attitudes, and preparedness for AI-based healthcare, as they represent the future professional workforce. This research seeks to assess the preparedness of nursing students to work in AI-supported clinical settings and identify educational gaps that require attention.⁶

NEED AND SIGNIFICANCE IN NURSING:

As healthcare globally shifts towards AI-enabled systems, it is vital for nurses to acquire competencies in emerging technologies to ensure safe and effective patient care .⁷ Nursing students, who will serve as future care providers, must possess not only clinical expertise but also the ability to navigate and collaborate with AI tools.⁸ Without appropriate preparation, they may experience apprehension or ineffective usage, which can compromise the quality of care.⁹

India is witnessing rapid advancements in digital healthcare initiatives. However, there is a lack of empirical evidence assessing how well nursing students are prepared for AI-assisted clinical environments.¹⁰Exploring their knowledge, perception, and level of preparedness will provide meaningful insights to guide curricular reforms and institutional strategies to support digital integration in nursing education.¹¹

STATEMENT OF THE PROBLEM:

A study to assess the preparedness of nursing students to integrate Artificial Intelligence (AI) technologies in health care settings at The Salvation Army Catherine Booth College of Nursing, Nagercoil, Kanyakumari District.

OBJECTIVES:

· To assess the perceived preparedness of nursing students to integrate AI technologies in clinical settings.

· To explore nursing students' awareness and understanding of AI applications in healthcare.

· To examine the readiness of nursing students for a tech-driven healthcare system.

METHODOLOGY:

This descriptive study was carried out at The Salvation Army Catherine Booth College of Nursing, Nagercoil, to explore how prepared nursing students are for integrating AI into healthcare practice. The study utilized a total enumeration sampling technique, including all 185 B.Sc. Nursing students as participants. A validated structured questionnaire was used, consisting of four sections:

· Section A: Demographic profile

· Section B: Preparedness to integrate AI

· Section C: Awareness and understanding of AI in healthcare

· Section D: Readiness for a technology-driven clinical environment

Responses were recorded on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Expert validation was sought for content relevance, and appropriate ethical measures were followed, including institutional approval and informed verbal consent from students. The collected data were analyzed using descriptive statistics, including frequencies, percentages, and means.

RESULTS AND DISCUSSION:

SECTION-A: Demographic variables

Table1: Distribution of sample according to their demographic variables. N=185

S. No

Characteristics

Frequency

Percentage

1. Age

1.1

1.2

1.3

1.4

1.5

18 Years old

19 Years old

20 Years old

21 Years old

22 Years old

18.9

22.2

25.9

22.7

10.3

2. Gender

2.1

2.2

Male

Female

155

16.3

83.7

3. Year of study

3.1

3.2

3.3

3.4

II semester

IV semester

VI semester

IV year

24.4

24.8

25.4

4. Have you ever read/ heard about AI

4.1

4.2

Yes

126

68.1

31.9

If yes, from which source

4.3

4.4

4.5

4.6

4.7

4.8

Newspaper

Television

Radio

Journals

Magazines

Podcast

9.5

38.9

25.3

14.5

3.9

7.9

5. Have any one taught you about AI

5.1

5.2

Yes

119

64.3

35.7

If Yes, from which source

5.3

5.4

5.5

5.6

5.7

Teachers

Friends

Internet

Relations

Influencers

26.9

46.2

1.7

4.2

6. Are you using AI in daily activities

6.1

6.2

Yes

If Yes, specify the reason

6.3

6.4

6.5

6.6

6.7

Smart phones

Internet and apps

Shopping

Home devices

Health care

67.4

32.6

The demographic profile of the 185 nursing students surveyed reveals a diverse sample in terms of age, gender, academic level, and exposure to artificial intelligence (AI). Most participants were between 18 and 22 years old, with the highest representation from 20-year-olds (25.9%). The majority were female (83.7%), and students were fairly evenly distributed across the II, IV, VI, and final semesters of their program. Notably, 68.1% of students had heard or read about AI, primarily through television (38.9%) and radio (25.3%), while fewer cited journals or magazines as sources. Around 64.3% had been taught about AI, mainly via the internet (46.2%), followed by friends (26.9%) and teachers (21%). Regarding AI usage in daily life, 48% reported using AI technologies, primarily through smartphones (67.4%) and internet-based applications (32.6%). However, none reported using AI for shopping, smart home devices, or healthcare, indicating a limited practical engagement with AI in those contexts. Overall, while awareness and interest in AI appear significant among students, direct and diversified usage remains limited, highlighting opportunities for enhanced digital education in nursing.

SECTION B: Perceived preparedness of nursing students to integrate AI technologies in health care settings.

Table 2. Mean value of perceived preparedness of nursing students to integrate AI technologies in health care settings. N=185

S. No	Perceived preparedness of nursing students to integrate AI technologies in clinical settings	Mean
1	I feel confident in understanding basic AI concepts	3.87
2	I am capable of working alongside AI-based tools in a clinical setting	2.26
3	My nursing curriculum has sufficiently prepared me to work with digital technologies	2.13
4	I am prepared to take additional training in AI to support my future practice	4.35
5	I would like AI-related modules integrated into our nursing education	4.16

The findings reveal that while students have a moderate level of confidence in their understanding of basic AI principles (mean = 3.87), they exhibit limited confidence in using AI tools within clinical environments (mean = 2.26). Additionally, they believe that their current nursing education does not adequately prepare them to work with digital health technologies (mean = 2.13). These outcomes are consistent with previous studies. For instance, in 2017 Topaz and Pruinelli⁵found that nurses often have only a surface-level understanding of AI, and Rokstad et al. (2021)¹² reported a lack of experiential learning with AI in clinical training. Similarly, Booth et al. (2021)¹³ noted the absence of structured digital health content in many nursing curricula, contributing to the preparedness gap. Despite these limitations, students demonstrated a strong interest in gaining AI-related knowledge, with a high readiness to pursue additional training (mean = 4.35) and a desire for AI content to be included in their courses (mean = 4.16). This proactive attitude supports earlier research by Amann et al. (2020)⁷ and Risling and Risling (2020)¹³, which emphasized the importance of positive student perceptions and curricular integration in fostering digital competence. Therefore, the study underscores the urgent need to revamp nursing education by incorporating AI literacy, hands-on learning opportunities, and collaborative approaches with other disciplines to better prepare future nurses for the digital transformation in healthcare.

SECTION C: Nursing students' awareness and understanding of AI applications in healthcare.

Table 3. Mean value of nursing students' awareness and understanding of AI applications in healthcare. N=185

S. No	Nursing students awareness and understanding of AI applications in health care	Mean
1	I am aware of how AI is being used in modern healthcare systems	3.05
2	I understand the potential applications of AI in patient monitoring.	2.26
3	I have learned about AI in my nursing coursework.	1.56
4	AI can support evidence-based nursing practice.	1.63
5	I can differentiate between AI, machine learning, and robotics.	2.26
6	AI tools can help reduce diagnostic and medication errors.	4.47
7	I understand how AI impacts patient safety and care quality.	4.31
8	AI can enhance nursing productivity through automation.	3.86
9	I have been exposed to AI-related tools during simulation or training.	2.41
10	I know about ethical issues related to the use of AI in healthcare.	2.19

The analysis of nursing students' awareness and understanding of artificial intelligence (AI) in healthcare reveals a mixed landscape, showing both growing recognition and notable educational shortcomings. While students demonstrate a fair awareness of AI's application in modern healthcare systems (mean = 3.05) and acknowledge its role in boosting nursing efficiency through automation (mean = 3.86), they exhibit a strong grasp of AI’s potential to minimize diagnostic and medication errors (mean = 4.47) and to enhance patient safety and quality of care (mean = 4.31). These positive perceptions suggest that students are beginning to understand the value of AI in improving health outcomes.

Despite this, the data also points to considerable gaps in foundational knowledge and formal education. Participants reported limited inclusion of AI content in their academic curriculum (mean = 1.56), a weak understanding of AI’s contribution to evidence-based nursing (mean = 1.63), and difficulty distinguishing between AI, machine learning, and robotics (mean = 2.26). These results echo Booth et al. (2021)¹³, who noted that many nursing programs lack structured instruction on digital technologies and AI, resulting in inadequate preparation for tech-integrated healthcare settings. Moreover, hands-on experience with AI through simulations or practical learning was scarce (mean = 2.41), and awareness of ethical considerations related to AI use in healthcare was also low (mean = 2.19) (WHO, 2021)¹⁴. This finding supports the concerns raised by Topaz and Pruinelli (2017)⁵, who stressed the importance of teaching the ethical, legal, and practical dimensions of AI in nursing programs. Similarly, Risling and Risling (2020)¹⁵ advocate for the integration of AI and informatics into the core nursing curriculum to equip future professionals with the digital competencies required.

SECTION D: Readiness of nursing students for a Tech-driven health care.

Table 4. Mean value on readiness of nursing students for a tech-driven health care. N=185

S. No	Readiness of nursing students for a tech-driven health care	Mean
1	I feel confident in adapting to emerging technologies in healthcare.	3.87
2	I am prepared to use AI tools in future clinical practice.	2.50
3	I am interested in learning more about AI and healthcare technology.	3.45
4	My nursing program has provided basic training on digital health tools.	2.54
5	I believe leadership in nursing requires digital literacy.	4.71
6	I am capable of critically evaluating AI-based clinical recommendations.	2.70
7	I would participate in a workshop or training session on AI for nurse leaders.	4.69
8	I am ready to advocate for ethical use of AI in clinical settings.	3.06
9	I understand the role of nurse leaders in implementing health technologies.	4.31
10	I can work collaboratively with IT and AI professionals in a hospital setting.	3.87
11	I think AI knowledge will give me an edge in future nursing roles.	4.27
12	I can mentor or educate peers on the responsible use of healthcare technologies	3.59
13	I am confident in leading a nursing team that uses digital health tools.	3.47
14	I feel prepared to influence AI-related policy or decision-making in my future role.	4.37
15	My nursing education should include leadership training focused on tech-driven healthcare systems.	4.78

The findings suggest that nursing students possess a strong interest and a moderate level of readiness for engaging in a healthcare system increasingly shaped by technology, especially in roles involving leadership and advocacy. A key insight is the overwhelming agreement among students that digital literacy is essential for nursing leadership (mean = 4.71), with even stronger support for incorporating leadership training tailored to technology-driven healthcare into nursing education (mean = 4.78). These insights reflect the changing demands on nurse leaders, who are now expected to embrace and promote technological advancements in clinical practice.

Additionally, a large proportion of students expressed enthusiasm for participating in AI-related workshops (mean = 4.69), and many believe that having AI knowledge will offer an edge in future nursing careers (mean = 4.27). These attitudes align with the perspectives of Risling and Risling (2020),¹⁷ who emphasized the need to embed digital leadership into nursing education to prepare students for modern healthcare environments.

Despite this enthusiasm, students report only moderate levels of practical preparedness. Their confidence in adapting to new technologies is average (mean = 3.87), and while interest in expanding their knowledge about AI is relatively high (mean = 3.45), their confidence in applying AI tools in clinical settings remains low (mean = 2.50). This underscores the necessity for more hands-on learning experiences and structured educational content. Moreover, students highlighted inadequate training on digital technologies within their current nursing programs (mean = 2.54), and only a moderate ability to assess AI-generated clinical suggestions (mean = 2.70). This is particularly concerning, as nurses must be equipped to make sound clinical judgments in partnership with digital systems. As Booth et al. (2021)¹³ noted, nurses’ capacity to effectively utilize AI is closely linked to the digital competencies offered through their formal education.

On a positive note, students show a willingness to advocate for ethical practices in AI use (mean = 3.06) and recognize the leadership role nurses play in deploying digital health technologies (mean = 4.31). They also report a fair degree of confidence in collaborating with IT and AI professionals (mean = 3.87), as well as mentoring peers in appropriate technology usage (mean = 3.59), indicating the rise of a digitally conscious mindset (Animesh D. Ahire et. Al, 2025)¹⁸. Moreover, students expressed a reasonable level of confidence in leading nursing teams with digital tools (mean = 3.47) and contributing to policy decisions involving AI in healthcare (mean = 4.37). These findings suggest a shift toward a leadership-focused outlook, although there remains a pressing need for enhanced institutional support and curriculum reform. As highlighted by Topaz and Pruinelli (2017)⁵, incorporating digital leadership into nursing education is essential for cultivating nurse leaders who can confidently and ethically navigate AI-integrated healthcare environments.

RECOMMENDATIONS:

1. Curriculum Integration: Include AI-related concepts, applications, and ethical discussions in nursing curricula. - Make digital health, informatics, and data science essential subjects in nursing education.

2. Faculty Development: - Organize training programs for nursing educators on AI tools and teaching strategies.

3. Simulation and Practical Exposure: - Establish simulation labs for students to experience AI-based patient monitoring and decision-making.

4. Workshops and Seminars: - Conduct regular awareness sessions and expert talks to build students’ confidence in AI.

5. Ethics and Leadership Training: - Teach students how to ethically implement AI and encourage leadership in digital health transformation.

6. Collaboration with Tech Professionals: - Facilitate interdisciplinary learning between nursing and IT/AI professionals.

7. Research and Policy Engagement: - Encourage nursing students to participate in AI-related research and health technology policy discussions.

CONCLUSION:

The research highlights a growing interest among nursing students in AI and its application in healthcare. However, it also identifies a significant gap in hands-on training, curriculum content, and confidence in practical application. These findings stress the need to update nursing education to align with current digital advancements. Incorporating AI literacy, practical simulations, ethics, and leadership training will be vital in preparing nursing graduates to function effectively in future AI-integrated clinical settings.

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Received on 18.08.2025 Revised on 06.10.2025

Accepted on 12.11.2025 Published on 21.02.2026

Available online from February 23, 2026

Asian J. Nursing Education and Research. 2026;16(1):15-19.

DOI: 10.52711/2349-2996.2026.00004

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.