Metaphorical perspectives of pediatric nurses on the use of artificial intelligence in the education of children with chronic diseases

Abstract

Purpose

This study aimed to explore pediatric nurses’ perceptions of using artificial intelligence in the education of children with chronic diseases through metaphor analysis.

Design and methods

This study was designed as a qualitative descriptive study using metaphor-based content analysis. Sixty pediatric nurses from a university hospital participated. The data were collected using a semi-structured metaphor form and analyzed using content analysis. The metaphors generated by the participants were organized into thematic categories to identify underlying perceptions and attitudes toward artificial intelligence use in pediatric care.

Results

Most participants were female (95%), and only 20% reported prior experience with artificial intelligence. A total of 42 distinct metaphors were identified. These metaphors were grouped into five themes: “providing information and guidance role,” “supportive and helpful role,” “understanding complexity and multidimensionality,” “social-emotional support role,” and “technology and mechanical power role.” The most frequently used metaphor was “guide,” followed by private tutor, machine, smart friend, helping hand, translator, compass, school, information pool, lantern, and light.

Conclusions

The findings suggest that pediatric nurses generally have positive perceptions of artificial intelligence, though they may underestimate its ethical and safety challenges due to limited training and experience. It is essential to strengthen nurses’ artificial intelligence literacy, ethical awareness and decision-support competencies through education and scenario-based training programs to ensure the safe and effective integration of artificial intelligence technologies into pediatric nursing practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-026-04717-6.

Background

In pediatric nursing, patient education is a fundamental component of care, particularly in the management of chronic diseases, where children and their families require continuous guidance, information, and support. In this context, the use of artificial intelligence (AI) has gained increasing attention as part of the digital transformation of healthcare, contributing to learning, monitoring, and clinical decision-making processes in pediatric settings [1]. AI enables personalized care and targeted treatment plans by swiftly analyzing complex data [1]. AI offers significant advantages, particularly in primary healthcare, such as managing limited resources, educating patients, and monitoring chronic diseases. Furthermore, AI-supported applications enable patients to track their symptoms and take a more active role in their care by offering personalized recommendations [2].

Integrating these systems into pediatric care significantly improves early diagnosis and the development of personalized treatment plans, particularly for chronic diseases requiring continuous monitoring [1, 3, 4]. Analyzing data from electronic health records and wearable technologies enables the early detection of symptom changes in patients, allowing for timely intervention in complications and improving care effectiveness [5]. Additionally, AI can enhance learning experiences and outcomes by generating educational materials tailored to children with chronic diseases [6]. In chronic conditions such as diabetes and respiratory diseases, real-time data tracking and predictive analytics facilitate the monitoring and support of pediatric patients [1].

Since nurses play a critical role in analyzing symptoms and managing chronic diseases in the long term, their active participation is necessary to effectively apply AI to support children’s disease management [5]. Research findings demonstrate that nurses’ perceptions and attitudes toward AI significantly impact the acceptance and effective application of new technologies [7, 8]. Understanding pediatric nurses’ perceptions of AI is crucial for successfully integrating it into pediatric care [9, 10].

However, research examining pediatric nurses’ anxiety, concerns, and perceptions of AI metaphors is limited [11]. Furthermore, no specific data were identified in the literature regarding how these metaphorical perceptions are reflected in highly specialized applications, such as the education of children with chronic diseases. Most studies have examined nurses’ general attitudes toward AI and their acceptance levels [10, 12–15]. Furthermore, these studies have relied on quantitative methods to measure attitudes and intentions. Only recently have researchers begun to explore metaphorical perceptions and anxiety dimensions [11]. The lack of data on how pediatric nurses relate AI to specific professional practices risks the inadequate representation of children in AI application and development studies [16].

While perceptions and attitudes often represent measurable cognitive and affective states, metaphor analysis allows for the exploration of deeper symbolic interpretations [17, 18]. This method is particularly appropriate for exploring healthcare professionals’ perceptions of emerging technologies, as it reveals how nurses map familiar, everyday concepts onto novel, abstract systems [19]. In the context of pediatric chronic diseases, where patient and family education are critical, understanding these symbolic representations helps clarify how AI is conceptually integrated into daily practice [1, 5, 20].

Metaphors are frequently used in scientific studies to make abstract concepts more concrete, revealing individuals’ underlying cognitive schemas, fears, and hopes regarding new technologies [17, 18]. By defining emerging technologies through familiar conceptual frameworks, metaphors offer rich insights into how nurses conceptualize their professional identity, patient relationships, and the mutual interaction between human care and technological evolution [19, 21, 22]. These expressions can also simplify concepts, explain human–technology relationships, highlight social impacts, and inform regulatory frameworks [23]. Nevertheless, research examining nurses’ metaphorical perceptions of AI remains limited [11, 24]. This study focuses on metaphorical expressions to help us better understand the cognitive and emotional barriers to adopting AI. It will determine how pediatric nurses conceptualize AI in the education of children with chronic diseases. Furthermore, focusing on pediatric nurses who care for children with chronic diseases, as well as adopting a qualitative approach, gives this study the potential to contribute something unique to the literature.

The study aims to reveal the metaphors that pediatric nurses use to describe using AI in the education of children with chronic diseases and the reasons behind these metaphors.

The research questions are as follows:

• What metaphors do pediatric nurses use to describe the use of AI in the education of children with chronic diseases?

• What is the reason behind nurses explaining their preferred metaphors?

• How do metaphor patterns vary descriptively according to the selected demographic characteristics of the participants?

Methods

Study design and participants

This study was conducted as a qualitative descriptive study using metaphor-based content analysis to explore pediatric nurses’ metaphorical perceptions of AI in the education of children with chronic diseases. Purposive sampling was used. The inclusion criteria were having previous experience working with children with chronic diseases, knowing Turkish, and volunteering to participate in the study.

Sixty-eight nurses working in the pediatric units of a university hospital initially participated in the study. Following the exclusion of responses that did not meet the criteria for metaphor analysis, data from 60 participants were included in the final analysis. In addition, both nurses with and without prior experience using AI were included.

Data collection

A semi-structured interview form was developed by the researchers specifically for this study and is provided as Supplementary File 1. The form was designed in line with the research aim and administered to the participants. It included demographic questions (presented in Table 1) and a single open-ended metaphor elicitation prompt designed to encourage participants to express their perceptions in a concise and structured manner. Participants were asked to complete the statement: “The use of artificial intelligence in the education of children with chronic diseases is like ……………. because …………….” on a separate sheet of paper. Data collection was conducted between April and June 2025.

Table 1.

Descriptive characteristics of participants (n:60)

Variable		f	%
Age	mean ± SD (min-max)	37.02 ± 8.67 (24–55)
Gender	Female	57	95.0
	Male	3	5.0
Marital status	Single	22	36.7
	Married	38	63.3
Children	Yes	35	58.3
	No	25	41.7
Educational status	Associate degree	1	1.7
	Bachelor’s degree	53	88.3
	Master’s degree	4	6.7
	Doctorate	2	3.3
Years of experience	0–5 years	14	23.3
	6–10 years	9	15
	11–15 years	10	16.7
	16 years and over	27	45.0
Previous training in AI	Yes	3	5
	No	57	95.0
How would you describe your aptitude for using technology?	Very low	2	3.3
	Low	3	5.0
	Medium	40	66.7
	High	11	18.3
	Very high	4	6.7
Previous experience with AI	Yes	12	20
	No	48	80

The forms were administered face-to-face in a separate room within the clinics where the nurses worked. Participation was voluntary, and confidentiality was ensured throughout the process. Participants were given 5–20 min to think about and complete their responses. The researchers explained the purpose of the study and clarified any questions when needed, while avoiding guidance that could influence participants’ responses. All participants received the same instructions and completed the form under similar conditions to ensure consistency in data collection.

Participants completed the forms independently. Two of the three researchers, both female, were actively involved in the data collection process. Both held doctoral-level qualifications in nursing; one is an academic specializing in children with chronic diseases, and the other has approximately 15 years of clinical experience with this population. Although the researchers were familiar with the study context, care was taken to maintain a neutral approach during data collection. The completed forms were collected by the researchers.

Data analysis

Data were analyzed manually using qualitative content analysis with a focus on metaphor interpretation. The analytic process followed a structured, step-by-step procedure including identification, coding, categorization, and theme development. First, all responses were read repeatedly to gain familiarity with the data (familiarization stage).

In the identification and initial coding stage, metaphorical expressions produced by the participants were examined, and their underlying meanings were interpreted. Valid metaphors were defined as expressions that included both a metaphorical comparison and an explanatory statement (i.e., “… is like … because …”). Responses lacking either component or not demonstrating clear metaphorical meaning were excluded. In total, eight responses were excluded, and the metaphors of 60 participants were included in the analysis. These criteria were applied consistently throughout the analysis process.

Conceptual depth was considered sufficient as recurring patterns were observed across the data, and no new themes emerged in the later stages of analysis. During the analysis process, all metaphors were independently reviewed by the researchers, and their meanings were recorded.

In the categorization stage, metaphors were coded based on their underlying meanings, and those with similar functions and conceptual patterns were grouped together. In the theme development stage, these categories were organized into broader themes based on similarities in meaning, function, and role within the context of pediatric care. Each metaphor was evaluated together with its explanatory statement to ensure accurate categorization.

For example, metaphors emphasizing the guidance and information-providing function of AI in the education of children with chronic diseases were included in the “Providing Information and Guidance Role” theme, whereas metaphors highlighting supportive or assistive functions were grouped under the “Supportive and Helpful Role” theme. These categories were then organized into broader themes.

The analysis was conducted independently by multiple researchers, and disagreements were resolved through discussion until consensus was reached. This multi-stage analytic process corresponds conceptually to layered coding procedures in qualitative research.

Trustworthiness

The trustworthiness of this qualitative descriptive study was established using the criteria of credibility, dependability, and transferability. Dependability was ensured by calculating inter-coder agreement using the Miles and Huberman formula [25]. An independent faculty member with expertise in nursing coded the metaphors separately from the research team. Out of 60 metaphor expressions, 12 showed discrepancies, resulting in an inter-coder agreement rate of 80%, which is considered acceptable in qualitative research. These discrepancies mainly arose from differences in the interpretation of thematic placement, particularly for more abstract or multidimensional metaphors such as Personal Scanner, Smart Friend (appearing in three instances, with disagreements in two cases due to different interpretations), Ocean, Sea, Sky, Wizard, Private Tutor, Flower, Garden, Personal Doctor, and Game.

Credibility was enhanced through prolonged engagement with the data and the use of direct participant quotations to ensure that the findings accurately reflect participants’ meanings. Transferability was supported by providing a detailed description of the study context, participant characteristics, and the sampling process, allowing readers to assess the applicability of the findings to other settings. Participant checking was not conducted due to the anonymous nature of the written metaphor forms.

Ethical considerations

Ethical approval was obtained from the Hacettepe University Health Sciences Research Ethics Committee before starting the research. (Approval no: SBA 25/064 Date:28.01.2025) In addition, nurses were informed about the study, and written informed consent was obtained from all participants prior to data collection. This study was conducted in accordance with the principles of the Declaration of Helsinki.

Results

The majority of nurses participating in the study were female (95%). Looking at the nurses’ length of service, the majority (45%) had 16 years or more of professional experience. Only 5% of nurses reported having previously received training related to AI. The nurses participating in the study mostly described their aptitude for using technology as moderate (66.7%). Only 20% of the nurses stated that they had experience using AI. Descriptive statistics for the participants are presented in Table 1.

The metaphors developed by participants regarding the use of AI in the education of children with chronic diseases are presented in Table 2. In this context, a total of 42 different metaphors were identified. As seen in Table 2, participants most frequently described AI using the metaphor of a guide (f: 4, 6.6%). Other frequently used metaphors were defined as private tutor, machine, smart friend, helping hand, translator, compass, school, information pool, lantern, and light.

Table 2.

Metaphors used by participants to describe AI (n:60)

Metaphor	f	%	Metaphor	f	%
Guide	4	6.6	Sun	1	1.6
Machine	3	5.0	Second doctor	1	1.6
Private tutor	3	5.0	Medicine	1	1.6
Smart friend	3	5.0	Encyclopedia	1	1.6
School	2	3.3	Personal assistant	1	1.6
Compass	2	3.3	Personal doctor	1	1.6
Translator	2	3.3	Personal guide	1	1.6
Helping hand	2	3.3	Personal scanner	1	1.6
Lantern	2	3.3	Amusement park	1	1.6
Information pool	2	3.3	Metro	1	1.6
Light	2	3.3	Miracle	1	1.6
Informative	1	1.6	Notebook	1	1.6
Garden	1	1.6	Ocean	1	1.6
Knife	1	1.6	Puzzle	1	1.6
Life saver	1	1.6	Rehabilitation	1	1.6
Flower	1	1.6	Robot	1	1.6
Sea	1	1.6	Wizard	1	1.6
Early warning system	1	1.6	Magic mirror	1	1.6
Key	1	1.6	Water	1	1.6
Sky	1	1.6	Tourist guide	1	1.6
Glasses	1	1.6	Way	1	1.6
Game	1	1.6	Navigation device	1	1.6

f: frequency, % percentage

The metaphors produced by the participants were grouped under five themes based on their perceived functions in the education of children with chronic diseases. Each metaphor was interpreted together with its explanatory statement to ensure accurate and conceptually consistent thematic placement. Table 3 presents the themes to which the metaphors belong, along with their frequency and percentages.

Table 3.

Themes related to metaphors

Theme	Metaphor (f)	f	%
Providing Information and Guidance Role	encyclopedia(1), informative (1), information pool(2), light(2), notebook(1), school(2), lantern(2), personal guide(1), navigation device(1), compass(2), guide(4), translator(2), way(1), tourist guide(1), sun(1), magic mirror(1)	25	41.7
Supportive and Helpful Role	private tutor (3), second doctor (1), personal doctor (1), rehabilitation (1), helping hand (2), life saver (1), early warning system (1), medicine (1), glasses (1), personal assistant (1)	13	21.7
Understanding Complexity and Multidimensionality	key (1), garden (1), flower (1), sea (1), sky (1), miracle (1), ocean (1), puzzle (1)	8	13.3
Technology and Mechanical Power Role	Personal scanner(1), machine(3), metro(1), robot(1), wizard(1), knife(1)	8	13.3
Social Emotional Support Role	Smart friend(3), amusement park(1), game(1), water(1)	6	10

f: frequency, % percentage

Table 3 shows that the majority of participants’ metaphors clustered around the theme of “providing information and guidance role” (41.7%). There were 13 metaphors under the theme of “supportive and helpful role”, 8 under the theme of “understanding complexity and multidimensionality”, 8 under the theme of “technology and mechanical power role”, and 6 under the theme of “social emotional support role.”

The metaphors and reasons related to the theme of providing information and guidance role, which generated the most metaphors, are presented in Table 4. This theme includes metaphors reflecting the information provision and guidance functions of AI in the education of children with chronic diseases. There are a total of 16 different metaphors in this theme.

Table 4.

Metaphors and their reasons within the providing information and guidance role theme

Metaphor code	Gender	Age range	Metaphor	Because;
3	F	20–29	Notebook	I can record everything.
5	F	30–39	Magic mirror	Reflects the child’s strengths and weaknesses, showing teachers and families the most appropriate path.
7	F	20–29	Lantern	It provides guidance in the dark and uncertain areas encountered during the learning process, is adaptable to the child’s needs, and facilitates the learning process.
10	F	30–39	Light	With its ease of use and accessibility, it is like light for those with chronic diseases who are frequently exposed to the hospital environment.
12	F	30–39	Compass	When used correctly, it can serve as a guide.
13	F	30–39	Lantern	It sheds light on their education during long-term hospital stays.
15	F	20–29	Information pool	Research can be conducted easily.
17	F	30–39	Information pool	Its easy accessibility and always up-to-date information make it both reliable and time-saving for people with chronic diseases.
20	F	20–29	Translator	It facilitates learning and application by explaining science and scientific data in a way we can understand.
24	F	20–29	Sun	Supports them in experiencing enlightenment when they feel lost, with the help of AI.
26	M	20–29	Tourist guide	Guides and facilitates education.
32	F	30–39	Way	By understanding children’s language, it can offer them a more enjoyable education.
33	F	30–39	Light	It illuminates all the methods we cannot see and, because it is easy to use and simple to access, it lights the way for every step they take.
35	F	20–29	Compass	Accelerates care and education.
38	F	40–49	Personal guide	Provides specialized education tailored to the child’s interests.
39	F	40–49	Encyclopedia	A place where they can get preliminary information.
42	F	20–29	Guide	Makes understanding easier and serves as a guide.
43	F	40–49	School	It provides easy access to unlimited information.
44	F	30–39	Guide	Makes our work easier.
53	F	40–49	School	Enables them to access information.
54	F	30–39	Navigation device	Makes life easier for children and healthcare personnel and reduces their workload.
56	F	40–49	Guide	It can provide instruction, support, and solutions depending on the child’s condition.
57	F	40–49	Guide	Provides real-time analysis and information on any topic.
58	F	30–39	Informative	Can access answers to patient questions or relate symptoms that the patient is unaware of to their disease.
59	F	40–49	Translator	Helps children understand complex medical terms in a simple and fun way.

Table 5 presents metaphors and reasons within the supportive and helpful role theme. This theme includes metaphors reflecting the functions of AI in supporting, helping, and facilitating the education of children with chronic diseases. There are a total of 10 different metaphors in this category.

Table 5.

Metaphors and their reasons within the supportive and helpful role theme

Metaphor code	Gender	Age	Metaphor	Because;
2	F	50–59	Helping hand	It provides support, foresight, and assistance.
8	F	40–49	Medicine	It can contribute to improving coordination in patient monitoring and care.
23	M	20–29	Second doctor	Can be provided with equipment that can speed up the diagnosis process.
25	F	20–29	Helping hand	Solves problems that the human brain cannot solve
27	F	20–29	Personal assistant	Ensures nothing is overlooked.
34	F	30–39	Life saver	Saves lives.
37	F	40–49	Private tutor	Provides content tailored to the child’s level.
41	F	40–49	Private tutor	It can be designed to personalize education according to the needs of patients and nurses.
45	F	30–39	Rehabilitation	Provides happiness, peace, and confidence.
50	F	40–49	Personal doctor	Researches and gets clear answers to questions.
51	F	50–59	Early warning system	Assists healthcare professionals in diagnosis and treatment.
52	F	40–49	Private tutor	Reduces the impact of disabilities.
60	M	40–49	Glasses	Clarifies blurred information.

Table 6 presents metaphors and their reasons within the theme of understanding complexity and multidimensionality. This theme reflects metaphors that conceptualize the phenomenon as complex, multidimensional, and dynamic. The included metaphors emphasize different aspects of this complexity. For example, “puzzle” and “key” highlight problem-solving, structure, and the ability to unlock solutions. “Ocean,” “sea,” and “sky” reflect vastness, limitless possibilities, and multiple ways of thinking. In contrast, metaphors such as “flower” and “garden” emphasize growth, development, and nurturing processes. The metaphor “miracle” reflects transformative potential and the ability to achieve outcomes that may seem otherwise unattainable. These shared meanings illustrate how participants understand the phenomenon as a multifaceted and evolving structure. There are a total of 8 different metaphors in this category.

Table 6.

Metaphors and their reasons within the theme of understanding complexity and multidimensionality

Metaphor code	Gender	Age	Metaphor	Because;
11	F	40–49	Puzzle	It fosters problem-solving, creative thinking, and mastery of technology skills.
18	F	20–29	Ocean	It is boundless.
19	F	20–29	Sea	Offers many alternative ways of thinking.
21	F	50–59	Sky	It is boundless.
36	F	20–29	Miracle	Makes the impossible possible.
40	F	50–59	Flower	It brings freshness to its surroundings, light, beauty, and understanding.
46	F	30–39	Garden	All children are colorful, just like flowers. It can provide them with the soil and wholeness they need to bloom.
49	F	40–49	Key	It unlocks every door.

Table 7 presents metaphors and their reasons within the theme of technology and mechanical power role. This theme includes metaphors reflecting the function of AI in managing mechanical power and systems technologically in the education of children with chronic diseases. There are a total of 6 different metaphors in this category.

Table 7.

Metaphors and their reasons within the theme of technology and mechanical power role

Metaphor code	Gender	Age	Metaphor	Because;
1	F	40–49	Robot	Provides a solution.
4	F	30–39	Personal scanner	It enables the monitoring and control of patients’ personality and clinical characteristics, their diseases, and chronic disease processes.
6	F	40–49	Machine	Can create medication schedules for chronic diseases, send reminders, and provide alerts for lifestyle changes.
29	F	30–39	Wizard	Adds novelty and interest to the lives of pediatric patients.
30	F	40–49	Machine	It’s very easy.
31	F	40–49	Machine	AI and its subfields, machine learning and deep learning, enable the storage, processing, and analysis of large amounts of data.
47	F	40–49	Metro	Available immediately when needed.
48	F	40–49	Knife	If used consciously, it is effective; if used unconsciously, it can cause harm like a sharp knife.

Table 8 presents metaphors and their reasons within the social emotional support role theme. This theme includes metaphors reflecting the social emotional function of AI in the education of children with chronic diseases, such as providing emotional support, trust, and comfort. There are a total of 4 different metaphors in this category.

Table 8.

Metaphors and reasons within the social emotional support role theme

Metaphor code	Gender	Age	Metaphor	Because;
9	F	30–39	A smart friend	You can talk to it and get advice.
14	F	40–49	A smart friend	Answers every question.
16	F	20–29	A smart friend	Responds according to the child’s mood and needs.
22	F	20–29	Amusement park	It is as captivating and intriguing as it is distracting.
28	F	20–29	Water	Offers opportunities for life and development.
55	F	20–29	Game	It offers more enjoyable and engaging educational experiences using AI for chronically ill children who are bored with traditional storytelling.

The distribution of metaphor themes according to participants’ prior use of AI is presented in Table 9. The table shows how metaphors are distributed across themes among participants with and without prior AI experience. Overall, metaphors were grouped under similar themes in both groups. Among participants with prior AI experience, metaphors were more frequently associated with the “Providing Information and Guidance Role” theme. However, these findings should be interpreted with caution due to the limited number of participants with prior AI experience.

Table 9.

Distribution of metaphors and themes among nurses who had previously used AI

Experience with AI use	Metaphor	Theme(f)
Yes	tourist guide, informative, personal assistant, guide, translator, way, private tutor, sea, sun, miracle, smart friend, personal scanner	Providing Information and Guidance Role (6) Supportive and Helpful Role (2) Understanding Complexity and Multidimensionality(2) Social Emotional Support Role (1) Technology and mechanical Power Role (1)

f: frequency

The results regarding nurses’ metaphors on the use of AI in the education of children with chronic diseases according to their length of service are presented in Table 10. The table shows the distribution of metaphor themes across experience groups. Nurses with 16 years or more of work experience tended to produce metaphors related to the “Supportive and Helpful Role” theme more frequently than those with less experience.

Table 10.

Distribution of metaphors and themes according to length of service

Work experience	Metaphor	Theme(f)
0–5 years	sea, personal assistant, miracle, lighthouse, amusement park, game, tourist guide, translator, information pool, second doctor, ocean, water, compass, guide	Providing Information and Guidance Role (6) Understanding Complexity and Multidimensionality (3) Supportive and Helpful Role (2) Social Emotional Support role(3)
6–10 years	notebook, magic mirror, light, smart friend, sun, helping hand, wizard, way, light	Providing Information and Guidance Role (6) Social Emotional Support Role (1) Supportive and Helpful Role (1) Technology and Mechanical Power Role (1)
11–15 years	compass, smart friend, life saver, garden, informative, information pool, navigation device, guide, personal scanner, robot	Providing Information and Guidance Role (5) Technology and Mechanical Power Role (2) Supportive and Helpful Role (1) Understanding Complexity and Multidimensionality (1) Social Emotional Support Role(1)
16 years and more	lantern, rehabilitation, key, machine, glasses, school, guide, smart friend, guide, personal guide, personal doctor, machine, private tutor, school, puzzle, translator, metro, encyclopedia, medicine, knife, machine, private tutor, private tutor, helping hand, sky, flower, early warning system	Providing Information and Guidance Role (8) Supportive and Helpful Role (9) Understanding Complexity and Multidimensionality (4) Social Emotional Support Role(1) Technology and Mechanical Power Role (5)

f: frequency

Discussion

This qualitative study employed a metaphor analysis to reveal the views of pediatric nurses on the use of AI in the education of children with chronic diseases. Content analysis categorized the metaphors used by pediatric nurses into themes such as “providing information and guidance role,” “supportive and helpful role,” “understanding complexity and multidimensionality,” “social emotional support role” and “technology and mechanical power role.”

The majority of the metaphors developed in this study fall under the “providing information and guidance role” and “supportive and helpful role” categories, consistent with previous research. While this may suggest that nurses currently conceptualize AI as a collaborative tool rather than a substitute [11, 26], this finding should be interpreted cautiously. The data consist of brief metaphorical expressions that do not directly explore perceptions of professional replacement, role displacement or technological threat. Furthermore, the predominance of favorable metaphors may reflect an “innovation bias” or “social desirability bias”, where participants reproduce dominant institutional discourses that portray AI as inherently progressive and beneficial rather than expressing critical or ambivalent views. A study examining nurses’ and patients’ willingness to accept and use service robots in healthcare settings found moderate willingness among nurses, with concerns about robotic nurses replacing human nurses standing out [12]. However, similar to Karaarslan et al. (2024), most nurses in this context seem to view AI as a tool that enhances and facilitates their workload without replacing them [9, 27].

Descriptive variations based on experience revealed that nurses with over 16 years of experience frequently produced metaphors related to the “supportive and helpful role”. This trend may indicate that as clinical work becomes more complex with seniority, the cognitive and organizational demands of late-career nursing create a natural alignment with metaphors of coordination and decision assistance. Conversely, “social emotional support role” metaphors exhibited a consistently low frequency across all experience strata. This flat distribution likely reflects professional socialization processes within nursing, where technology is traditionally framed as functional and instrumental, while emotional labor is viewed as a distinctly human attribute.

Providing information and guidance role

In our study, most nurses developed metaphors emphasizing the “providing information and guidance role” of AI. In this category, nurses most often described AI’s role using metaphors such as “guide, information pool, light, school, lantern, compass, and translator’’. For example, a pediatric nurse stated: “Using AI in the education of children with chronic diseases is like having a guide. It can provide instruction, support, and solutions depending on the child’s condition” (Participant 56, female, 40–49 years). This illustrates that nurses perceive AI as a tool that provides access to information, guidance, and support. The literature also emphasizes the potential of AI applications for data collection, symptom monitoring, and providing individualized recommendations in caring for children with chronic diseases [1]. AI-supported decision support systems can facilitate treatment and recovery by responding to symptom inquiries and directing individuals to appropriate care [3].

Supportive and helpful role

Metaphors in this category, such as “private tutor, helping hand, second doctor, personal doctor, rehabilitation, life saver, early warning system, medicine, glasses, and personal assistant,” reflect AI’s supportive role. Nurses emphasized that AI is a supportive, individualized, and barrier-reducing tool. Similarly, one nurse described the situation as follows: “Using AI in the education of children with chronic diseases is like having an extra helping hand. It provides support, foresight and assistance” (Participant 2, female, 50–59). Another nurse stated: “Using AI in the education of children with chronic diseases is like having a private tutor. It can be designed to personalize education according to the needs of patients and nurses.” (Participant 41, female, 40–49 years). While some literature evaluating the acceptability of a humanoid robot for the self-management of children with type 1 diabetes found that children embraced the robot as a helper [28], it should be noted that physical service robots and AI systems represent distinct technological categories, and the conceptual transfer between these contexts requires careful justification. According to the literature, AI technologies can continuously monitor patients’ vital signs and detect early warning signs [29, 30]. This aligns perfectly with the metaphors of “second doctor, personal doctor, life saver, early warning system, and personal assistant.”

Understanding complexity and multidimensionality

The metaphors generated under the theme of understanding complexity and multidimensionality (key, puzzle, garden, flower, sea, sky, ocean, and miracle) show that nurses perceive AI as something that regulates, integrates, and transforms the chronic disease education process. One nurse expressed this idea as follows: “Using AI in the education of children with chronic diseases is like a garden. All children are colorful, just like flowers. It can provide them with the soil and wholeness they need to bloom” (Participant 46, female, 30–39 years). The literature emphasizes that integrating AI with nursing theories can lay the groundwork for a holistic approach to chronic disease care [31].

Social emotional support role

Metaphors related to social emotional support role (e.g., smart friend, amusement park, water, game) demonstrate that nurses view AI as a means of supporting children’s emotional needs. Nurses often compare AI to a smart friend who responds according to the child’s mood and needs. A similar situation was observed in Rony et al.‘s (2024) study, in which nurses viewed AI as a virtual health companion that provides information and supports patient education [14].

Technology and mechanical power role

The Artificial Intelligence Chronic Management System (AICMS) model improves communication among patients, families, and healthcare professionals through AI-based decision support and automated monitoring [3]. Within the theme of technology and mechanical power role, pediatric nurses’ descriptions of AI using metaphors such as machine, robot, personal scanner, wizard, metro, and knife correspond to the features offered by the AICMS system. In this category, nurses view the efficiency and speed of AI positively, while also emphasizing its potential risks when uncontrolled [3].

Our findings show that the vast majority of nurses view AI positively, which aligns with previous studies [13, 15, 32, 33]. A recent mixed-methods study also determined that the metaphors produced by pediatric nurses were largely positive [11]. However, our findings differ slightly from studies heavily emphasizing nurses’ ethical concerns [7, 14, 27]. In our study, the “knife” metaphor suggests that AI can be beneficial when used consciously but harmful when used unconsciously. Nevertheless, the predominance of positive metaphors may suggest that potential risks and ethical dimensions of using AI in chronic disease education are not immediately salient in the participants’ conceptual frameworks. This may stem from nurses having limited training and experience with AI, or because the heavy workload causes them to prioritize practical conveniences. Therefore, while nurses maintain generally positive perceptions, strengthening their awareness of AI ethics, privacy and security issues remains an important consideration.

Limitations

This study has several methodological limitations that should be acknowledged. First, data were collected face-to-face in a professional clinical setting, which may have introduced social desirability bias; participants might have felt inclined to provide socially acceptable or positive responses regarding a technology often associated with innovation and progress. Second, relying on a single written prompt and short, elicited metaphorical responses inherently carries an interpretative bias and limits the depth of exploration into complex issues, such as technological threat or ethical concerns, particularly in the absence of follow-up interviews. In addition, the use of a structured metaphor elicitation prompt (“… is like … because …”) may have influenced participants’ responses by guiding them toward comparative or more instrumental framings. Third, regarding transferability, the sample was drawn from voluntary participants at a single hospital and was characterized by a predominance of female nurses with limited prior AI training or experience. Cultural context may also have influenced metaphor selection and interpretation. While sufficient conceptual variation was achieved for this qualitative exploratory study, these contextual factors mean that the transferability of the findings to broader or more technologically experienced pediatric nursing populations should be approached with caution.

Conclusions

This study explores how pediatric nurses conceptually frame the integration of AI in the education of children with chronic diseases. The main findings reveal that nurses predominantly conceptualize AI through supportive and informative metaphors (e.g., “guide,” “private tutor,” “helping hand”), offering valuable insights into their cognitive representations of this technology1. Rather than directly confirming behavioral acceptance, these metaphors suggest a readiness to view AI as a collaborative and assistive clinical tool rather than a professional threat. While previous studies have addressed nurses’ perceptions of AI in general, this research provides context-specific insights into the patient education process in chronic diseases. Furthermore, the predominantly positive nature of the metaphors may suggest that the potential ethical and safety challenges of AI are not yet fully integrated into nurses’ conceptual frameworks, likely due to limited prior AI training. Recognizing these metaphorical representations is a fundamental step toward understanding nurses’ conceptual readiness. Consequently, these insights can cautiously inform the development of targeted AI education and scenario-based training programs that align with nurses’ practical needs, ultimately supporting the safe, conscious, and effective integration of AI technologies into pediatric nursing practice. Although generational differences were not the primary focus of this study, future research with larger and more diverse samples may explore variations in metaphorical perceptions of AI across generational groups.

Electronic Supplementary Material

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Abbreviation

AI

Artificial Intelligence

Author contributions

E.B. and T.T. were responsible for the conceptualization and design of the study. Data collection was carried out by T.T. and H.Ö. Formal analysis and methodology were conducted by E.B., T.T., and H.Ö. Supervision and validation of the study were performed by E.B., T.T., and H.Ö. E.B. and T.T. drafted the original version of the manuscript. All authors read and approved the final manuscript.

Funding

The authors received no specific funding for this study.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to ethical and confidentiality considerations related to qualitative interview data, but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Hacettepe University Health Sciences Research Ethics Committee before starting the research (Approval No: SBA 25/064, Date: 28.01.2025). In addition, the nurses were informed about the study and their informed consent was obtained. This study was conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.