Content area
Aim
The aim of this study is to examine nursing students' attitudes toward AI use, their patterns of use and levels of AI addiction, as well as to evaluate the impact of emerging fears such as AIlessphobia.
Background
Generative artificial intelligence (AI) is rapidly evolving and is increasingly being used among university students. Concepts such as “AIlessphobia” -the fear of being without AI- highlight the emergence of these issues.
Design
This study used a mixed-methods design.
Methods
The study was carried out during the 2024–2025 Spring Semester with nursing students. Data were collected using the Scale for Dependence on Artificial Intelligence (DAI) and semi-structured interview questions. Qualitative interviews were conducted with students who scored 15 or higher on DAI. The quantitative sample consists of 200 students and the qualitative sample was determined based on quantitative and qualitative data.
Results
Most participants (81.5 %) reported using AI tools, with ChatGPT being the most preferred among them. The interview included 13 questions and the themes emerged during the individual interviews. Thematic analysis highlighted five themes: educational benefits, negative effects, AI addiction, future expectations and AIlessphobia. Students noted that AI eased learning but reduced critical thinking and reported emotional distress when deprived of AI access.
Conclusions
The widespread use of AI tools in education produces both positive and negative effects. It is essential for educators to support students in integrating these tools with critical thinking skills and digital awareness. Such support is crucial for preventing AI addiction and AIlessphobia.
1 Introduction
Generative artificial intelligence (AI) is a rapidly growing field with a wide range of applications ( Gupta et al., 2024). AI refers to the process of developing machines, software and applications that can perform tasks typically requiring human intelligence, such as understanding natural language, analysing patterns and making decisions based on available data ( Suva and Bhatia, 2024).
Thanks to its features like rich interaction possibilities and the capacity to provide accurate responses, this technology has attracted the attention of a wide range of users ( Zhou and Zhang, 2024). Productive AI has the potential to increase user productivity by supporting business processes. For instance, software experts can use it to optimise coding processes. Teachers can use this technology to create effective presentations from course content. Students can interact with it to learn in detail about a particular topic. Designers can enhance their creative process by producing images and videos from given inputs. Similarly, healthcare professionals can use it as an effective tool in areas such as preparing medical documents and analysing images ( Zhou and Zhang, 2024).
However, along with the benefits offered by productive AI, the overuse of these technologies also carries significant risks ( Ünal and Kılınç, 2024). One such risks is AI addiction, a problem arising from the overuse of AI technologies that can lead to negative consequences with addictive tendencies ( Savaş, 2024; Hu et al., 2023; Wiederhold, 2018). AI addiction is defined as over-dependence on AI technologies and applications in various aspects of life, including academic studies, daily routines and social interactions ( Zhang et al., 2024). Users exhibiting AI addiction often show addictive tendencies in their interactions with AI technologies. For example, tendencies such as emotional addiction to chatbots, addiction to social chatbots and addiction to conversational AIs are prominent examples of this situation ( Huang et al., 2024).
Excessive dependence on AI technologies may weaken individuals' own cognitive skills such as problem solving, creative thinking and critical analysis ( Morales-García, Sairitupa-Sanchez, Morales-García and Morales-García 2024; Zhai et al., 2024; Bozkurt, 2023). Moreover, users' overconfidence in these systems may lead to negative consequences like not questioning the accuracy of the answers provided or ignoring ethical issues ( Zhai et al., 2024; Grassini, 2023; Dempere et al., 2023; Gao et al., 2023; Xie et al., 2021). Furthermore, it is predicted that emotional attachment to chatbots may have negative effects on close relationships in real life and distract individuals from their social lives ( Xie et al., 2023; Xie and Pentina, 2022). Although the relationship between AI addiction and mental health problems has not been fully determined, it is known that AI addiction may threaten people's interpersonal connections and negatively affect their mental health ( Huang et al., 2024).
AI is an important innovation that continues to be effective in the field of education ( Gezgin and Kurtça, 2024). When AI dependency on students is examined, it is suggested that this situation may lead to weakening of cognitive skills, decreasing motivation levels ( Ahmad et al., 2023) and loss of independent thinking ability ( Zhang et al., 2024) ( Ahmad et al., 2023). However, students express concerns about the potential effects on creativity, critical thinking and ethical writing practices ( Malik et al., 2023).
The integration of AI into nursing education can enrich the learning experiences of nursing students, enabling them to be better equipped for their professional roles in the health field ( Liu et al., 2023). Especially in recent years, studies conducted on nursing students have shown that the inclusion of AI-based tools in educational processes increases learning motivation, critical thinking and problem solving skills and improves clinical decision-making skills ( Labrague et al., 2025; Ma et al., 2025; Lifshits and Rosenberg, 2024). However, fast and easy access to information may lead nursing students to become more dependent on AI-based tools. This may negatively affect students' intrinsic motivation towards problem solving and their tendency to work independently ( Liu et al., 2023).
In this context, over-dependence on AI may trigger anxiety and feelings of inadequacy among students when AI tools are inaccessible. This situation increases the potential for the phenomenon defined in the literature as ‘AIlessphobia - the fear of being left without AI’ to become widespread among students (
Gezgin and Kurtça, 2024). This phenomenon differs from digital addiction or technology-related anxiety, referring specifically to psychological distress caused by AI tool unavailability in academic contexts (
Gezgin and Kurtça, 2024). While digital addiction is generally associated with excessive and impulsive technology use and technology anxiety refers to the tension experienced during technology use (
Kim et al., 2023; Dresp-Langley and Hutt, 2022) AIlessphobia describes
This study examined the attitudes of university students studying nursing towards AI technologies, their usage patterns, their addiction levels towards AI and their thoughts about the fear of being left without AI, which is defined as ‘AIlessphobia’ and evaluated the effects of their interaction with these technologies on educational processes and clinical practices. Nursing students are a group that closely follow technological developments as an important component of health services and actively use these technologies in clinical decision-making, patient care and education processes ( Wei et al., 2025; Glauberman et al., 2023). However, despite their active engagement, there is currently no nursing-specific legal regulation that governs the use of AI in clinical decision-making, which raises ethical and professional concerns ( Mohammed et al., 2025). Therefore, nursing students were selected as the target group in the study; by determining their perceptions of AI, addiction levels and new fears developed in this context, important results were reached for both individual learning processes and professional competences.
2 Method
2.1 Study design
In this study, a mixed method design was used. In the study, a descriptive qualitative approach (semi-structured interviews) and a cross-sectional quantitative method were applied simultaneously to prevent potential bias based on a single method, to provide a more holistic analysis by bringing together complementary data sources and to balance the limitations specific to certain methods ( Denscombe, 2008). The integration of qualitative and quantitative methods provides researchers with the opportunity to test or validate hypotheses, while also providing the advantage of increasing the reliability and validity of the findings through the triangulation mechanism ( Flick, 2014).
2.2 Settings and participants
This cross-sectional study was conducted at the Faculty of Health Sciences, Department of Nursing, at a university located in Türkiye. The data collection process was carried out between February and March 2025 with undergraduate nursing students from all academic years (1st, 2nd, 3rd and 4th year).
In this study, G*Power 3.1.9.4 programme was used to determine the sample size of quantitative data. In the power analysis for the independent sample t test, the effect size d = 0.5, the significance level α = 0.05 and the statistical power was determined as 80 %. In addition, for the one-way analysis of variance (ANOVA) applied to evaluate the difference between the classes, the effect size f = 0.25, α = 0.05, power = 80 % and the number of groups was entered as 5. According to the results of both analyses, the sample size of 200 people reached in the study provides sufficient power level in terms of statistical power (actual power = 0.80 for t-test; actual power = 0.81 for ANOVA).
For qualitative data, semi-structured interviews were conducted with 12 students who scored 15 and above on the Scale for Dependence on Artificial Intelligence. Purposive sampling method was used in participant selection and it was observed that data saturation was reached because of the interviews. The data obtained in the interviews were enriched to reflect the experiences of the participants in depth and the data collection process was terminated when no new information was added. This approach was chosen to focus specifically on individuals with higher AI dependence levels to gain deeper insights into AI addiction and AIlessphobia.
2.3 Data collection
The study data were collected through voluntary individual interviews between February and March 2025. The data collection process was carried out by the first researcher, who is a doctoral student in the field of public health nursing, a research assistant at a foundation university and experienced in qualitative research and data collection. The second researcher is an academic who has a doctorate degree in public health nursing, works as an associate professor at a state university and specialises in qualitative research.
Interviews were conducted after written informed consent was obtained from the participants and the data collection process continued until data saturation was reached. That is, interviews were terminated when no new information emerged ( Saunders et al., 2018). The interviews were conducted in a classroom setting, lasted an average of 40 min (ranging from 30 to 50 min) and were recorded on a voice recorder. No interview was repeated and none of the students left the study.
The collected data were uploaded to Google Drive™, accessible to both researchers and planned to be stored securely on a password-protected laptop for 2 years.
3 Questionnaires
3.1 Socio-demographic form
A questionnaire form developed by the researcher based on the existing literature was used to collect the data ( Reyhan and Dağlı, 2023). In the questionnaire form, there are questions about the participants' age, gender, grade level, their knowledge and use of AI tools and the AI tool they use most frequently.
Scale for Dependence on Artificial Intelligence (DAI)
Dependence on Artificial Intelligence (DAI) is a scale developed by Morales-García et al. (2024) and adapted to Turkish by Savaş (2024) to measure the participants' level of dependency on AI technologies. DAI scale consists of 5 items, each rated on a 5-point Likert scale (1 = Strongly Disagree, 5 = Strongly Agree), yielding a total score range between 5 and 25. Higher scores indicate a greater interest in or reliance on AI technologies. However, there is no validated cut-off score to determine whether a participant is “addicted” or not. Cronbach’s alpha coefficient reported in the original study was 0.87 ( Savaş, 2024), while in the current study it was calculated as 0.750.
3.2 Semi-structured interview form
Within the scope of the qualitative study, the participants were informed about the purpose of the research and their written and verbal consent was obtained before the interviews. The interviews were conducted in a quiet classroom environment, lasted an average of 40 min and were recorded with a voice recorder. In order to ensure the reliability of the qualitative interview questions, the researchers created the interview guide by conducting a comprehensive literature review and evaluations based on expert opinions. To test the clarity and applicability of the interview questions, a pre-assessment was applied to three students and it was determined that the questions were clear and understandable. Accordingly, no changes were made to the questions (
3.3 Data analysis
3.3.1 Quantitative data analysis
Quantitative data were analysed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Numerical and percentage distributions were used to analyse demographic data. Minimum, maximum, mean and standard deviation values were calculated to evaluate the dimensions of the DAI scale. Independent sample t-test and ANOVA test were applied to examine the relationship between the sub-dimensions of the DAI scale and the sociodemographic variables of the students.
3.3.2 Qualitative data analysis
In this study, qualitative data were analysed using thematic analysis method. The analysis process started from the first interview and ended when data saturation was reached. The interviews were transcribed in detail by the first researcher who did not participate in the interview process. The transcripts were read several times by both researchers and went through manual verification and comparison processes. The researchers first analysed independently and then conducted mutual evaluations on coding strategies and theme definitions at each stage. Conducting the analysis process in successive stages enabled the researchers to examine and discuss the conceptual framework in depth, thus increasing the internal consistency and validity of the coding framework. The qualitative data analysis was conducted manually without the use of any software.
4 Results
It was determined that 88 % of the students participating in the study were female, 50 % of them were between the ages of 17–20 and the mean age of the participants was 20.64 ± 1.93 years. It was determined that 29.5 % of the students were first year students. While 75 % of the students stated that they had knowledge about AI tools, 81.5 % stated that they actively used these tools. It was determined that the most common AI tools used was ChatGPT with a rate of 79.1 % (
4.1 Quantitative findings
The mean and standard deviation of DAI scale scores of the students participating in the study were 13.02 (SD 3.70) (min 5, max 21). When
According to the independent sample t-test results, there was no statistically significant difference between the scores of the DAI scale according to gender variable (p = 0.057). Scale scores of female and male participants were similar.
When evaluated in terms of age groups, the mean score of the DAI scale of the 17–20 age group was found to be 12.55 (SD 4.01) and the mean score of the 21–30 age group was found to be 13.48 ± 3.33. There was no statistically significant difference between these groups on the basis of an independent sample t-test (p = 0076).
In the evaluation made according to the knowledge of AI tools of the students participating in the study, the mean score of the students who knew the tools was 13.20 (SD 3,67), while the mean score of the students who did not know the tools was 12.46 (SD 3.80). This difference was not statistically significant (p = 0.222).
However, a significant difference was observed in the analysis according to the use of AI tools. The mean scale score of the students who used AI tools was 13.36 (SD 3.52), while the mean score of the students who did not use AI tools was 11.49 (SD 4.14). According to the independent sample t-test results, this difference is statistically significant (p = 0.005). This finding shows that students who use AI tools have higher levels of dependence on AI compared with those who do not use AI tools.
When the difference of the DAI scale scores according to grades was analysed, it was found that the mean and standard deviation of the DAI scale scores of the preparatory/1st grades w (SD 3.92), the 3rd grades was 13.65 (SD 3.41) and the 4th grades was 13.35. (SD 3.21).
As a result of the One-Way Analysis of Variance (ANOVA), there was no statistically significant difference between the classes in terms of the DAI scale scores (p = 0.232).
4.2 Qualitative findings
The qualitative part of the study was carried out with 12 nursing students. Of these, 11 were female and one was male. Most participants were 1st year students. Experiences related to the use of AI, addiction to AI and fear of AI were analysed within the framework of five main themes and sub-themes (
4.3 Use and benefits of AI in education
In this section, nursing students' views on the use of AI in education and its benefits were analysed. Students stated that AI tools offer many advantages such as quick access to information, time saving and support in patient care, as well as their use in homework and lessons:
"I think AI is very useful in our profession, especially in issues such as care plans, the meaning of medicines and diseases. It really provides me a great convenience when researching these topics. Apart from that, I think it is very useful professionally in general." (P8)
‘I use AI to get detailed information during exam periods or when I am trying to understand a topic better. I think it is especially useful for learning complex topics and doing in-depth research.’(P3)
‘…. I can use my time more efficiently and continue my studies more effectively.’(P5)
" AI makes a great contribution to me in the assignments given by my professors. For example, while it takes quite a while to read a book from beginning to end, thanks to AI, I can access the information I need faster. It also provides me great convenience in the process of finding articles. In internships, it helps me to reinforce my theoretical knowledge and increase my knowledge." (P4)
4.4 Negative effects of AI
In this section, the negativities perceived by nursing students towards the use of AI were examined. The insecurity felt by the students in case of restricting their access to AI, the fear of losing their professional competences and their concerns about the effect of AI on decision-making processes were evaluated. In addition, the possible negative effects of AI such as weakening critical thinking, negatively affecting the decision-making mechanism, over-dependence on ready-made information and reducing the ability to think were also discussed:
"I think AI reduces our creativity level. Because being able to access everything so easily reduces our need to think and produce. Instead of developing an idea or coming up with something original, we tend to take what is ready and use it. This dulls our creativity over time." (P4)
‘In fact, there is a primary concern for the nursing profession; with the development of AI and robot technologies, there is a concern about whether these systems will replace nurses.’(P5)
‘…. may be weakening our thinking skills, imagination and original thoughts.’(P5)
4.5 AI addiction
In this section, nursing students' thoughts on AI addiction are included. Students state that easy access to AI applications can develop addiction over time. It is also stated that these technologies, which are used intensively especially during exam periods, negatively affect time management and cause unnecessary time loss. One student remarked:
“……. The phones in our hands are actually AI products. For example, even if I only take a one-hour exam, when I leave the exam, I feel as if I haven't spent time on the phone for days. This has become a kind of internal drive.” (P4)
‘Although I do not use them frequently in my daily life, I tend to use AI applications more, especially during exam periods. This situation can create a feeling of addiction over time.’(P3)
‘I often turn directly to AI because it provides fast and easy access to information. This situation has created a habit of applying without thinking over time and created a feeling of addiction in me.’(P1)
‘………. this becomes a habit over time and feels like phone addiction.’(P4)
“I am fascinated by AI, but I am not only dependent on it because I can also do research on the internet. Still, I don't think “never without AI”. But I cannot deny that it becomes addictive over time.”(P2)
4.6 Future perspective on AI
This section includes nursing students' thoughts on the future of AI and their perspectives on the possible effects of this technology on professional practices. The students generally expressed cautious optimism, recognizing both the potential benefits of AI to support nursing tasks and concerns about its limitations, particularly regarding the irreplaceable human aspects of care:
"Although there is a concern about whether these systems will replace the profession with the development of AI and robot technologies in terms of the nursing profession, I do not have this concern. Because robots do not have emotional and spiritual aspects, I do not think they can fully replace a nurse. In addition, these systems can only function to the extent that we program them; they do not have the ability to think analytically on their own. Therefore, I believe that AI can be a tool to support our profession when used in the right areas and consciously."(P4)
"I think AI can have both positive and negative effects, especially on thinking processes. However, in some cases, it should not be ignored that it may negatively affect the nursing profession."(P5)
"From the perspective of the nursing profession, I think that the inclusion of AI in education will have certain contributions; however, these contributions may not be valid in all areas. Under my own control and when I need it, I prefer to use AI positively in my profession. As a result, I realise that I will reflect both positive and negative aspects of this technology in my professional life."(P7)
4.7 Fear of being without AI
In this section, students' thoughts on the fear of being left without AI and the habit-forming potential of this technology are given:
"Yes, there is definitely a fear of being left without AI. Because as I said before, thanks to AI, we can access information much faster and easier. This makes our work practical. So I feel like I won't be able to do some things well enough without AI."(P10)
‘Actually, I have never directly defined this as a fear, but maybe I may have experienced such a situation without realising it, especially during the exam preparation process.’ (P9)
"I think AI has become a part of everyone's life. Therefore, the fear of not having access to this technology is present in me, as it is in many people."(P7)
‘Without AI tools, I would probably have fewer resources to turn to, which probably wouldn't satisfy me as much.’(P8)
’……. A friend of mine tried to upload all the files to the AI system. The system didn't accept the files, so no content could be created. He panicked and got stressed; he reacted like ‘I can't do it, it doesn't work, what should I do? Then he tried to try again on another platform. This was both a serious waste of time for him and caused him to experience high levels of stress. This whole process is an example of the negative effects of over-dependence on AI."(P1)
5 Discussion
In this study, nursing students' knowledge, usage and dependency levels of AI tools were evaluated with both quantitative and qualitative data. The findings show that most students are quite familiar with AI tools, both in terms of knowledge and frequency of use, with tools like ChatGPT being particularly preferred. This finding is similar to the findings in recent studies that reveal the effect of the widespread use of artificial intelligence technologies in education on students ( Moskovich and Rozani, 2025; Nashwan and Abujaber, 2025; Vieriu and Petrea, 2025; Akutay et al., 2024; Taskiran, 2023; Labrague et al., 2023). Studies in the literature emphasise that AI tools such as ChatGPT are important tools that support the learning process of nursing students and increase their satisfaction ( Gonzalez-Garcia et al., 2024; Syafriati, 2024). However, the rapid, accurate and persuasive answers offered by these technologies may lead students to develop overconfidence and dependency on them over time ( Abujaber et al., 2023).
In the study, the mean score of the DAI scale was determined as 13.02 ± 3.70, this suggests that the students' dependency levels towards AI are at a moderate level. The qualitative findings also support these quantitative data. In qualitative findings, students stated they use AI tools more intensively, especially during exam periods and that this use sometimes leads to a feeling of addiction. Interestingly, as seen in studies on internet addiction, some students who reported not using AI tools still scored moderately on the dependency scale, which may be explained by factors such as curiosity, perceived benefits, or social influence, aligning with previously identified motivation types such as escape, social, entertainment and instrumental ( Mahama et al., 2024; Ng and Lin, 2022; Choi and Drumwright, 2021; Cho.,2019). Zhang et al. (2024), determined that students with low academic self-confidence or experiencing high levels of academic stress had a stronger belief in AI tools would increase their academic achievement. Driven by this belief, these students tended to use AI more frequently and intensively ( Zhang et al., 2024). Similarly, Hong and Chen (2024) found that achievement motivation and self-efficacy level had indirect effects on students' tendency to use AI tools ( Hong and Chen, 2024). In addition, individual factors such as time pressure and reward sensitivity directly effect on students' tendency to use AI tools ( Abbas et al., 2024). Studies conducted in this context highlight that overconfidence in AI by students may bring a series of negative effects. Accordingly, it is stated that the risk of addiction may increase, creativity and innovation skills may weaken and the development of critical thinking and problem solving skills may be interrupted. It is also emphasised that students' independent analysis and interpretation skills may regress and their research and academic writing skills may not develop sufficiently ( Abd-Alrazaq et al., 2023; Duhaylungsod and Chavez, 2023; Koos and Wachsmann, 2023; Santiago et al., 2023). Qualitative analyses support this, revealing that AI is perceived as both a supportive and threatening tool by the students. While some students stated that AI facilitates access to information, saves time and makes academic processes more efficient, others pointed out that these tools weaken critical thinking skills, increase the tendency to turn to ready-made information, may create dependency over time and this may lead to a decline in creativity and innovation skills, weaken problem solving and independent thinking skills and prevent the development of research and academic writing skills.
These findings highlight the need to integrate AI literacy and ethical guidelines into nursing education curricula to balance the benefits and risks of AI use. Educators should emphasize critical thinking and independent problem-solving skills alongside AI tool usage to prevent over-dependence and promote professional competence ( Shin et al., 2024; Glauberman et al., 2023). In this context, nurse educators can integrate case-based discussions, simulations and reflective practices to help students critically evaluate when and how to use AI in clinical settings. Recent studies support these educational strategies ( Liaw et al., 2025; Shin et al., 2024; Saatçi et al., 2024; Akutay et al., 2024).
In this study, although the most frequently used AI tool by the students was ChatGPT, it was found that the addiction scores of the students who used AI tools were significantly higher than those who did not use them (p = 0.005). Additionally, in the qualitative interviews, students stated that easy access to tools such as ChatGPT creates a kind of ‘getting used to the ready’ situation over time and this can turn into a psychological dependence. Although it is seen that variables such as perceived usefulness and ease of use of technology affect users' intention to use ChatGPT continuously ( Solomovich and Abraham, 2024; Shaengchart, 2023), concerns about problematic use, especially the risk of developing addiction, are also increasing ( Hong and Chen, 2024). In parallel with the literature, the findings of the study show that easy access to AI tools increases the frequency of use in students and strengthens the risk of developing addiction over time.
Although no significant difference was found between AI addiction scores according to gender (p = 0.057), female students' mean score was notably higher than male students'. In addition, no significant difference was found between age groups and grades in terms of AI addiction. However, the slight increase in dependency scores as the grade level increases suggests that students apply more to AI tools as their academic load increases. Qualitative data also support this. Especially during the internship periods, students stated that they used AI to reinforce theoretical knowledge.
Finally, some students reported experiencing a ‘fear of being left without AI'. In the study, the fact that students stated that they experienced stress and panic when they could not access AI, especially during exam periods, is one of the symptoms of this. Some students may use chatbots as a coping tool to cope with academic anxiety ( Gezgin and Kurtça, 2024). AI tools can reduce the academic anxiety experienced by students ( Han, 2020). Therefore, students with academic anxiety using AI tools to increase their academic competence is related to this situation. Also, as in Netlessphobia and Nomophobia, the absence of AI in educational environments can also lead to anxiety and fear. Because staying away from AI for tasks like doing homework easier, helping in online exams, overcoming academic performance anxiety and protecting the status of academic success can cause fear and panic in students ( Gezgin and Kurtça, 2024).
This study contributes to literature by investigating nursing students' attitudes, usage patterns and addiction levels related to AI, focusing particularly on emerging AIlessphobia. Utilizing a mixed-methods approach, it provides a comprehensive understanding of students’ experiences and AI's psychosocial impacts in nursing education. Given AI's increasing integration in clinical training and decision-making, addressing AIlessphobia in health education is essential to prevent the development of maladaptive psychological responses to AI unavailability in future healthcare professionals.
5.1 Strengths and limitations
Thanks to the mixed-method design combining quantitative and qualitative data, this study examined nursing students’ attitudes, behaviours and addiction levels towards AI in depth. Including the concept of ‘AIlessphobia’ contributes significantly to the literature. The sufficient sample size and data saturation also support validity. However, the study has limitations. Data from a single university and the exclusion of non-users from interviews limit generalisability. Some non-users scoring moderately on the dependency scale suggest influences beyond behaviour, such as curiosity or social factors. Limited interviews may have overlooked hesitation or unfamiliarity with AI. Future studies should incorporate these groups for a more comprehensive understanding. Also, since AI addiction and AIlessphobia are new concepts, the theoretical framework remains limited.
6 Conclusion
The widespread use of AI tools in education may have both positive and negative consequences. It is recommended that educators support students in using these tools by integrating them with critical thinking skills and digital awareness to reduce the risk of AI addiction and fear of being left without AI.
CRediT authorship contribution statement
Saglam Rukiye Kevser: Writing – original draft, Visualization, Formal analysis, Data curation, Conceptualization. Bilge Kalanlar: Writing – review & editing, Validation, Supervision, Methodology.
Ethical approval
Ethical approval was obtained from Başkent University Ethics Committee (No: 416566).
Funding
This research did not receive any specific grant from any kind of funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Competing Interest
The authors declare no conflicts of interest.
Acknowledgements
The authors would like to thank the nursing students for their valuable support.
Table 1
| How do you use AI technologies in your education processes?
How do you evaluate the benefits of these technologies for you? What do you think about the role of AI tools in nursing education? In which areas do you think it is more useful? When using AI tools, do you think you experience an addictive level of interaction? Why? How does the overuse of AI tools affect your learning and problem-solving skills? How do you feel when you are away from AI technologies? How does this situation affect you? When you use AI tools, what impact do you observe on your training or clinical practice? What are the effects of working with AI on your professional skills (e.g. critical thinking, decision-making)? Do you have any concerns about this? Do you ever feel that your social relationships or other responsibilities are affected when using AI tools? Why? What impact do you think AI technologies will have on the nursing profession and your education in the future? How do you interpret the concept of AIlessphobia? Do you think this fear has a counterpart in you or in your environment? |
Table 2
| n | % | |
| Gender | ||
| Female | 176 | 88.0 |
| Male | 24 | 12.0 |
| Age (Mean±SD=20.64 ± 1.93) | ||
| 17–20 years old | 100 | 50.0 |
| 21–30 Years | 100 | 50.0 |
| Academic year | ||
| Preparation | 9 | 4.5 |
| 1 | 59 | 29.5 |
| 2 | 44 | 22.0 |
| 3 | 57 | 28.5 |
| 4 | 31 | 15.5 |
| Knowing AI tools | ||
| Yes | 150 | 75.0 |
| No | 50 | 25.0 |
| Using AI tools | ||
| Yes | 163 | 81.5 |
| No | 37 | 18.5 |
| Most frequently used AI tool* | ||
| Search Engines | 3 | 1.8 |
| Computer | 6 | 3.7 |
| Canva | 1 | 0.6 |
| ChatGPT | 129 | 79.1 |
| Gemini | 1 | 0.6 |
| Deepsek | 2 | 1.2 |
| NotebookLM | 1 | 0.6 |
| Siri | 1 | 0.6 |
Table 3
| Variable | Group | Mean | SD | t | p |
| Gender | Female | 13.20 | 3.62 | 1.913 | 0.057 |
| Male | 11.67 | 4.10 | |||
| Age group | 17–20 | 12.55 | 4.01 | −1.785 | 0.076 |
| 21–30 | 13.48 | 3.33 | |||
| Knowing AI tools | Yes | 13.20 | 3.67 | 1.225 | 0.222 |
| No | 12.46 | 3.80 | |||
| Using AI tools | Yes | 13.36 | 3.52 | 2.829 | 0.005 * |
| No | 11.49 | 4.14 | |||
| Academic year | Mean | SD | F | p | |
| Prep−1 | 12.32 | 3.96 | 1.443 | 0.232 | |
| 2 | 13.02 | 3.92 | |||
| 3 | 13.65 | 3.41 | |||
| 4 | 13.35 | 3.21 |
Table 4
| Themes | Subthemes |
| Use and benefits of AI in education | Use in assignments and courses
Quick access to information Timesaving Support in patient care |
| Negative effects of AI | Weakening critical thinking
Negatively impact on decision-making Overdependence on ready-made information Reduced cognitive ability |
| AI addiction | Easy access is addictive
Intensive use during exam periods Time loss |
| Future perspective on AI | A supportive tool in the nursing profession
Technological developments in healthcare |
| Fear of being without AI | Habit formation
Quick access to information Loss of time |
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