Article

Nurse Education in Practice 79 (2024) 104062

Available online 10 July 2024 1471-5953/Crown Copyright © 2024 Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Navigating challenges and opportunities: Nursing student’s views on generative AI in higher education

Anthony Summers a,*, May El Haddad a, Roslyn Prichard a, Karen-Ann Clarke a, Joanne Lee a, Florin Oprescu b

a University of the Sunshine Coast, School of Health, Discipline of Nursing, Sippy Downs, Qld 4558, Australia b University of the Sunshine Coast, School of Health, Discipline of Public Health, Sippy Downs, Qld 4558, Australia

A R T I C L E I N F O

Keywords: Generative artificial intelligence ChatGPT Nursing education Higher education Student perspectives Ethical usage Patient care Technology integration

A B S T R A C T

Aim: This qualitative study aims to explore the perspectives of nursing students regarding the application and integration of generative Artificial Intelligence (AI) tools in their studies. Background: With the increasing prevalence of generative AI tools in academic settings, there is a growing in- terest in their use among students for learning and assessments. Design: Employing a qualitative descriptive design, this study used semi-structured interviews with nursing students to capture the nuanced insights of the participants. Methods: Semi-structured interviews were digitally recorded and then transcribed verbatim. The research team reviewed all the data independently and then convened to discuss and reach a consensus on the identified themes. Results: This study was conducted within the discipline of nursing at a regional Australian university. Thirteen nursing students, from both first and second year of the programme, were interviewed as part of this study. Six distinct themes emerged from the data analysis, including the educational impact of AI tools, equitable learning environment, ethical considerations of AI use, technology integration, safe and practical utility and generational differences. Conclusions: This initial exploration sheds light on the diverse perspectives of nursing students concerning the incorporation of generative AI tools in their education. It underscores the potential for both positive contribu- tions and challenges associated with the integration of generative AI in nursing education and practice.

1. Introduction

Generative artificial intelligence (AI) uses deep learning to generate human-like content in response to complex and varied prompts (Lim et al., 2023). It creates content using existing data from the sources it has access to and follows a specific style as prompted by the user. Due to the uniqueness of the content created by generative AI tools, common plagiarism checkers may struggle to detect this work (Tam et al., 2023). Importantly, generative AI tools, like ChatGPT, Scribe, Dall-E2 and Wordtune, are becoming more widely used in academia. However, their widespread accessibility by students raises concerns about the potential impact on the quality of outcomes in the higher education sector, including considerations such as accuracy, effective learning, personal development and career prospects (Chan and Hu, 2023).

The misuse of generative AI by nursing students is concerning due to the potential negative impact this may have on patient care (Irwin et al., 2023). For example, nursing students can create work using generative AI and submit it for marking as part of their coursework, without developing or applying any critical thinking and analysis to the work. Therefore, it becomes possible that students may complete their nursing degrees without learning the essentials of safe nursing practice. Impor- tantly, nursing students need critical thinking skills to make sound clinical decisions about patient care (Christianson, 2020).

Universities are increasingly recognising the importance of prepar- ing and supporting students across all disciplines in the ethical use of generative AI tools (Michel-Villarreal et al., 2023). Promoting the ethical use of these learning tools involves guiding students in using generative AI to enhance their learning, increase their critical thinking

* Corresponding author. E-mail addresses: [email protected] (A. Summers), [email protected] (M.E. Haddad), [email protected] (R. Prichard), [email protected]

(K.-A. Clarke), [email protected] (J. Lee), [email protected] (F. Oprescu).

Contents lists available at ScienceDirect

Nurse Education in Practice

journal homepage: www.elsevier.com/locate/issn/14715953

https://doi.org/10.1016/j.nepr.2024.104062 Received 11 April 2024; Received in revised form 9 June 2024; Accepted 8 July 2024

Nurse Education in Practice 79 (2024) 104062

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skills and interrogate the accuracy of the information provided to them. Teaching nursing students how to ethically integrate generative AI tools into their work could significantly contribute to ensuring safe patient care. Furthermore, the integration of emerging technologies, like generative AI tools, into nursing education holds the promise for more immersive personalised learning experiences. According to Sharma and Sharma (2023), generative AI tools can enhance students’ knowledge, skills and confidence, which enables them to make informed judgements and provide safe patient care, particularly in the field of remote patient monitoring. The teaching of nursing students to ethically use and inte- grate generative AI tools into their work could contribute to ensuring safer care is provided to patients (Sharma and Sharma, 2023).

Educating nursing students on how to use generative AI tools safely and effectively, to ensure that patient safety is maintained is important for nurse educators. Therefore, this study aimed to explore the view- points of nursing students, enrolled at a regional university, about the uses and potential integration of generative AI tools into their nursing studies.

2. Methodology

2.1. Research aim and design

To achieve the study aims, a qualitative descriptive design, guided by Sandelowski (2000) approach, was employed. This approach was deemed most appropriate as it aligns with the intent to offer a straightforward description of the phenomena, providing a factual description of the case through the viewpoint of the participants in their own language (Sandelowski, 2000). Furthermore, this approach is well suited for capturing the nuanced insights of nursing students’ engage- ment with generative AI tools within their studies. The Consolidated Criteria for Reporting Qualitative Research, devised by Tong et al. (2007), was used to report the results.

2.2. Participants

Ethical approval (A231897) was granted by the Human Ethics Board of the university. Subsequently, nursing students enrolled in three courses that allowed the use of generative AI tools, received targeted emails inviting them to participate in the study. As students agreed to participate, the lead researcher provided further information about the study and invited the students to provide convenient times for them to be interviewed. Students were interviewed on first come first served basis until data saturation occurred. A total of 13 nursing student self- nominated and participated in the study. There were seven female participants and six male participants. Ten participants were domestic students and three were classed as international students. No participant was in the third year of their studies, eight were from their second year and five from their first year.

2.3. Data Analysis

All the interviews were recorded and transcribed verbatim by the lead researcher. Data analysis followed a thematic analysis approach guided by Braun and Clarke (2006) six-phase process: (1) familiarisation with the data, (2) generating initial codes, (3) grouping codes into themes and allocating relevant data to each theme, (4) reviewing themes for coherence and saturation, (5) refining and generating meanings for each theme and (6) preparing the report with selected extracts and relating the analysis back to the research question. The data underwent independent review by team members, who later convened to discuss and reach consensus on the identified themes.

3. Results

Six themes were identified within the data related to the perspectives

of nursing students, enrolled at a regional university, regarding the integration of generative AI tools into their nursing studies. These were:

1. Educational Impact of AI Tools 2. Equitable Learning Environment 3. Ethical Considerations 4. Technology Integration 5. Safety and Practical Utility 6. Generational Differences

3.1. Educational impact of AI tools

Participants expressed that learning in nursing relied on experience, which generative AI does not offer. According to Participant 4, “you need experiential experience to help you learn, you cannot get that from gen AI.” Similarly, Participant 1 noted, “you are not experiencing learning, because the answer is provided to you, you haven’t sought out the answer.” Some participants felt that using generative AI bypasses the learning journey, as Participant 8 stated, “learning was bypassed by using generative AI.” This approach was seen to promote “lazier learners,” according to Participant 1.

Participants expressed concerns that the use of generative AI “closes people off to other streams of learning,” as noted by Participant 1. This, in turn, leads to a reduction in “critical thinking skills,” according to Participant 11. Participant 5 highlighted that relying on generative AI means “you are not using your brain, you are not critically thinking.” Participant 4 adds this may lead to students doing the “minimum to get their degree,” emphasising that “copying and pasting is not learning or understanding.”

The positive side of generative AI was highlighted by Participant 8, who stated it “helped with understanding,” as Participant 6 stated it can “provide a summary of concepts for you to learn and understand.” This learning, according to Participant 9, was done by “providing alternative perspective,” and Participant 2, “alternative ways to learn, i.e., dia- grams, videos, or animation." Generative AI also helps with learning, as Participant 4 states, students can use generative AI to “double-check if their understanding is correct.” This is confirmed by Participant 6 who stated if “the output is similar to what I had learnt and understood.”

Participants 9 and 10 felt their efficiency improved when using generative AI as it was able to “help with grammar,” allowing them to proofread assessments effectively. For Participant 3, using generative AI allowed them to become efficient because it “helped overcome language barriers.” As proofreading and translation were taken over by generative AI, students freed up time to focus on their learning. To help with this efficiency generative AI, as Participant 8 stated, the “gathering, appraising and finding information,” also becomes efficient.

Along with efficiency, the productivity of students also improved, as Participant 6 stated, generative AI allows me to “create work, reading lists and summaries in minutes, rather than hours.” Students were then able to focus on the learning of a topic or provided them with oppor- tunities to explore other related topics.

Increasing efficiency and productivity is beneficial, if the reliance and accuracy of the created content is understood. As Participant 11 stated, the content “needs to be taken with a grain of salt … they are good in certain context, but you cannot blindly accept it.” Participant 12 goes on to state, “there are nuances that a computer cannot pick up on.” These nuances can change how a patient reacts or what care is provided. Participants 4 and 9, stated this will improve as “generative AI tools will become more reliable and accurate in the future.”

The accuracy of the information created by a generative AI tool is questionable. As Participant 8 states, “how is the content created accu- rate and safe, when it is unclear where the information came from.” Participant 11 agreed, as you “cannot trust the accuracy of generative AI tools when they make up sources.” According to Participant 3, this could be improved if they “show you where it derived its information from,

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think the accuracy would improve.” Which, according to Participant 4, means “at some point it is going to become more reliable and accurate.”

3.2. Equitable learning environment

If an equitable learning environment is to be fostered, generative AI tools need to be available to all students. Participant 8 states, “a level playing field needs to exist or equitability among students won’t exist.” Because as Participant 10 states, if universities “enable and reward a student for less work, because they have access to generative AI and another student doesn’t, it is not equitable.” Participant 10 goes on to state, “to make it equitable, everyone needs access to the same tools to ensure the same success.”

Generative AI tools are capable of functioning across multiple lan- guages, the equitability of learning is possible, as Participant 2 high- lights “learning for those who have English as a second language will improve.” Participant 5 states this will happen because students can “ask questions in their first language and get results back in a way they un- derstand.” Equitability is further enhanced, according to Participant 10 as students can get their “grammar and diction used checked.” As Participant 3 explains students are unfazed by using generative AI in this fashion, as it is “similar to Grammarly, Studosity, or Learning Advisors.”

Equitability and fairness are not maintained, according to Partici- pant 3, if “students use generative AI to create and submit assignments.” Especially, as Participant 12 goes on to explain, “those who have used generative AI were not submitting their own work.” Therefore, Partici- pant 5 suggests those who have used generative AI “should be marked differently as they haven’t done the work or research.”

3.3. Ethical considerations

Participants discussed the ethical use of generative AI tools. Partic- ipant 10 explored the topic of maintaining academic integrity when using generative AI and felt that it was “akin to plagiarism,” with Participant 11 stating you are just “copying and pasting output and accepting that it is correct,” and Participant 1 adding you are “not making connections if you use generative AI, which is what you need to show in assessments.” Therefore, for academic integrity to maintained, generative AI needs to be used ethically. Participant 12 stated that generative AI work “is not the student’s work” and thus it is unethical to use. Participant 3 thought “it is a cheating tool,” what Participant 12 states gives students “an unfair advantage.”

Generative AI was consistently seen as unethical, with a perception of it being misconduct, as Participant 3 saw it as “a tool to cheat,” Participant 4, “it is plagiarism,” and Participant 8 “it is a copy and paste tool.” Participant 3 highlights “the work is not the students,” and Participant 9 “the student does not own the work produced,” and Participant 5 “something else is doing the assignment,” meant genera- tive AI use was misconduct. This unethical use then becomes in- equitable as Participant 12 stated, the “student is being graded on work that is not their work.” Participant 12 goes on to stating that if “unregulated and allowed students will graduate without completing the correct work.” Which Participant 8 feels will ultimately “devalue their degree.”

The ethical use of generative AI allowed participants to comprehend a topic better as Participant 3 stated it helped “generate ideas on a topic,” or Participant 6 stated provide “alternative ways of presenting the concept,” and Participant 7 stated helped “further understanding of a topic.” These views helped participants to develop different perspec- tives on a topic. Participant 10 also highlighted the ethical benefit of generative AI in assisting students who had English as Second Language comprehend a topic, because it could “convert a topic into their own language and explain something in their own language.”

Along with the comprehension of concepts, generative AI was ethi- cally used to help speed up research time as Participant 11 stated it was able to “provide a summary of an article,” and Participant 1 “develop a

summary of a topic,” by being able to “provide a summary or a pre- sentation of lecture.” Generating summaries of materials allowed par- ticipants to gain an understanding in a faster way, compared with traditional ways of summarising information. Participant 3 felt that summaries were able to “provided a broad overview of a topic/concept.”

Being able to structure and edit work, was another ethical use par- ticipants discussed. Participant 10 highlighted this included “asking for it to proofread an assignment,” Participant 2 “shorten the word count of an assignment,” and Participant 9 “help with the grammar used.” participant 3 also added that Generative AI “helped to link ideas together,” to allow smoother argument flows in their discussions.

3.4. Technology integration

Participant 6 argued that generative AI was “another tool, similar to Google, that leverages technology to assist in the generation of assess- ment content,” and if used ethically, should not pose a problem. Yet, Participant 8 argued, the integration of generative AI technology into education “can be negative if it is then used unethically,” because if used blindly you cannot trust it. Participant 9 did highlight that it is “tech- nology that is here to stay, so we need to learn to use it.”

Should generative AI technology be integrated into nursing, Partic- ipant 9 felt “everyone should have access to it.” Many participants felt that integrating generative AI into nursing, would as Participant 1 stated lead to nursing “focus more on the screen and not fully engage with patients and may miss subtle clues as to what is happening.” Participant 3 felt that “nursing can be harmed if those using it do not critically think about what it is producing,” with Participant 5 adding “nursing is ho- listic and generative AI misses this holistic approach.”

Participant 8 expressed that “nursing is about interacting with a patient face-to-face and not through a screen,” with Participant 5 add- ing, “face-to-face interactions allows the nurse to pick up on the layers within conversation and the subtext and non-verbal cues a patient provides,” and Participant 10 adding, “nurses are needed to help inter- pret the emotions of the patient.” These expressions highlight that the participants felt that there is an importance in interacting with patient’s face-to-face rather that through a computer, because as Participant 1 states nurses “function in the grey emotive areas of life, which genera- tive AI cannot do,” which is confirmed by Participant 10 who stated “nurses have a level of empathy that generative AI cannot demonstrate,” because nurses “focus on an individual’s need and adapt and do not focus rigidly on rules,” which is how generative AI functions.

Some benefits from the use of generative AI in nursing were expressed by Participant 6, who stated, “it will reduce the amount of time we waste researching things,” and Participant 2 who stated generative AI can “produce a rapid check and summarisation of current guidelines.” These benefits are in the non-face-to-face aspects of the nursing profession, not in the everyday face-to-face patient interactions nurses do. Another example of this non-face-to-face benefit was expressed by Participant 8, who stated “generative AI could also allow the nurse to develop education packages for patients that is at the level of their understanding.”

Participant 11 felt that the integration of technology into practice needed to occur slowly, “as it is new technology and there is a lack of clarity around accuracy.” Yet, Participant 2 felt that people “adapt to new technology really well, so that they are not left behind.” With Participant 7 feeling that as the quality of the output from generative AI tools improves, the integration of generative AI will see an “improve- ment in patient safety and care.”

When technology is integrated into the classroom, Participant 8 felt that students need to “adapt their ways of learning to ensure they consider how generative AI is used.” Participant 8 also felt that teachers need to consider how they use generative AI in teaching and in assess- ments, as generative AI is now in existence so “just like the calculator took over the abacus, generative AI is a tool we need to adapt to using.”

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3.5. Safety and practical utility

Participant 2 saw generative AI as a tool that “improves patient safety,” by providing quick and definitive answers to tasks like medi- cation calculations, where doing it manually “2 or 3 times could provide the wrong answer and delay the administration of medication, until everyone is happy the correct answer has been obtained.” Participant 7 also generative AI tools “would also improve patient safety,” especially with the development of nursing specific tools, “as routine repetitive work could be done by the generative AI tool.”

The safe use of a generative AI tool in delivering patient care is as to deliver person-centred care you need to respond all the person’s needs as Participant 10 stated, “how can a generative AI tool provide appropriate care, when it cannot interpret the needs of a patient, when it cannot see or hear all the subtle nuances a patient is provides in the face-to-face discussion with their nurse.” The understanding of these nuances, ac- cording to Participant 11 means “people currently trust a human, more than a robot to provide their care.”

The practical utility of generative AI was identified in the non-direct contact aspects of the nursing profession, Participant 11 stated this included the “researching of areas to improve patient safety.” Partici- pant 5 also felt that the safety of patients could be improved by “generative AI tools as the can look at multiple sources of information to determine possible outcomes for patients.”

3.6. Generational differences

The final significant theme to emerged during the interviews was around generational differences. Participants expressed diverse views based on their generational backgrounds, highlighting a potential age- based divide in attitudes towards technology adoption. Several partici- pants attributed their perspectives on generative AI to generational differences. For instance, Participant 1 stated, "I think it’s a generational thing. To be fair to any new technology, it does have limits for people who use it. I mean, I don’t expect my mom to be using ChatGPT anytime soon." This comment suggests a recognition of varying levels of comfort and familiarity with technology across the different generations. Participant 8, expressed a similar sentiment, stating, "I’m a mature age student, so you know, there might be something in the way that the youngest students use it and don’t even consider the same things that I’m considering, because they’re so you know, their sense of the use- fulness of things are always necessarily going to lead to that."

4. Discussion

This study represents an initial exploration that has focused on un- derstanding the experiences and attitudes of nursing students regarding the use of generative AI. Interestingly, those nursing students who were interviewed expressed more unfavourable views about the use of generative AI than favourable views. The implications and applications of this preliminary data are discussed next.

In the realm of nursing education, several participants did express the view that acquiring practical experiences and engaging in reflection are essential for comprehending the essence of being a nurse. This viewpoint aligns with the experiential learning theory of Kolb and Kolb (2005) and is associated with professional identity development. This highlights the concern that using generative AI could impede this learning process, especially if the final assessment is tailored for students who aim to fulfill the “minimum to get their degree,” as noted by Participant 4.

This minimum effort then becomes a cause of concern for the pro- fessional future of nursing. If a student is merely meeting the minimum requirements for obtaining their degree, it raises questions about whether they will possess the skills, maturity and professional identity necessary to become a nurse. As highlighted by participants in this study, nurses need critical thinking skills, which according to Shirazi

and Heidari (2019) enable them to discern essential data and address clinical and human challenges. Participants also emphasised the importance of communication skills to understand the nuances of con- versations and non-verbal cues, crucial for providing safe and effective patient care. As nurses deal with the grey emotive area of life, they therefore, need to be able to adjust their perspectives and care once a subtle nuance is revealed to them, something a generative AI tool cannot do when it rigidly follows the rules of its prompts.

Critical thinking also plays a crucial role in assessments, as partici- pants expressed concerns that using generative AI might hinder students from making the necessary cognitive and theoretical connections needed to demonstrate understanding of what is being assessed. Vil- larroel et al. (2018) argue that appropriate assessments are essential in higher education for students to demonstrate the depth of learning they succeed in mastering. Therefore, submitting work, which is not their own is not demonstrating the depth of their learning. If students fail to demonstrate their own acquired understanding of a topic, it raises questions about their ability, as future nurses, to provide safe patient care.

Some participants argued that submitting academic work generated by AI constitutes academic misconduct. Others were concerned about the fairness of assessment marking, noting that while some students invest significant effort into creating their assessments, those students using generative AI tools may exert minimal effort yet receive compa- rable marks. While the increase in marks may be favourable for the student, it is crucial to ensure that knowledge acquisition, a fundamental aspect of assessments is demonstrated for future safe nursing practice. Ultimately, if students rely on generative AI tools to complete their as- sessments without demonstrating the required knowledge acquisition, then the value of holding a nursing degree could be compromised.

The participants’ views on the generation differences of generative AI shed some light on the complex interplay between generational at- titudes and the adoption of generative AI tools in nursing education. While some participants viewed generative AI as a natural part of their technological upbringing, others expressed scepticism or reluctance, citing concerns about its impact on learning experiences and the development of critical thinking skills. The differences in these views suggest that a one-size-fits-all approach to integrating AI tools into nursing education may not be effective and that educational strategies should consider the diverse perspectives of students from different generations.

Future studies should explore the generational differences in greater detail to better understand how such differences might influence the effective integration of generative AI tools in nursing education. By considering these nuances, educators and policymakers can develop more tailored approaches that effectively address the needs and con- cerns of students across different generational cohorts. Additionally, future studies could focus on providing a more intricate examination of students’ perspectives on ethical and unethical application of AI in nursing education. Furthermore, there is a need to delve deeper into exploring the positive aspects and applications of AI in nursing educa- tion in future studies.

5. Strengths and limitations

A strength of this work is that it demonstrates what nursing students think about the use of generative AI in their studies. Using the voice of these students, the research team were able to describe six themes, (1) Educational Impact of AI Tools, (2) Equitable Learning Environment, (3) Ethical Considerations, (4) Technology Integration, (5) Safety and Practical Utility and (6) Generational Differences, about generative AI and how it impacts on their studies. Examples of the student voice within these themes highlight both the positive and negative aspects of using generative AI.

This study is limited by the sample size used within the study. A larger sample population would be needed to confirm whether the six

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identified themes are an accurate representation of the thoughts of nursing students on generative AI. Further work with students from other disciplines, from different age groups and from either an inter- national or national background will help to confirm whether the themes identified are relevant to everyone.

6. Conclusion

This initial exploration has provided insights into the diverse per- spectives of nursing students regarding the integration of generative AI in nursing education, particularly in the context of assessments. While some participants highlighted the positive aspects of generative AI, there remains a lack of understanding and regulation concerning how these positive aspects can be effectively used to demonstrate knowledge acquisition in nursing education.

Most participants expressed confidence that nursing as a profession is safe from the encroachment of generative AI tools. They acknowledged potential benefits in the use of such tools for non-patient facing tasks. However, the consensus was that in patient facing tasks, where genuine interpersonal skills are paramount, nurses should possess the ability to discern subtle behavioural cues, a capability currently beyond the reach of existing generative AI versions. This emphasises the indispensable human touch required in patient care, a dimension that generative AI tools may lack currently.

The use of generative AI tools is still being fully evaluated and their benefits and pitfalls are still to be fully explored. What this exploratory study has shown is that students feel there is a benefit of using genera- tive AI ethically, but if used unethically then the value of their degree could be undermined. Educators should prioritise integrating ethical use of generative AI into learning and assessment strategies in their courses, to safeguard the integrity and value of obtaining a degree.

Nursing is a profession characterised by face-to-face interactions. Participants in this study expressed concerns that this aspect of nursing would be lost with the use of generative AI tools. They emphasised that the nuanced human contact and interactions play a pivotal role in shaping patient experiences and at this point of time, generative AI tools cannot replicate this dimension. However, participants acknowledged potential benefits in generative AI in the non-face-to-face aspects of patient care, but even here, care needs to be taken to ensure the accuracy of generative AI content.

Ethical approval

Human Ethics Board of the University of the Sunshine Coast A231897

Funding source

None declared.

CRediT authorship contribution statement

Joanne Lee: Writing – review & editing, Validation, Formal analysis.

Karen-Ann Clarke: Writing – review & editing, Validation, Formal analysis. Florin Oprescu: Writing – review & editing, Validation, Formal analysis. Anthony Summers: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodol- ogy, Formal analysis, Data curation, Conceptualization. May El Had- dad: Writing – review & editing, Validation, Formal analysis. Roslyn Prichard: Writing – review & editing, Validation, Formal analysis.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

none declared.

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