Artificial intelligence in nursing education: Student perspectives on benefits, risks, and readiness for NCLEX-RN

The American Nurses Association (ANA) recently released a revised edition of the ANA Code of Ethics for Nurses, updating the 2015 edition. The newest edition explicitly addresses the use of AI and notes that the expertise of nurses should be used to influence the use of AI in clinical practice. Furthermore, the newest edition of the ANA Code of Ethics for Nurses continues to outline the profession’s core ethical principles while expanding guidance related to emerging technologies, stating that when nurses use AI in practice, that use must be balanced with establishing and maintaining client trust, confidentiality, and privacy.^[5] AI is transforming healthcare delivery, influencing diagnostics, risk reduction, clinical decision making, care coordination, and access to services. In acute care settings, tools such as sepsis early warning systems, deterioration prediction scores, fall risk algorithms, and pressure injury prediction models are increasingly embedded in routine clinical tasks. Nurses are especially impacted as these technologies intersect with their clinical judgment, communication, and workflow management. In 2021, the ANA also published the Nursing: Scope and Standards of Practice which emphasizes that ethical practice is integral to all aspects of nursing. Standard 7, titled Ethics, directs nurses to safeguard sensitive information and uphold ethical, legal, and regulatory expectations, reinforcing the principle of non maleficence.^[6] In addition, Standard 17, titled Resource Stewardship, highlights the responsibility to use appropriate resources, including connected health technologies, in ways that support safe and effective patient care.^[6] As AI tools such as ChatGPT become increasingly embedded in academic and clinical contexts, understanding how nursing students use these technologies is essential. One study noted that while using LLMs to enhance academic work is not inherently problematic, risks arise when students become overly reliant on these tools or fail to recognize inaccurate information.^[2] Within the 2025 ANA Code of Ethics for Nurses nursing educators are called upon to ensure the development of knowledge, skills, and the moral dispositions of nursing students that are essential to clinical practice.^[5] Insights from this study may help educators develop policies, learning activities, and syllabus language that align AI use with updated ethical standards and support responsible integration into nursing education and clinical practice.

The purpose of this study was to investigate the experiences of nursing students in utilizing AI for personalized learning, including the challenges faced, ethical considerations, and its role in preparing for the NCLEX-RN exam.

A descriptive qualitative phenomenological design was used to gain an in depth understanding of undergraduate nursing students’ experiences with AI. This approach was appropriate for exploring how students make meaning of AI in their academic studies and clinical preparation and/or encounters. Data collection included a semi structured conversational interview and an online survey with open ended questions. The study was guided by four research questions: 1) What opportunities does AI offer for personalized learning? 2)What challenges are associated with using AI in nursing courses? 3)What are students’ perceptions of ethical considerations related to AI use in coursework and clinical practice? And 4) How is AI being used to support NCLEX-RN preparation?

Interviews were offered in person, via Zoom, or in small focus group formats and were designed to last 15-30 minutes. With participant permission, interviews were audio recorded and transcribed verbatim using NVivo Transcription services. Data management was supported by NVivo and institutional Google tools. Qualitative thematic content analysis was used to identify patterns and themes.

The study was conducted at a four year university in the Midwest. A purposive sampling strategy was used to recruit traditional undergraduate baccalaureate nursing students. Recruitment occurred through word of mouth, flyers distribution, and email invitations. A total of 58 students participated, representing sophomore, junior, and senior academic levels. Freshman were excluded as they are not enrolled in the nursing program.

Data were collected over an eight week period from October to November 2025. One participant completed an individual conversational interview, while the remaining participants completed an anonymous online survey with open ended questions. Surveys were distributed via email to sophomore, junior, and senior level nursing students, and flyers were posted to encourage participation. The researcher also visited six undergraduate nursing classrooms to support recruitment efforts. The interview lasted approximately 15–20 minutes and was audio recorded with participant consent. Verbatim transcription was completed using NVivo Transcription services, and all data were organized and managed using institutional Google tools. Participation was voluntary and confidential, and students were informed that they could withdraw at any time without penalty. Informed consent was obtained prior to the interview and at the start of the online survey. Institutional Review Board (IRB) approval was secured before data collection, and the study was classified as exempt.

Responses were analyzed using a qualitative thematic analysis approach guided by Braun and Clarke’s six phase framework.^[7] This method provided a systematic and transparent process for identifying patterns within students’ descriptions of their use of AI in learning and test preparation. The analytic process began with repeated readings of survey responses to achieve immersion in the dataset. A coding scheme was developed according to the four research questions. Codes were generated to reflect students’ own words and to organize the data. The final codebook was composed of 31 codes. Themes were then reviewed in relation to both the coded extracts and the full dataset to ensure internal consistency and analytic rigor. During this stage, some themes were refined or removed. The final phase involved synthesizing themes into a coherent narrative account supported by quotations from participants’ responses. This analytic approach enhanced transparency, credibility, and depth in representing BSN students’ perspectives on their use of AI in academic and clinical learning contexts.

Rigor was maintained throughout the study using established criteria for trustworthiness, including credibility, dependability, confirmability, and transferability. Credibility was supported through prolonged engagement with the data, including repeated readings of survey responses and the interview transcript to ensure accurate representation of participants’ perspectives. Inductive coding and theme development were conducted systematically, and analytic decisions were documented to maintain transparency. Verbatim transcription of the interview further strengthened accuracy. In addition, a faculty peer not involved in the study reviewed the preliminary themes and illustrative quotations to ensure they were appropriate and grounded in the data. Dependability was enhanced by maintaining a clear audit trail of coding processes and theme refinement using NVivo and institutional Google tools. This documentation ensured that the analytic process was traceable and consistent. Confirmability was supported by deriving interpretations solely from participants expressed perspectives as noted in the survey responses and interview transcript. Transferability was supported by providing detailed descriptions of the study setting, sample characteristics, and data collection procedures.

A total of fifty-eight students participated in the study (N-58), with fifty-seven (n = 57) participants completing the online survey and one completing the interview (n = 1). Most students were between the ages of 18 to 24-years old and Caucasian, with Sophmores level students representing just under half all the participants (see Table 1).

The results of the current study allowed the researchers to highlight the lived experiences of undergraduate nursing students in utilizing AI in their educational studies. Analysis of participant responses revealed overarching themes within each of the four questions, which describe how undergraduate nursing students are integrating AI into their learning practices. Collectively, these themes illustrate a pattern of students using AI not as a shortcut, but as a strategic learning partner that enhances organization, comprehension, and exam readiness.

First, students described AI as a source of personalized learning support (see Table 2). Participants emphasized AI’s ability to tailor explanations and provide clarification when concepts felt overwhelming. Students also used AI to rephrase questions and support idea generation for academic tasks, underscoring its role as a flexible, responsive learning tool. Second, students highlighted AI’s value in creating study guides and integrating resources. Many reported uploading lecture notes, PowerPoints, or textbook excerpts to generate concise summaries and structured study materials. This process helped them organize large volumes of content into manageable formats and save time. Third, AI played a prominent role in practice question generation and exam preparation. Students frequently relied on AI to create NCLEX-style questions, quizzes, mock exams, and focal points aligned with course content. These tools supported self-assessment, reinforced key concepts, and helped learners determine their readiness for the licensure exam. Finally, students described increased engagement and retention through AI-supported tools. Flashcards, podcasts, case studies, and randomized question banks were cited as features that made studying more dynamic and helped reinforce learning. Together, these themes demonstrate that students are leveraging AI to personalize their learning, streamline complex information, enhance exam preparation, and engage with content in more interactive ways.

First, students expressed significant apprehension about the accuracy, reliability, and functional limitations of AI tools. Participants frequently noted that AI-generated content could be incorrect, incomplete, or misaligned with nursing curriculum expectations. They emphasized the need to verify AI generated content. Some also described functional challenges, which limited its usefulness. Second, students highlighted academic integrity and ethical or professional risks. Many respondents associated AI with cheating, plagiarism, and academic dishonesty. They also raised concerns about the credibility of AI-generated references, potential bias, and privacy issues. Third, students worried about the loss of critical thinking, creativity, and clinical preparedness. Participants feared that overreliance on AI could erode essential cognitive skills, including independent judgment, problem-solving, and creative reasoning. Several respondents linked AI use to diminished hands-on practice and practical thinking, raising concerns about readiness for the NCLEX and real-world patient care (see Table 3).

Student responses revealed several interconnected ethical concerns related to the use of AI in nursing education and clinical practice. These concerns are clustered into three themes. First, students expressed substantial concerns about trust, reliability, and patient safety. Participants described skepticism toward AI’s accuracy and misinformation in outputs. Several students emphasized that inaccurate AI-generated information could pose risks to patient safety, particularly if used without verification. Concerns about confidentiality and potential Health Insurance Portability and Accountability Act (HIPAA) violations further contributed to apprehension about integrating AI into clinical practice. Second, students highlighted academic integrity, accountability, and professional boundaries as central ethical issues. Many respondents associated AI with cheating, plagiarism, and shortcuts that bypass meaningful learning. They worried that AI could obscure accountability in coursework and diminish the expectation that nursing students produce original work. Participants stressed that ethical use requires clear boundaries to ensure that AI supplements, rather than replaces, professional judgment. Third, students described fears of erosion in critical thinking and clinical reasoning. Respondents expressed concern that habitual reliance on AI could weaken cognitive skills, including clinical judgment and problem-solving. Together, these themes illustrate that students view AI as a powerful but ethically complex tool that carries risks which intersect with trust, academic integrity, professional identity, and clinical competence (see Table 4).

Student responses revealed several interconnected ways in which AI is being used to support NCLEX preparation. These uses clustered into three themes, with a fourth theme reflecting uncertainty (see Table 5). First, students overwhelmingly described using AI for practice question generation. Participants emphasized that AI enables the creation of NCLEX style questions, case studies and med math problems. Students valued the ability to generate an “endless” supply of exam aligned questions, noting that these tools helped reinforce clinical reasoning and promote “thinking like a nurse.” Second, students highlighted AI’s role in study guide creation, summarization, and resource organization. Many respondents reported uploading lecture slides, notes, and readings into AI tools to produce structured study guides, summaries, and flashcards. This process helped condense large volumes of content, identify weak areas, and streamline their preparation. Students described AI as a tool that made studying more efficient and manageable. Third, students described AI as a source of adaptive, personalized, and feedback driven learning support. Participants noted that AI could tailor study plans, adjust practice questions based on performance, and provide immediate rationales or explanations. These features were perceived as beneficial for targeting individual learning needs and reinforcing understanding through timely feedback. A fourth, less frequently mentioned theme reflected uncertainty. Some students expressed uncertainty or distrust regarding AI’s accuracy or alignment with evolving NCLEX formats. Others observed that faculty and commercial platforms already incorporate AI into question banks and instructional materials, suggesting growing institutional acceptance. Lastly, first year students repeatedly noted this question was non-applicable. Together, these themes illustrate that students are using AI as a multifaceted tool to support NCLEX preparation.

For learning in general, students reported using AI to support personalized learning, organize content, and enhance exam preparation. AI was described as a flexible tool that could adapt explanations, generate study guides, and create practice questions aligned with course content. Students also highlighted the value of interactive modalities such as flashcards and scenario-based prompts that increased engagement and supported retention. The emphasis on interactive tools and personalized support reflects broader trends in AI-enabled education.^[8] However, students also identified substantial challenges, particularly related to accuracy, reliability, and academic integrity. These concerns mirror national guidance emphasizing the need for human oversight, verification of AI-generated content, and clear boundaries.^[9,10]

In describing challenges, students emphasized substantial concerns regarding accuracy, reliability, and functional limitations. Many students noted that AI outputs required verification and did not always align with nursing curriculum expectations. Academic integrity emerged as a central theme, with students expressing worry that AI could facilitate cheating, plagiarism, or superficial learning. Concerns over open access to AI, such as ChatGPT, and potential student plagiarism was also identified in another study.^[11] Students also voiced concerns about diminished critical thinking and clinical preparedness and the need to preserve core nursing competencies. Together, these themes illustrate that while students recognize AI’s potential, they also perceive substantial risks. This aligns with a study conducted with practicing nurses in which they indicated that AI tools need to provide meaningful clinical support that aligns with clinicians’ professional judgment.^[12] In another study, practicing nurses praised the rapid analytical capabilities of AI and AI’s efficiency to offer insights into changing patient conditions, but nurses were quick to note how patients seek virtues that transcend AI’s capabilities, such as compassion and understanding.^[13]

These findings underscore the need for clear guidance and faculty-supported strategies to ensure responsible integration of AI in nursing education and use of AI in clinical practice, which both preserve core nursing values and human connection, and focus on improving patient outcomes. Additionally, students also raised concerns about the credibility of AI-generated references, potential bias, and privacy issues. These concerns reflected a strong sense of professional accountability and the importance of maintaining trust in nursing education. To help reduce student anxiety around AI use, faculty should establish clear policies and guidelines and intentionally integrate AI into the curriculum, so students have structured opportunities to develop critical thinking, problem-solving, and independent reasoning skills.^[8] Furthermore, these policies could mirror policies within healthcare systems which currently utilize AI to improve nursing workflows and assist providers in early detection and interventions as patient conditions change; therefore, enhancing clinical decision-making.^[13]

Ethical perceptions added further complexity, often overlapping with the themes identified within the challenges.

Students questioned whether AI could be trusted in clinical contexts, citing risks to patient safety, confidentiality, and professional accountability. They also expressed concern that AI mediated interactions could erode human connection and interpersonal skills essential to nursing practice. Authors in one study reinforced this point by emphasizing that nurses must use AI in ways that are thoughtful, ethical, and rooted in transparency, accountability, and empathy to support truly human centered care.^[12] Despite these concerns, some students acknowledged that AI could be used ethically when applied with moderation and transparency. These findings are similar to what two others studies noted in regard to safeguarding patient data, identifying algorithmic bias, ensuring transparency, and upholding professional accountability when using AI tools in nursing education and clinical practice.^[13,14] Furthermore, practicing nurses had similar reservations as nursing students in this study, in that they had concerns about the preservation of nursing expertise and the unique human connection central to nursing when AI is integrated in clinical practice.^[13]

Regarding NCLEX-RN preparation, students described AI as a powerful tool for generating exam-focused practice questions, summarizing content, and providing personalized study plans with immediate feedback. However, students also expressed uncertainty about AI’s ability to keep pace with evolving exam formats and emphasized the need for caution when relying on AI for high-stakes preparation. Taken together, these findings illustrate that undergraduate nursing students view AI as a multifaceted educational resource that enhances personalization, efficiency, and exam readiness, yet simultaneously raises concerns about accuracy, ethics, and the preservation of essential nursing competencies. These findings align with recommendations from other studies that noted how AI could be used as a teaching strategy in nursing education to support critical thinking through the generation of adaptive quizzes, case study generation, and automated feedback.^[15] Furthermore, it is recommended that nursing faculty should embrace emerging technologies with intentionality.^[16]

Faculty have an opportunity to role model use of AI and provide structured guidance to students. The findings of this study underscore the need for nursing programs to establish clear guidelines for ethical and responsible AI use. National organizations emphasize that AI should augment, not replace, clinical reasoning and professional judgment.^[9] Integrating AI literacy into curricula may help students navigate issues of accuracy, bias, and confidentiality while strengthening their ability to use AI as a supplemental learning tool.^[14] Programs should also consider how AI supported study strategies can complement traditional teaching without undermining critical thinking or academic integrity.^[15] Lastly, nursing students reported improvements in learning outcomes, clinical practice, and overall satisfaction when using AI tools, thus faculty should embrace these new methods of learning.^[4]

Future studies should explore how AI influences learning outcomes, clinical judgment, and NCLEX-RN performance over time. Longitudinal research could examine whether early exposure to AI supported learning strengthens or weakens critical thinking and clinical preparedness. Finally, research examining the effectiveness of structured AI guidelines or curricular interventions could inform best practices in nursing education.

Several limitations should be considered when interpreting the findings of this study. First, the sample was drawn from a single four year Midwest university, which may limit transferability to other nursing programs with different curricular structures, student demographics, or institutional cultures. The reliance on self reported perceptions may also introduce response bias, as students who hold strong positive or negative views about AI may have been more likely to participate. Additionally, the study captured student experiences during a period of rapid evolution in AI technologies and NCLEX-RN testing formats. As AI tools and exam expectations continue to change, students’ perceptions may shift, and some findings may become time bound. The study also focused on students’ reported behaviors rather than direct observation of AI use, which may limit insight into how AI is actually integrated into study routines or clinical preparation. Finally, because the survey was administered online, participants may have interpreted questions differently, and the absence of follow up probing for most respondents may have constrained the depth of individual responses. Despite these limitations, the study offers meaningful insights into how undergraduate nursing students perceive and engage with AI across learning, ethics, and NCLEX-RN preparation. The findings provide a foundation for future research exploring faculty perspectives, longitudinal changes in AI adoption, and the development of evidence based guidelines for responsible AI integration in nursing education.