Intelligence Amplified: Leveraging AI for Transformative Nursing Education Research

Abstract

The potential applications of artificial intelligence (AI) in nursing education research seem to be unlimited. AI has the potential to support nursing education researchers at every stage of the research process. However, there remains a hesitancy and uncertainty for many nurse researchers to use AI in their work. For example, a colleague told me of hesitating to click on an icon in an AI platform for fear of what might happen. Fears and concerns are not just in nursing. In a recent survey, Wiley (2025) examined the current state of AI use in research across disciplines. They found that researchers were concerned about ethics (54%), information privacy (47%), and a lack of clear guidelines with the need for additional education (63%). The number of AI platforms is growing rapidly. There are platforms to conduct literature discovery, create surveys, analyze data, and write and summarize text, to identify a few. My focus here is not on specific AI platforms. Rather, it is to energize researchers to leverage AI’s potential and highlight how AI may be applied throughout the research process, with practical examples tailored to nursing education research. LITERATURE REVIEW, PROBLEM IDENTIFICATION, RESEARCH QUESTIONS Identifying the problem and germane research question(s) is a crucial stage in the research process. AI has the capability to enhance the efficiency and depth of literature exploration in this preliminary stage. As a starting point, literature mining tools can retrieve and scan thousands of articles to identify trends, knowledge gaps, and unexplored areas while also providing summaries of existing data (Bolaños et al., 2024). Additionally, a scan may identify other researchers conducting similar research. However, a word of caution is warranted at this point, as clearly articulated by Oermann (2024a) in a recent editorial in this journal: AI is a tool, and “these tools do not have access to full-text articles in MEDLINE, CINAHL, and other databases that are behind a paywall” (p. 337). As researchers, we must recognize and acknowledge the limitations of AI tools. They can make mistakes, be inaccurate, and have blind spots that may introduce bias. We must exercise due diligence at every step of the process. Another area that AI can help with is the reframing of general research topics into something that is researchable. AI can assist in formulating questions that are clear and answerable, generating multiple variations across different designs and approaches. One’s area of research interest is often broad, for example, the concept of presence in nursing education. Using AI, a researcher wanting to study the assessment of nursing presence might uncover inconsistent definitions in the nursing literature, rendering empirical assessment challenging. This knowledge can assist the researcher in formulating a potential research question that directly addresses the problem. STUDY DESIGN Selection of the methodology to answer one’s research question(s) can also be challenging, particularly for the novice researcher. AI can assist with generating clear and measurable research questions and hypotheses, the important next step in determining the study design. Furthermore, AI can support the researcher by suggesting potential designs based on purpose, research questions, and context. When choosing a possible methodology, it is important to develop a succinct prompt to answer a specific research question and possibly explore other options. However, writing a strong prompt to support this process takes practice. A vague prompt can lead to a poor response from AI. An example of an effective prompt for exploring nursing presence might be: “Summarize recent methodologies used to study nursing presence in nursing education and recommend a study design based on current trends. Include citations.” DATA COLLECTION AND ANALYSIS Collecting evidence is the next step, and creating a survey, demographic questionnaire, or interview questions can be a daunting process. As a starting point to guide data collection, the researcher can prompt AI to generate potential items and questions based on the research question. However, AI-generated research questions and hypotheses must be tested with evidence (Extance, 2018), and the researcher must acknowledge personal responsibility for modifying and adjusting questions to fit the study design, population, and context. For intervention or longitudinal designs, AI can offer timelines, procedures, and data collection protocols to consider during decision-making. AI tools for qualitative data analysis have existed for many years, and their ability to code and generate potential themes has accelerated in the past decade. However, there is discussion in the literature regarding the role of AI for qualitative analysis. There seems to be consensus that AI might serve as a useful tool in approaches based on data coding or content analysis (Maj et al., 2025) but may not be able to capture a deeper interpretation of the data (Morgan, 2023). Another concern may be the inability of others to determine the credibility and trustworthiness of the analytic decisions. Nevertheless, we may see a paradigm shift in terms of AI use in qualitative data analysis. Likewise, there are multiple quantitative data analysis tools available for identifying patterns or predicting outcomes in data sets. For example, one could apply a machine learning algorithm to identify students who are at potential risk for failing. In addition, AI-learning analytic systems can be trained to extract data to optimize modeling. It is important to document the AI method used, including codes and parameters, for purposes of replication. Another tool is data visualization, which can be used to facilitate connections among different variables prior to statistical analysis. ETHICAL CONSIDERATIONS AND DISCLOSURE The learning curve for everyone in the ethical application of AI in our research is continually evolving. Resnik and Hosseini (2025) stated that “although using AI in scientific research has steadily grown, ethical guidance has lagged far behind” (p. 2). Best practice is remembering that AI is a tool to augment our research, but we are the humans making the decisions. Some areas of ethical concern that have been identified include transparency, informed consent, accountability, bias, privacy, and security. With regard to transparency, disclosure is essential. Research participants must know how AI will be used, whether in data collection, analysis, or during an intervention. This information must be explicit and clearly stated in the informed consent. This includes identifying who is responsible for AI’s performance and any potential risks. For example, if AI miscodes qualitative data, it is the researcher who is responsible, not the machine. As with all research, secure data storage and participant confidentiality must be ensured. Be certain to remove names and locations prior to uploading data into any platform! Bias is a critical concern when using AI in all steps of the research process: It can affect the ethical integrity of the research. Nursing education represents a diverse population of students and faculty. Unfortunately, the dominant data training of AI may lack diversity and marginalize voices that do not align with its output. This may serve to further reinforce existing inequities reported in research and subsequently implemented in our teaching practice. Reflecting on your own positionality (Patterson & Morin, 2025) is a beginning step to address AI in your research. Bias is a human issue; however, one could argue that bias has infiltrated the data outputs of AI. LIMITATIONS IN NURSING EDUCATION RESEARCH Recognizing and addressing the limitations of AI in your research is an important step in the research process. We must remember that AI is a tool to facilitate our work, a tool that cannot grasp human experience or context the way we can. Loss of the intricacy and nuances of the emotional experiences of faculty or students is significant for our teaching practice. The complexity of the phenomena we study may be oversimplified. There will always be a need for human oversight to interpret the significance of research results. The majority of nursing journals require that authors be transparent in their use of AI in their research and writing. The current author guidelines for Nursing Education Perspectives (NEP) specifically state that “authors are fully responsible for the content of their manuscript, even those parts produced by an AI tool and are thus liable for any breach of publication ethics. Use of AI tools must also be indicated on the title page.” As eloquently noted by Oermann (2024b), “We will be revisiting the use of AI to write scholarly publications for years to come…. If you use AI tools in the preparation of your paper, it is your ethical responsibility to disclose it” (p. 52). SUMMARY AI use in research is exploding as I write this editorial. Its transformative potential for the science of nursing education is within our reach. This will require training in the use and application of AI for nurse educators and researchers who need the “right preparation…to avoid known risks” (Byrne, 2025, p. 71). Faculty development must begin now, and it must be ongoing. Consider attending the 2025 NLN Education Summit in Orlando, Florida, September 17 to 19: “The Challenge: Unraveling the Mysteries of AI.” Matthew Byrne, editor of NEP’s Emerging Technologies Center, will speak at the National Faculty Meeting on the “The AI Revolution in Nursing Education.” Additionally, Dr. Byrne and Dr. Jennie De Gagne will co-edit a special issue of NEP in 2027 with the theme “Artificial Intelligence in Nursing Education” (neponline.net). The possibilities of how we use AI in research are endless, along with its ability to perform repetitive tasks that may free the educator/researcher to focus on the application of evidence. But we must not lose sight of its limitations. Rather, consider AI an evolving partner for conversation. Experiment with AI tools and engage with them dialogically, and/or consider collaborating with AI specialists in the design of robust studies. It is our core responsibility to review the output of AI with a critical lens without assuming there are no errors or biases. AI requires human researchers to verify the output. It cannot replace researchers — their creativity, innovation, critical thinking, and insights. It is a tool to aid us in our research journey. Continue to explore, learn, and determine how AI can help you transform the science of nursing education!

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