Abstract
Background/objective:
Generative artificial intelligence is profoundly transforming the field of nursing. Nursing education needs to make corresponding progress to cultivate nursing personnel who can adapt to the technological environment. Conduct a scoping review on the application of generative artificial intelligence in cultivating research literacy among nursing graduate students to provide a reference for future paradigm shifts in graduate education.
Method:
Following the methodological framework of scoping reviews, relevant studies were systematically retrieved from Chinese and English databases. The search period spanned from the inception of the databases to January 10, 2026. Two researchers independently screened and extracted data, and summarized and analyzed the included literature.
Results:
A total of 12 articles were included. The application of Gen AI in nursing graduate research literacy training primarily encompasses paper writing and revision, enhancing innovative and critical thinking skills, and improving learning and research efficiency. Nevertheless, caution is still required regarding information accuracy and ethical safety.
Conclusion:
Gen AI may play a positive role in cultivating research literacy among nursing graduate students, but corresponding research is still in its early stages. Future research should strengthen experimental studies, provide empirical research containing data, actively integrate cutting-edge technologies, promote their in-depth application in this field, while ensuring the safety and effectiveness of the technology, thereby effectively promoting innovation and development in nursing.
1 Introduction
Artificial Intelligence (AI) is a comprehensive term denoting the technology that empowers computers to mimic human cognitive capabilities, such as learning and reasoning (Kleib et al., 2024; von Gerich et al., 2022). Generative Artificial Intelligence (Gen AI) is an artificial intelligence system that can generate innovative content via fundamental models, encompassing but not restricted to Large Language Models (LLMs), Text to Image Models (e.g., Chat GPT, Claude), and Multimodal Models, among others. It is significantly transforming the domains of scientific research and healthcare, offering convenience to individuals (Li et al., 2025). Therefore, nursing education needs to make corresponding progress to cultivate nursing personnel who can adapt to the technological environment (Han et al., 2025; Simms, 2025). Research literacy pertains to the capability of researchers to acquire, interpret, evaluate, and utilize research information within the realm of academic research, underscoring the integration of fundamental theories and practical applications. It encompasses four core competency domains: (1) methodological reasoning (comprehending research design and analytical methodologies); (2) epistemological awareness (critically appraising knowledge assertions and validity); (3) academic communication (clarifying research findings and processes); (4) ethical discernment (guiding integrity, bias mitigation, and responsible conduct) (Xu and Yang, 2021). As high - level reserve talents (Zhang et al., 2024), nursing postgraduate students are required to concentrate on the enhancement of their research literacy during their educational process. Critical thinking skills are beneficial for identifying the deficiencies within the discipline and elevating the educational quality (Zhu et al., 2025). Nevertheless, the innovative education and training for nursing postgraduate students in China are still in the nascent stage, and the research delving into their innovative behaviors and disciplinary capabilities is relatively scarce (Shi et al., 2022b). The integration of nursing education with Gen AI tools has the potential to identify cross - study design patterns, explore theoretical frameworks, contribute to the enhancement of paper frameworks and argument structures, and assist users in detecting potential ethical biases in research. This integration holds promise for enhancing the research literacy of nursing graduate students. In the long term, it may contribute to the improvement of the overall quality of nursing care. Consequently, grounded in the scoping review framework proposed by Arksey and Malley (2005) and adhering to the PRISMA ScR best-practice reporting standard, this study undertakes a comprehensive review of the application of Gen AI in the cultivation of research literacy among nursing graduate students. The objective is to attain a comprehensive comprehension of the current research status and developmental trends of Gen AI in this domain, thereby offering a foundation for nursing researchers to conduct relevant studies.
2 Materials and methods
2.1 Definition of core concepts
Research literacy: The ability of researchers to acquire, interpret, evaluate, and utilize research information in academic research, emphasizing the ability to understand, evaluate, and apply existing results. It is a specific application of critical thinking in scientific research that affects academic writing, but focuses on input and processing, with the core being interpretation rather than creation.
Research ability: The ability to directly engage in research and generate new knowledge, including experimental design, data analysis, paper writing, etc.
Critical thinking: It is a general thinking skill that includes analysis, reasoning, evaluation, etc., applicable to any field.
Academic writing: It focuses on output and expression, and is the process of presenting one's research findings or ideas in standardized academic language.
2.2 Protocal development and registration
Due to the exploratory nature of this scoping review and the absence of a scoping review registry at the time of study initiation, a formal protocol was not registered.
2.3 Quality assessment
According to Arksey and O'Malley, quality assessment is not a mandatory component of scoping review, as its primary purpose is to map the breadth of literature rather than evaluate the quality of evidence or overall results. Therefore, this study did not conduct a formal critical evaluation of the included studies. To partially address this issue, we extracted literature features and information to provide contextual explanations for the evidence base (as shown in Table 1).
Table 1
| Author | Country | Research method | Research tool | Research content | Research results |
|---|---|---|---|---|---|
| Zheng Xintong (Zheng et al., 2025) | China | Qualitative research; descriptive research | Large Language Model, LLM | Understand the views and experiences of master's degree students in nursing on using large language models | Large language models may assist nursing postgraduates in acquiring knowledge, refining and revising their papers, and enhancing research quality and efficiency. However, they pose risks to privacy, challenge academic integrity, and impede the improvement of innovation capabilities. |
| Liu Ting (Liu et al., 2024) | China | Qualitative research | Artificial Intelligence Generated Content, AIGC | The experience of nursing master's degree students using AIGC tools. | AIGC tools could assist in literature organization and collection, enhance the efficiency of learning and research, polish papers, improve the quality of academic writing, and promote personalized autonomous learning. However, there are concerns about the usefulness, reliability, and comprehensiveness of information, the accessibility and cost of using the tools, as well as potential security risks. |
| Hong zhan Jiang (Jiang et al., 2026) | China | Qualitative research; descriptive research | Gen AI | To comprehensively Understand nursing postgraduates’ experiences and perceptions regarding Gen AI using qualitative interviews and promote their proper application. | Gen AI have potential to enhance research efficiency and improving nursing education quality, and assisting academic writing and knowledge comprehension. However, challenges such as difficulties in evaluating generated content, technical anxiety, security concerns, ethical ambiguity, and academic integrity risks might hinder deeper adoption. |
| Malik Sallam (Sallam et al., 2023) | Jordan | Experimental research | Chat Generative Pre-trained Transformer, Chat GPT | Develop and validate TAME - Chat GPT, and assess the adoption and cognition of Chat GPT among nursing undergraduates and graduate students. | Using Chat GPT may facilitate quick access to information and enhance the efficiency of learning and research. However, excessive reliance on it may lead to a lack of independent thinking ability, hindering the development of one's own skills. |
| Rukiye Kevser Sağlam (Sağlam and Kalanlar, 2025) | Türkiye | Qualitative research; descriptive research | Chat GPT | Gain a deeper understanding of the usage experience and opinions of ChatGPT among master's and doctoral students in nursing. | Chat GPT could offer suggestions for revising and polishing papers, improve the quality of writing, and provide personalized learning methods. However, there are ethical and informational concerns, and the protection of personal data remains a matter for discussion. |
| Meghan Reading Turchioe (Reading Turchioe et al., 2024) | America | Experimental research | Chat GPT4.0 | The discussion centered on how to assist students in the doctoral nursing program in achieving a more profound understanding of Chat GPT's capabilities and advantages through concise interventions. | Chat GPT potentially offer clear and concise suggestions, enhance the understanding of academic materials, boost research efficiency, and facilitate personalized learning approaches. However, there are problems with the accuracy and precision of the information it provides. |
| Yuanyuan Luo (Luo et al., 2023) | China | descriptive research | Chat GPT | Conducted a questionnaire survey to understand the cognition and demand of nursing undergraduates and graduate students regarding Chat GPT. | By using Chat GPT, individuals may obtain personalized learning methods and improve their critical thinking and innovation abilities. |
| Ruifu Kang (Kang et al., 2025) | China | Qualitative research; descriptive research | Gen AI | Experience and views of nursing graduate students on generative artificial intelligence. | Utilizing Gen AI might enhance their scientific literacy and boost research efficiency. However, there is a risk of generating false results, and ethical concerns merit careful consideration. |
| Maggie Mee Kie Chan (Chan et al., 2023) | China | Experimental research | Chat GPT | The views and concerns of nursing graduate students regarding the use of Chat GPT. | Using Chat GPT potentially enhance critical thinking and problem - solving skills, as well as improve research efficiency. However, there is a concern about the lack of empathy, and excessive reliance by users can affect their independent thinking ability. |
| Michael D Bumbach (Bumbach et al., 2024) | America | Experimental research | Chat GPT | Integrating Chat GPT-related assignments into the classroom, understand their views of utilize Chat GPT and perceptions. | Chat GPT might enhance the efficiency of learning and research by offering personalized learning methods. |
| Suzanne BAKKEN (Bakken et al., 2024) | America | Experimental research | Chat GPT | Lessons learned from integrating Chat GPT into the curriculum of a doctoral program in nursing. | Integrating Chat GPT into the curriculum potentially offer personalized learning approaches, enhance students’ ability to analyze academic materials, and foster the development of their innovation capabilities. |
| Funda Aslan (Aslan, 2025) | Türkiye | Qualitative research; descriptive research | Chat GPT | Through the method of phenomenological qualitative research, this study investigates the experiences and perceptions of postgraduate nursing students regarding their use of Chat GPT, providing insights for nursing educators. | Chat GPT may help nursing postgraduates balancing academic and clinical responsibilities, enhance personalized and self-directed learning among nursing students. There are concerns about AI reducing critical thought and engagement. |
Basic characteristics of included literature (n = 12).
2.4 Determine the research question
The specific research questions are: ①What are the application effects of Gen AI in the cultivation of research literacy among nursing graduate students? ②What are the potential application values and limitations of Gen AI in the cultivation of research literacy among nursing graduate students?③What are the strategies to increase the application of Gen AI in the scientific research literacy of nursing graduate students?
2.5 Inclusion and exclusion criteria for literature
Inclusion criteria: ①The content involves the application of Gen AI in the cultivation of research literacy among nursing graduate students; ②The full text is accessible; ③Both Chinese and English literature are included. Exclusion criteria: ①Repeated publication, incomplete information and data; ②The full text is not accessible; ③Non-academic research articles such as social commentaries, reviews, newsletters, and opinion pieces.
2.6 Search strategy
A systematic search was carried out across databases such as CNKI, Wanfang, VIP, PubMed, Web of Science, Cochrane Library, Sinomed, CINAHL(EBSCO) and Embase, supplemented by manual searches to prevent any potential omissions. The search spanned from the establishment of the databases to January 10, 2026. Taking CNKI as an example of a Chinese database, the search query is (SU = Nursing+Nursing postgraduates+Nursing Education) AND (Generative AI + AI + ChatGPT+ChatGPT+DeepSeek) AND (Scientific Research Ability+Scientific Research Quality+Scientific Research). For the English databases, taking PubMed as an example, the search query was (((((((nursing[Title/Abstract]) OR (nursing postgraduates[Title/Abstract])) OR (nursing education[Title/Abstract])) AND (scientific literacy[Title/Abstract])) AND (artificial intelligence[Title/Abstract])) OR (Chat gpt[Title/Abstract])) OR (Deepseek[Title/Abstract])) OR (generative artificial intelligence[Title/Abstract])OR (chat gpt[Title/Abstract])
2.7 Literature screening and data extraction
The research selection and data visualization both follow the iterative approach of the Arksey and O'Malley frameworks. In the research selection stage, two researchers conduct screening independently. If ambiguous situations are encountered during the screening process, the team will hold a meeting to clarify and refine the inclusion criteria. If necessary, they will re - examine the previously screened literature and continuously calibrate the screening criteria through consensus. The data extraction work was carried out independently by two researchers. The data visualization adopts an iterative design: First, a preliminary data extraction table is created based on the research problem. Before formal extraction, the team randomly selects 5 included articles for pre - testing to evaluate the applicability of the table. During this process, the table is repeatedly discussed and revised based on the pre - experimental results. The final version of the data extraction table is used for the final data extraction of all literature only after reaching a consensus within the team. The content extracted from the literature includes: author, country, research methods, research tools, research content, and research results.
3 Results
3.1 Literature screening results
A preliminary search yielded 10,659 articles. After removing duplicates, 10,527 articles remained. After reading the titles and abstracts, 10,422 articles were eliminated. After reading the full texts, 88 irrelevant articles were excluded, and 5 articles for which the full texts were unavailable were also excluded. After layered screening, 12 articles were finally included (Aslan, 2025; Bakken et al., 2024; Bumbach et al., 2024; Chan et al., 2023; Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Luo et al., 2023; Reading Turchioe et al., 2024; Sağlam and Kalanlar, 2025; Sallam et al., 2023; Zheng et al., 2025). The literature screening process is shown in Figure 1.
Figure 1
3.2 Incorporate the basic characteristics of literature
Among the 12 articles (Aslan, 2025; Bakken et al., 2024; Bumbach et al., 2024; Chan et al., 2023; Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Luo et al., 2023; Reading Turchioe et al., 2024; Sağlam and Kalanlar, 2025; Sallam et al., 2023; Zheng et al., 2025), 2 are in Chinese (Liu et al., 2024; Zheng et al., 2025) and 10 are in English (Aslan, 2025; Bakken et al., 2024; Bumbach et al., 2024; Chan et al., 2023; Jiang et al., 2026; Kang et al., 2025; Luo et al., 2023; Reading Turchioe et al., 2024; Sağlam and Kalanlar, 2025; Sallam et al., 2023). China has the highest number of studies, with a total of 6 (Chan et al., 2023; Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Luo et al., 2023; Zheng et al., 2025)(50%), followed by the United States with 3 (Bumbach et al., 2024; Reading Turchioe et al., 2024), (Bakken et al., 2024) (25%), Türkiye with 2 (Aslan, 2025; Sağlam and Kalanlar, 2025)(16.7%), and Jordan with 1 (Sallam et al., 2023) (8.3%). The basic characteristics of the included articles are shown in Table 1.
3.3 The current application status and effectiveness of Gen AI in the cultivation of research literacy among nursing postgraduates
The included literatures demonstrate the effectiveness of Gen AI in cultivating research literacy among nursing postgraduates by examining its current application. Gen AI tools may not only check for errors like grammar and spelling in papers but also refine writing structures and enhance the quality of paper writing (Liu et al., 2024; Sağlam and Kalanlar, 2025; Zheng et al., 2025). By utilizing Gen AI tools, the critical thinking ability and innovation consciousness of nursing postgraduates might be improved, enriching their research ideas and approaches (Bakken et al., 2024; Chan et al., 2023; Luo et al., 2023). Moreover, it have potential to privide timely feedback, personalized learning methods, analyse academic materials and boost research efficiency (Aslan, 2025; Bumbach et al., 2024; Chan et al., 2023; Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Reading Turchioe et al., 2024; Zheng et al., 2025).
The included 5 experimental studies (Bakken et al., 2024; Bumbach et al., 2024; Chan et al., 2023; Reading Turchioe et al., 2024; Sallam et al., 2023), 6 qualitative studies (Aslan, 2025; Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Sağlam and Kalanlar, 2025; Zheng et al., 2025), and 1 descriptive study (Luo et al., 2023) all support that Gen AI can improve research efficiency. However, regarding the impact of Gen AI on critical thinking among nursing graduate students, the existing literature presents methodological differences. Experimental studies (n = 3) often report positive effects after short - term interventions. For example, Chan et al. (2023) and Luo et al. (2023) found that Chat GPT might enhance problem-solving skills and improve innovation ability. Qualitative research (n = 2) revealed potential cognitive risks of long-term use. Aslan’s (2025) interview data showed that participants were concerned that Gen AI tools may reduce critical thinking abilities, while Sallam et al. (2023) also pointed out that excessive dependence may lead to a decline in independent thinking abilities. This divergence suggests that short - term effect measurements in experimental studies may not capture the formation process of long - term cognitive dependence.
In addition, there are differences in the nature of academic integrity risks. Zheng et al. (2025), a Chinese scholar, regards the use of LLMs as a “fundamental challenge” to academic integrity, while Sağlam and Kalanlar (2025), a Türkiye scholar, regards it as an “ethical concern to be discussed” and a manageable problem. This may reflect differences in the strictness of academic norms in different countries, rather than objective differences in the risk itself.
3.4 Evaluation of empirical support strength
There is a highly consistent conclusion in existing research regarding the impact of Gen AI on learning efficiency. All 12 studies (covering multiple methodologies) indicate that Gen AI can improve learning efficiency, with strong evidence. However, this conclusion is mainly based on learners’ subjective perception and lacks objective indicators to support it. The actual effectiveness still needs further verification. In terms of academic integrity risks, over half (7/12) of the qualitative studies identified potential integrity issues that may arise from the use of Gen AI. There is a certain level of consensus on the evidence base, and the strength rating is moderate. However, existing evidence mostly remains at the level of phenomenon description, lacking quantitative assessment of the frequency and severity of risk occurrence, which limits the generalizability of this conclusion. There is contradictory evidence regarding the promoting effect of Gen AI on critical thinking. Three experimental studies support its positive role, while two qualitative studies propose the opposite view. Methodological differences make it difficult to integrate conclusions, and the overall evidence strength is weak. This indicates that the field is still in the exploratory stage and requires more rigorous experimental design to clarify controversies. Finally, in terms of academic writing quality, although six qualitative studies support the improvement effect of Gen AI, none of the studies have established a control group and they lack objective measurement indicators. This results in a weak evidence base and limited reliability and generalizability of the conclusions.
Therefore, current research on the effectiveness of Gen AI education applications presents a characteristic of “perceptual convergence and efficacy divergence”: evidence is relatively concentrated in subjective dimensions such as efficiency and experience, while there are methodological limitations and inconsistent conclusions in objective dimensions. Future research needs to strengthen experimental design and the introduction of objective indicators to enhance the integrity and persuasiveness of the evidence chain.
4 Discuss
4.1 The development potential of Gen AI tools in nursing graduate education is enormous, but there are regional differences in the application process
With the increasing complexity of the healthcare system, nursing graduate education is facing the challenge of deep integration of theory and clinical practice (Montejo et al., 2024). The rise of generative artificial intelligence has provided new possibilities for this challenge, among which Chat GPT is currently one of the most widely used technologies (Shi et al., 2022a). Compared to traditional classrooms, Gen AI has potential to guide research papers and improve research efficiency (Bakken et al., 2024; Bumbach et al., 2024; Jiang et al., 2026). It has been widely applied in clinical simulation case generation, literature analysis, and paper polishing, reshaping the learning paradigm of nursing graduate students. From the perspective of research distribution, the number of publications by Chinese scholars in this field ranks first in the world (Chan et al., 2023; Kang et al., 2025; Liu et al., 2024; Luo et al., 2023; Zheng et al., 2025), reflecting the high importance attached by the academic community and the continuous growth of related explorations; The number of publications in the United States is relatively high (Bakken et al., 2024; Bumbach et al., 2024; Reading Turchioe et al., 2024), which is related to the fact that Gen AI technology was first developed and released in the United States.
It is worth noting that due to the early exploration stage of Gen AI technology in nursing graduate education, there are significant regional differences in the implementation process of this technology. Gen AI should incorporate a wider range of language and cultural backgrounds, open access to the Gen AI platform to narrow the gap in resource acquisition, and ensure the applicability and fairness of using Gen AI in different environments (Boscardin et al., 2024). In addition, nursing educators need to find a balance between the global technological wave and local realities to promote the balanced development of Gen AI technology on a global scale.
4.2 Gen AI can accelerate the transformation of nursing graduate education, but it has limitations in legal, technological, ethical, and other aspects
This study indicates that the use of Gen AI tools may contribute to enhance the research efficiency of nursing graduate students (Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Reading Turchioe et al., 2024; Sallam et al., 2023), assist them in developing innovation and critical-thinking abilities (Aslan, 2025; Bakken et al., 2024; Bumbach et al., 2024; Chan et al., 2023; Luo et al., 2023), facilitate paper writing, and conduct paper refinement (Sağlam and Kalanlar, 2025; Zheng et al., 2025). Gen AI tools are accelerating the transformation of nursing graduate education models.
However, there are also a series of limitations, which was divided into two parts, “Empirically Reported Challenges” and “Conceptually Anticipated Risks”. The inclusion of research reports indicates the existence of academic integrity and ethical risks (Zheng et al., 2025), which might hinder the improvement of innovation and independent-thinking abilities (Aslan, 2025; Chan et al., 2023; Sallam et al., 2023). Moreover, the quality and accuracy of the generated information are questionable (Jiang et al., 2026; Kang et al., 2025; Liu et al., 2024; Reading Turchioe et al., 2024), and it may pose a threat to users’ privacy and data security (Sağlam and Kalanlar, 2025). After extensive literature review and analysis, it is worth noting the technological acquisition and cost barriers of Gen AI. Users may feel anxious due to unfamiliarity with this technology, and paying for some advanced features may increase their financial burden. Moreover, not all users have equal access to and use these tools (Schneider and Agus, 2021). Gen AI may lack humanistic care and emotional interaction, and cannot provide emotional support like human teachers (Ong, 2021).
These shortcomings indicate that although Gen AI may have significant advantages in accelerating the transformation of nursing graduate education, it is necessary to establish sound usage norms and mechanisms in practical applications and expand channels for technology acquisition. Based on this, to more safely and effectively integrate artificial intelligence technology into nursing education, it is necessary to address the challenges posed by data privacy, legal, and ethical issues (Jeyaraman et al., 2023; Zhou et al., 2025). In the future, it is essential to strengthen the supervision of the development and application of Gen AI technology, improve the accuracy of the generated content, refine the review mechanism, clarify ethical responsibilities, safeguard data privacy and technical security, and promote the further innovative integration of Gen AI technology and nursing education (Wu et al., 2024).
4.3 Strategies and suggestions for strengthening the application of Gen AI in the cultivation of scientific research literacy among nursing postgraduates
4.3.1 Bridge the evidence gap, strengthen quantitative research on academic integrity risks, and enhance cross-border collaborative governance
Given that the seven studies currently identifying academic integrity risks are qualitative interviews, future research should introduce large-scale surveys or behavioral experiments to quantify the frequency and specific manifestations of this risk and provide empirical evidence for intervention measures. In the process of introducing investigations, professional institutions should strengthen supervision and management of the application process of Gen AI, ensure the adoption of ethical Gen AI technology, promote standardized functions and best practices, and align them with the goals of nursing education and research. Since research results in this field are mainly distributed in China (50%) and the United States (25%), international cooperation and exchange should be strengthened to further promote fairness and universality in the application of Gen AI (Jia and Zhao, 2025).
4.3.2 Conduct reflective dialogue from a critical thinking perspective, standardize the application of Gen AI, and provide boundary conditions for its application
Due to methodological differences (experimental vs. qualitative) in existing research on critical thinking, future research needs to design long - term follow - up quasi-experimental studies, strengthen research and discussion on ethical issues related to artificial intelligence, and verify whether Gen AI plays an “auxiliary” or “alternative” role in different disciplinary backgrounds. This will provide boundary conditions for teaching applications and indirectly improve students’ ethical awareness and sense of responsibility (Chauhan and Currie, 2024). Three qualitative studies suggest that long - term use of Gen AI can hinder the improvement of students’ critical thinking. Therefore, educators should guide students to use Gen AI tools correctly and reduce their excessive dependence on Gen AI.
4.3.3 Nursing educators conduct curriculum restructuring and innovation, actively developing objective evaluation tools
The 12 included studies all agree that Gen AI can improve research efficiency with high consistency. However, these studies mainly rely on subjective feelings and lack more objective measurement methods. Nursing workers can re-evaluate their teaching methods and courses based on their teaching experience and student feedback. They should actively develop standardized objective testing tools and deeply explore the role of Gen AI in improving students’ research efficiency. In addition, nursing educators must constantly update their knowledge, integrate the concept of Gen AI into teaching, cultivate critical thinking about the application of Gen AI in the healthcare field, and guide students to use Gen AI tools correctly.
5 Summary
This research, grounded in Arksey's theoretical framework for scoping reviews, comprehensively summarizes and analyzes the application of Gen AI in the cultivation of research literacy among nursing postgraduate students both domestically and internationally. It conducts a systematic review from the perspectives of research methodologies, tools, content, and outcomes, with the aim of providing a theoretical basis for related studies. Nevertheless, certain limitations still exist. Firstly, although detailed internal protocols were established in advance, formal registration was not carried out because of the exploratory nature of the research and the limited registration options available for scope definition review at that time. Secondly, it did not formally evaluate the quality of the literature, and this aspect needs to be improved in the future. It is recommended to adopt a combination of qualitative and quantitative research methods, conduct high - quality experimental research, develop specific evaluation tools, and scientifically evaluate the application effects. Thirdly, the limited number of included studies may not fully represent the breadth of research in this emerging field. Moreover, the fact that the included literature is mostly descriptive may, to some extent, constrain causal reasoning and evidence strength. The significant heterogeneity in the design of included studies may limit the comparability between studies. Finally, most of the included studies were conducted in China and the United States, which limits the promotion of Gen AI in different healthcare systems and cultural backgrounds. Gen AI presents promising prospects within this domain, yet it also exhibits certain limitations. At present, the research in this area is in its nascent stage, characterized by a dearth of diverse empirical investigations and imperfect legal and regulatory frameworks. Future research endeavors should strike a balance between the advantages and limitations of Gen AI, conduct in-depth exploration of its practical application effects, fully leverage the supportive function of Gen AI in cultivating research literacy among nursing graduate students, and thereby promote the development of this field.
Statements
Author contributions
J-JB: Investigation, Visualization, Writing – original draft. HQ: Visualization, Investigation, Writing – original draft. N-NW: Supervision, Writing – review & editing, Validation. H-MG: Writing – review & editing, Supervision, Validation. ST: Writing – review & editing, Supervision, Conceptualization.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was funded by the Teaching Reform and Innovation Project of Higher Education Institutions in Shanxi Province in 2024, grant number J20240646, supported by the Department of Education of Shanxi Province, China.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
generative artificial intelligence, nursing education, nursing research, research literacy, scoping review
Citation
Ban J-J, Qi H, Wang N-N, Guo H-M and Tang S (2026) The application of generative artificial intelligence in the cultivation of scientific research literacy of nursing postgraduates: a scoping review. Front. Educ. 11:1789446. doi: 10.3389/feduc.2026.1789446
Received
16 January 2026
Revised
04 March 2026
Accepted
13 April 2026
Published
08 May 2026
Volume
11 - 2026
Edited by
Sergio Ruiz-Viruel, University of Malaga, Spain
Reviewed by
Ani Syafriati, Muhammadiyah University of Surakarta, Indonesia
Boshra Karem Mohamed El-Sayed, Alexandria University, Egypt
Updates
Copyright
© 2026 Ban, Qi, Wang, Guo and Tang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Shan Tang ty0916@126.com
Disclaimer
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