The ChatGPT Effect: Nursing Education and Generative Artificial Intelligence

Nurse Educators' Perceptions of the Use of Artificial Intelligence: A Qualitative Study

BACKGROUND

The integration of generative artificial intelligence (GAI) into nursing education raises concerns owing to nursing's strong emphasis on human-centered care. This study explored novice nurse educators' perceptions of GAI in nursing education, examining the challenges, opportunities, and factors influencing their decisions regarding their use, as well as their vision for GAI's future role.

METHOD

A descriptive qualitative study involving 17 nursing educators from various institutions was conducted using snowball sampling. Semistructured interviews conducted face-to-face and via Zoom were analyzed thematically using NVivo14 software.

RESULTS

Four themes emerged: (1) limited engagement and understanding of GAI; (2) challenges and skepticism; (3) readiness for GAI use; and (4) recommendations for improving GAI integration. Participants expressed mixed perceptions of the effects of GAI on nursing education.

CONCLUSION

Despite skepticism, nurse educators have recognized the potential of GAI. Educational institutions must raise awareness of the benefits of GAI, provide targeted training, and develop infrastructure to support its adoption. [J Nurs Educ. 2025;64(6):339-345.].

The Effect of a Generative AI-Based Teaching Strategy on Building Students' Competency

BACKGROUND

Assessment of the initial medical history data for pregnant women is an essential component of nursing training. Therefore, understanding clinical patient characteristics is crucial for developing students' ability to independently manage and care for pregnant women and to prepare students for entry into clinical practice.

METHOD

This study used a quasiexperimental pre- and posttest design. The generative artificial intelligence-based patient character creation (GAI-PCC) teaching strategy was used for the experimental group, and the conventional creating personas teaching strategy was used for the control group.

RESULTS

The findings indicated the GAI-PCC teaching strategy significantly improved students' training. Therefore, it is recommended that this teaching strategy be implemented as an innovative scheme in nursing.

CONCLUSION

The GAI-PCC teaching strategy has a strong influence on nursing students' maternal care education. Nursing scholars should consciously integrate appropriate generative AI technology and teaching strategies to enhance students' patient care abilities. [J Nurs Educ. 2025;64(6):346-355.].

The impact of digital competence on pedagogical innovation among nurse educators: The moderating role of artificial intelligence readiness

AIM

To investigate the relationships between digital competence, AI readiness and pedagogical innovation among nurse educators, with a specific focus on the moderating role of AI readiness.

BACKGROUND

Digital competence is vital for nurse educators, supporting technology integration and promoting pedagogical innovation. AI readiness further enhances this innovation, fostering dynamic learning environments. However, research on how digital competence and AI readiness together have an impact on pedagogical innovation among nurse educators remains limited.

DESIGN

Cross-sectional study.

METHODS

Data were collected from 600 nurse educators across various nursing faculties in Egypt. Validated scales measured digital competence, AI readiness and pedagogical innovation. Pearson correlation, multiple regression and moderation analyses were used to test study hypotheses.

RESULTS

Significant positive correlations were found between pedagogical innovation, digital competence (r = 0.546, p < 0.01) and AI readiness (r = 0.530, p < 0.01). Digital competence (B = 0.558, p < 0.001) and AI readiness (B = 0.580, p < 0.001) significantly predicted pedagogical innovation. AI readiness moderated this relationship (B = 0.199, p < 0.001, ΔR² = 0.0057), amplifying the effect at higher levels of AI readiness (B = 0.66, p < 0.001).

CONCLUSION

Digital competence and AI readiness play critical roles in promoting pedagogical innovation. Strengthening AI readiness through targeted training can enhance digital tools adoption in nursing education. It is crucial to revise academic standards for curricula and nurse educators to include AI competence, ensuring effective integration of AI and digital tools in nursing education through targeted training and infrastructure improvements.

Is use of ChatGPT cheating? Students of health professions perceptions

PURPOSE

The purpose of this study is to explore student perceptions of generative AI use and cheating in health professions education. The authors sought to understand how students believe generative AI is acceptable to use in coursework.

MATERIALS AND METHODS

Five faculty members surveyed students across health professions graduate programs using an updated, validated survey instrument. Students anonymously completed the survey online, which took 10-20 min. Data were then tabulated and reported in aggregate form.

RESULTS

Nearly 400 students from twelve academic programs including health and rehabilitation science, occupational therapy, physical therapy, physician assistant studies, speech-language pathology, health administration and health informatics, undergraduate healthcare studies, nurse anesthesiology, and cardiovascular perfusion. The majority of students identify the threat of generative AI to graded assignments such as tests and papers, but many believe it is acceptable to use these tools to learn and study outside of graded assignments.

CONCLUSIONS

Generative AI tools provide new options for students to study and learn. Graduate students in the health professions are currently using generative AI applications but are not universally aware or in agreement of how its use threatens academic integrity. Faculty should provide specific guidance on how generative AI applications may be used.

Prompts, privacy, and personalized learning: integrating AI into nursing education-a qualitative study

BACKGROUND

Generative artificial intelligence (GenAI) has emerged as a powerful tool in nursing education, offering novel ways to enhance clinical reasoning, critical thinking, and personalized learning. However, questions remain regarding the ethical use of AI-generated outputs, data privacy concerns, and limitations in recognizing emotional nuances.

OBJECTIVE

This study aims to explore how nursing students utilize GenAI tools to develop care plans, with a particular focus on the innovative role of prompt engineering. By identifying both challenges and opportunities, it seeks to provide actionable insights into seamlessly integrating GenAI into nursing education while safeguarding humanistic nursing skills.

METHODS

A qualitative design was adopted, involving semi-structured interviews with third-year undergraduate nursing students at a single institution. Participants worked with anonymized clinical cases and multiple GenAI tools, emphasizing the iterative design of prompts to optimize care-plan outputs. Data were analyzed thematically to capture detailed perspectives on AI-facilitated learning and ethical considerations.

RESULTS

Findings indicate that GenAI tools enhanced efficiency and conceptual clarity, allowing students to focus more on higher-order clinical thinking. Prompt engineering significantly improved the accuracy and contextual relevance of AI-generated care plans. However, students expressed concerns about incomplete or imprecise responses, GenAI's limited emotional understanding, and privacy risks associated with sensitive healthcare data. When used with careful prompt refinement and critical evaluation, GenAI was viewed as a valuable supplement rather than a replacement for humanistic nursing competencies.

CONCLUSION

This study highlights the transformative potential of GenAI in nursing education, underscoring the importance of structured prompt engineering and ethical safeguards. By balancing technological innovation with empathy, communication, and cultural sensitivity, nursing educators can harness AI to deepen clinical reasoning and prepare students for future AI-enhanced practice. Further research across diverse settings is needed to validate these findings and refine best practices for integrating GenAI into nursing curricula.

CLINICAL TRIAL NUMBER

Not applicable. This study did not involve a clinical trial.

The impact of ChatGPT on nursing education: A qualitative study based on the experiences of faculty members

BACKGROUND

Recent advancements in artificial intelligence technologies particularly ChatGPT have supported learning and fostered critical thinking in nursing education. However, integrating these tools into academic settings requires both ethical and strategic planning.

AIM

This study aims to explore the perspectives of nursing faculty members regarding the integration of ChatGPT into nursing education.

METHODS

This descriptive phenomenological study was conducted with 14 nursing faculty members. Data were collected using a socio-demographic information form and a semi-structured interview guide and analyzed using thematic analysis methods. The COREQ checklist was followed.

RESULTS

The average age of the nursing educators was 38.46 ± 5.23 years, and their average professional experience was 14.21 ± 3.35 years. Six main themes and twelve sub-themes were identified from the interviews. The main themes included information accuracy and reliability, contributions to educational processes, ease of use and challenges, development of professional knowledge and skills, integration into nursing practices, and the long-term impacts on nursing education.

CONCLUSION

ChatGPT has the potential to enhance nursing education by supporting theoretical learning, improving efficiency, and fostering personalized learning experiences. To successfully integrate ChatGPT into nursing curricula, educational institutions should implement specific strategies, such as faculty training programs and AI literacy initiatives, to equip educators and students with the necessary skills to use AI tools effectively.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Educational institutions should develop clear policies for the ethical and strategic integration of AI tools like ChatGPT. Future research should focus on specific methodologies, such as longitudinal studies that assess the impact of ChatGPT on student learning outcomes, as well as its long-term effects on nursing practice and healthcare services.

Exploring artificial intelligence literacy and the use of ChatGPT and copilot in instruction on nursing academic report writing

BACKGROUND

Nursing education increasingly emphasizes academic writing and communication, critical for delivering quality patient care and professional advancement. Rapidly emerging artificial intelligence (AI) tools such as ChatGPT and Copilot are transforming educational methodologies, and a focus is being placed on embedding AI literacy to effectively bridge the gap between theoretical knowledge and clinical practice. These technologies have the potential to reshape nursing education in a technology-driven health-care landscape.

AIM

This study investigated the effectiveness of AI literacy and the application of ChatGPT and Copilot in academic nursing report writing. It assessed the level of AI literacy of nursing students, examined the integration of basic AI concepts into a curriculum, and analyzed the impact of these tools compared with traditional teaching methods.

METHODS

The study adopted a sample of 203 senior nursing students from Southern Taiwan to compare an AI-enhanced teaching approach using ChatGPT and Copilot with conventional methods. The curriculum, centered on the "Writing Case Reports and Seminars" course, employed the Analyze, Design, Develop, Implement, Evaluate model and incorporated scaffolding techniques to synergistically integrate clinical skills with academic learning. AI literacy was measured using the Meta AI Literacy Scale (MAILS). Summative assessments, adhering to the Taiwan Nursing Association standards, focused on individual and group case report evaluations.

FINDINGS

Following an 18-week AI intervention, the experimental group demonstrated significant improvements in all dimensions of the MAILS. A ChatGPT usage of 100 % was found, with a notable enhancement discovered in the "Nursing Plan" section of case reports. Although the experimental group outperformed the control group in overall case report evaluations, the connections between identified problems and proposed plans were weaker and nursing interventions tended to be less individualized for the experimental group.

CONCLUSIONS

The incorporation of AI tools such as ChatGPT and Copilot into a scaffolding teaching framework significantly boosted students' AI literacy and performance in summative assessments. Effective AI training for students, supervised use of these tools, and continuous professional development for educators are paramount to successful implementation. Addressing the current limitations of AI has the potential to further improve academic writing, foster critical thinking, and ensure responsible application in patient care, ultimately leading to higher-quality and more effective nursing education.

How Chatbots Respond to NCLEX-RN Practice Questions: Assessment of Google Gemini, GPT-3.5, and GPT-4

ABSTRACT

ChatGPT often "hallucinates" or misleads, underscoring the need for formal validation at the professional level for reliable use in nursing education. We evaluated two free chatbots (Google Gemini and GPT-3.5) and a commercial version (GPT-4) on 250 standardized questions from a simulated nursing licensure exam, which closely matches the content and complexity of the actual exam. Gemini achieved 73.2 percent (183/250), GPT-3.5 achieved 72 percent (180/250), and GPT-4 reached a notably higher performance with 92.4 percent (231/250). GPT-4 exhibited its highest error rate (13.3%) in the psychosocial integrity category.

The role of artificial intelligence in shaping nursing education: A comprehensive systematic review

AIM

This systematic review assesses AI's application, effectiveness and impact on nursing education, while identifying research limitations.

BACKGROUND

AI integration in nursing education is transforming traditional teaching and learning paradigms.

DESIGN

A systematic review.

METHODS

Following PRISMA 2020 guidelines, a search was conducted in PubMed, Web of Science, Embase, Cochrane Library and CINAHL from the inception of the databases to November 1, 2024, focusing on "Artificial Intelligence" and "nursing education." Two reviewers independently screened and assessed the literature. The quality was assessed using the Cochrane Risk of Bias 2.0 (RoB-2) tool for randomized controlled trials (RCTs), the Agency for Healthcare Research and Quality (AHRQ) tools evaluation for observational studies and the JBI Critical Appraisal Checklist for quasi-experimental studies.

RESULTS

Fifteen studies involving 1464 nursing students and professionals were included. The application scenarios of AI technology in nursing education are diverse and varied and it has shown significant potential in many areas of nursing education, but conflicting results have also been observed. Evaluation of literature quality showed that there were seven high-quality studies and eight medium-quality studies. Artificial intelligence was found to have a positive impact on students at three levels: learning attitude and psychological effects, learning effectiveness and comprehensive clinical nursing competencies. Key research gaps were identified, including the lack of longitudinal studies, uneven study populations and the lack of measurement instrument validity and objectivity.

CONCLUSION

AI positively impacts nursing education but requires further research to address gaps and ensure long-term effectiveness and privacy protection.

REGISTRATION

PROSPERO ID: CRD42024562849.

Nursing Students' Perceptions and Use of Generative Artificial Intelligence in Nursing Education

Background/Objectives: Artificial intelligence (AI) is transforming nursing, with generative AI (GenAI) tools such as ChatGPT offering opportunities to enhance education through personalized learning pathways. This study aimed to explore nursing students' use of generative artificial intelligence (GenAI) and their perceptions of its use in nursing education, including its advantages, disadvantages, and perceived support needs. Methods: This study employed an online survey. The participants were 99 undergraduate nursing students in New York City. Data was collected online through self-report measures using semi-structured, open-ended questions. The data was analyzed using content analysis. Results: Most participants (92%) used GenAI tools to access accurate information, clarify nursing concepts, and support clinical tasks such as diagnoses and health assessments, as well as schoolwork, grammar checks, and health promotion. They valued GenAI as a quick, accessible resource that simplified complex information and supported learning through definitions, practice questions, and writing improvements. However, the participants noted drawbacks, such as subscription costs, over-reliance, information overload, and accuracy issues, leading to trust concerns. The participants suggested financial support, early guidance, and instructional modules to better integrate AI into nursing education. Conclusions: The results indicate that GenAI positively impacts nursing education and highlight the need for guidelines on critical evaluation. To integrate GenAI effectively, educators should consider introductory sessions, support programs, and a GenAI-friendly environment, promoting responsible AI use and preparing students for its application in nursing education.

Impact of Attached File Formats on the Performance of ChatGPT-4 on the Japanese National Nursing Examination: Evaluation Study

Unlabelled

This research letter discusses the impact of different file formats on ChatGPT-4's performance on the Japanese National Nursing Examination, highlighting the need for standardized reporting protocols to enhance the integration of artificial intelligence in nursing education and practice.

Application of artificial intelligence in medical education: A meta-ethnographic synthesis

With the advancement of Artificial Intelligence (AI), it has had a profound impact on medical education. Understanding the advantages and issues of AI in medical education, providing guidance for educators, and overcoming challenges in the implementation process is particularly important.

The objective of this study is to explore the current state of AI applications in medical education.

A systematic search was conducted across databases such as PsycINFO, CINAHL, Scopus, PubMed, and Web of Science to identify relevant studies. The Critical Appraisal Skills Programme (CASP) was employed for the quality assessment of these studies, followed by thematic synthesis to analyze the themes from the included research.

Ultimately, 21 studies were identified, establishing four themes: (1) Shaping the Future: Current Trends in AI within Medical Education; (2) Advancing Medical Instruction: The Transformative Power of AI; (3) Navigating the Ethical Landscape of AI in Medical Education; (4) Fostering Synergy: Integrating Artificial Intelligence in Medical Curriculum.

Artificial intelligence's role in medical education, while not yet extensive, is impactful and promising. Despite challenges, including ethical concerns over privacy, responsibility, and humanistic care, future efforts should focus on integrating AI through targeted courses to improve educational quality.

Increasing Generative Artificial Intelligence Competency among Students Enrolled in Doctoral Nursing Research Coursework

BACKGROUND

 Generative artificial intelligence (AI) tools may soon be integrated into health care practice and research. Nurses in leadership roles, many of whom are doctorally prepared, will need to determine whether and how to integrate them in a safe and useful way.

OBJECTIVE

 This study aimed to develop and evaluate a brief intervention to increase PhD nursing students' knowledge of appropriate applications for using generative AI tools in health care.

METHODS

 We created didactic lectures and laboratory-based activities to introduce generative AI to students enrolled in a nursing PhD data science and visualization course. Students were provided with a subscription to Chat Generative Pretrained Transformer (ChatGPT) 4.0, a general-purpose generative AI tool, for use in and outside the class. During the didactic portion, we described generative AI and its current and potential future applications in health care, including examples of appropriate and inappropriate applications. In the laboratory sessions, students were given three tasks representing different use cases of generative AI in health care practice and research (clinical decision support, patient decision support, and scientific communication) and asked to engage with ChatGPT on each. Students (n = 10) independently wrote a brief reflection for each task evaluating safety (accuracy, hallucinations) and usability (ease of use, usefulness, and intention to use in the future). Reflections were analyzed using directed content analysis.

RESULTS

 Students were able to identify the strengths and limitations of ChatGPT in completing all three tasks and developed opinions on whether they would feel comfortable using ChatGPT for similar tasks in the future. All of them reported increasing their self-rated competency in generative AI by one to two points on a five-point rating scale.

CONCLUSION

 This brief educational intervention supported doctoral nursing students in understanding the appropriate uses of ChatGPT, which may support their ability to appraise and use these tools in their future work.

An example of leveraging AI for documentation: ChatGPT-generated nursing care plan for an older adult with lung cancer

OBJECTIVE

Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer.

METHOD

This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT.

RESULTS

While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs.

CONCLUSION

Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

Training and Technology Acceptance of ChatGPT in University Students of Social Sciences: A Netcoincidental Analysis

This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this artificial intelligence tool. The analysis considers variables such as gender, academic year, prior experience with ChatGPT, and the training provided by university faculty. The networks created with the statistical tool "CARING" highlight the role of perceived utility, credibility, and prior experience in shaping attitudes and behaviors toward this emerging technology. Previous experience, familiarity with video games, and programming knowledge were related to more favorable attitudes towards ChatGPT. Students who received specific training showed lower confidence in the tool. These findings underscore the importance of implementing training strategies that raise awareness among students about both the potential strengths and weaknesses of artificial intelligence in educational contexts.

Collaborative project-based learning in global health: Enhancing competencies and skills for undergraduate nursing students

BACKGROUND

Despite the importance of collaboration and communication in global health, existing educational approaches often rely on traditional one-way instruction from instructor to student. Therefore, this study aimed to evaluate the effectiveness of a newly developed undergraduate curriculum on global health in enhancing nursing students' competencies in global health and communication, problem-solving, and self-directed learning skills.

METHODS

A 15-week course "Global Health and Nursing" was designed for undergraduate nursing students, and a collaborative project-based learning method was used. Study participants were undergraduate nursing students enrolled in the course. The study was a multi-method study and included quantitative and qualitative components. It employed a one-group pretest-posttest design to quantitatively assess the impact of the curriculum. Additionally, student experiences with the learning process were qualitatively explored through a focus group interview. A total of 28 students participated in this study, and 5 of them participated in the focus group interview.

RESULTS

The collaborative project-based learning method significantly improved global health competency (t = - 10.646, df = 22, p < 0.001), with a large effect size. It also improved communication skills (t = - 2.649, df = 22, p = 0.015), problem-solving skills (t = - 3.453, df = 22, p = 0.002), and self-directed learning skills (t = - 2.375, df = 22, p = 0.027). Three themes were found through the focus group interview: (a) Promoting global health competency; (b) Fostering life skills through collaborative projects; and (c) Recommendations for future classes. The focus group interview indicated that overall, the study participants were satisfied with the collaborative project-based method for global health education.

CONCLUSIONS

This study confirms that project-based learning significantly boosts the competencies and skills of students, recommending its broader adoption in nursing education. Nursing instructors should consider adopting this teaching approach for global health education at the undergraduate level. Future studies may employ a longitudinal design to assess the prolonged effects of the collaborative project-based learning approach, particularly focusing on the long-term retention of skills and the broader applicability of this model across different educational settings.