Experiential Use of Generative AI in a Synchronous Graduate Nursing Course

The rapid evolution of artificial intelligence (AI), particularly probabilistic models such as Generative AI (GenAI), is transforming healthcare and education worldwide through significant intellectual and practical applications. In nursing education, the integration of these technologies presents both promising opportunities and significant challenges (DeGagne, 2023a). GenAI tools, such as ChatGPT, Google Gemini, and Microsoft Copilot, can generate coherent text, analyze data, and simulate realistic clinical case scenarios (DeGagne, 2023 AUTHOR is this A or B - RY; O’Connor et al., 2023c; Topaz et al., 2025). These tools have the potential to support clinical reasoning and judgment, assist with scholarly writing, and promote evidence-based practice among nursing students (Hobensack et al., 2024; Ng et al., 2022; Shin, et al., 2023).

Nursing and other healthcare professionals are utilizing GenAI to support their writing, patient education development, and clinical decision-making skills, while also identifying limitations such as bias, misinformation, and inaccuracies (Jallad et al., 2024; Sommer et al., 2024). For GenAI integration to be beneficial, faculty development, intentional curricula inclusion, and clear ethical guidelines are necessary to ensure safe and responsible use, with an understanding of its’ legitimacy and accuracy of the content. Nurse educators should have the competence and confidence to educate students on the benefits and challenges of utilizing AI tools (Dorin & Atkinson, 2024; Rony et al, 2025).

As nurse educators prepare practitioners for complex healthcare situations, balancing GenAI integration that are aligned with the American Association of Colleges of Nursing (AACN, 2021) Essentials may enhance learning outcomes and promote digital competencies. The inclusion of GenAI in a structured educational format can positively impact student confidence and patient care (Chang & Su, 2025). Integrating GenAI can foster students’ abilities to evaluate AI outputs, address ethical concerns, and meet emerging practice expectations in rapidly evolving, technology-driven healthcare and academic settings.

Development and Implementation of an Experiential GenAI Assignment

Graduate nursing programs are designed to cultivate advanced critical thinking, leadership, and translational research skills. Building students’ technological competence and confidence in using GenAI is therefore an essential responsibility for educators. Integrating GenAI requires faculty to develop the knowledge, skills, and confidence to apply these tools effectively, while also addressing ethical and andragogical considerations. Issues related to academic integrity, bias, data privacy, and dependence on computerized outputs must be leveraged with potential benefits of increased student engagement, individualized learning, and enhanced decision-making (U.S. Department of Education, 2023).

In response to this need, the author developed the course Instructional Strategies and Technology as part of a newly approved fully online Master’s in Nursing Education program in our school of nursing (SON). The primary objective of this course was to apply diverse instructional modalities within nursing education. The course was first offered as an elective in Spring 2024. However, the author recognized that the digital technological competencies expected of students were not fully met.

An educator’s ability to use a new educational tool is closely linked to confidence in implementing it effectively for students to fully benefit from its use (Dorin & Atkinson, 2024; Rony et al, 2025). To integrate a digital competency assignment appropriately and successfully, I self-identified my own foundational AI knowledge gap. To address this erudition deficit, I undertook an intensive professional development process by attending multiple webinars, participating in a two-day AI Bootcamp, and reviewing extensive scholarly literature. This process strengthened my AI knowledge, technical skills, and confidence in applying GenAI in nursing education. Gaining insight, educational resources and pedagogical strategies helped me to develop the experiential GenAI assignment and provide a learning opportunity for students to engage in health information technology analysis, planning, implementation, and evaluation regarding a patient education.

(LEIGH: our OJIN style manual permits the use of first person when appropriate to improve flow and readability. I edited this a bit to reflect that. Thanks, Jackie)

An experiential GenAI pilot assignment was developed and integrated into the Spring 2025 course offering. This assignment enhanced course learning outcomes and strengthened students’ digital competencies. Designed for registered nurses, the assignment focused on using GenAI tools, evaluating the validity of generated information, and verifying content through reputable evidence-based nursing journals and databases. Students were required to fact-check and cross-reference AI-generated outputs to build clinical and judgment skills. Nurses should be able to assess the legitimacy and accuracy of such information as more patients are using GenAI for health information (Topaz, et al., 2025).

The GenAI assignment and its evaluation were adapted from multiple published resources and best practices (Jallad et al., 2024; O’Connor et al., 2023a; O’Connor et al., 2023b; Sommer et al., 2024). To ensure that nurses remain at the forefront of the AI era in healthcare and education, understanding and integrating GenAI into nursing curricula is imperative. As the largest segment of the healthcare workforce, nurses must be prepared to apply AI tools competently and ethically (DeGagne, 2023b; DeGagne et al., 2024; Watson, 2024). Nurse educators play an integral part in fostering the skills, knowledge, and confidence needed for students to become competent practitioners in a technologically advancing healthcare environment. AI integration can provide unique learning opportunities, promote educational efficacy, and strengthen the nurse-patient relationship to promote improved patient outcomes (DeGagne, 2023b). The Figure describes the experiential use of GenAI assignment.

Figure. Experiential Use of Generative AI (GenAI) Assignment

Methodology

Aims and Objectives of Experiential Use of GenAI Assignment
The pilot assignment objectives were to a) examine best evidence to integrate GenAI tools to assist patients/families in understanding a medical condition, treatment options and nursing care and interventions; b) examine whether GenAI tools contain errors in their outputs and biases that reflect stereotypes and inequities in society. To address these aims, students explored health literacy by using a GenAI tool of their choice to create customizable patient education materials about a self-selected health condition and its management. The materials included, but were not limited to, lifestyle modifications, diet, exercise, medications, treatments, and nursing interventions.

Design
A primary goal of the assignment was to help graduate nursing students develop technological confidence and appreciation of GenAI capabilities and limitations. Students were expected to verify the accuracy of GenAI-generated content, identify potential biases, fabricated and falsified information, and assess whether the material reflected any stereotypes or inequities. They were also required to cross-check AI outputs against credible, evidence-based nursing and appropriate databases.

A retrospective data review institutional review board (IRB) waiver was granted for this required written assignment. Students received assignment criteria at the beginning of the Spring 2025 semester and were instructed to submit this assignment via the course learning platform.

To evaluate the effectiveness of this first-time implemented assignment, data were analyzed to explore: (1) student engagement with GenAI tools; (2) perceived usefulness and usability of GenAI for patient education; and (3) evidence of competence in digital and health literacy, and clinical judgement skills.

Theoretical and Conceptual Frameworks
The design of this graduate nursing course was grounded in Knowles’ Adult Learning Theory, which emphasizes that adult learners are self-directed, internally motivated, and ready to apply new knowledge to real-life problems (Knowles, 1975, 1978). Knowles’ andragogical model frames adult learners as autonomous and problem-oriented, with prior experience serving as a valuable resource for learning (Knowles, 1975, 1978).

In alignment with this perspective, Kolb’s Experiential Learning Theory (ELT) further informed the development and implementation of the GenAI assignment. Kolb’s model theorizes that learning is a cyclical process involving concrete experience, reflective observation, abstract conceptualization, and active experimentation (Kolb, 1984). The experiential GenAI assignment was intentionally structured to provide students with a learning experience that required them to engage with AI tools, analytically reflect on outputs, integrate scholarly evidence, and apply insights to real-world nursing education and clinical practice. By combining principles of andragogy with experiential learning, this assignment aimed to promote digital literacy, decisive thinking, and ethical understanding, which are essential competencies for advanced nurse practitioners.

Sample and Setting
The course was delivered within a diverse, urban SON at a large academic institution. During the Spring 2025 semester, five graduate students self-enrolled in the synchronous elective. All participants were registered nurses with varied clinical backgrounds and professional experiences, providing a rich context to apply adult learning and experiential approaches to the integration of GenAI.

Data Collection and Analysis
Following IRB waiver approval and the submission of final course grades to the registrar, the author retrieved the submitted GenAI assignments for retrospective analysis. Qualitative data was based on the students’ experiential use of the GenAI assignment.

A thematic analysis approach was employed to examine and gain data familiarity. An inductive coding process was used to identify patterns and recurrent concepts in students’ interpretations, capturing salient features without imposing preconceived categories. Codes were iteratively refined and grouped into preliminary clusters, with attention paid to both recurring patterns and outlier perspectives. Through this process, broader themes were constructed to represent the underlying coded data meaning. Themes were then reviewed, refined, and defined to ensure internal coherence and distinctiveness. The resulting themes captured students’ perceptions of the assignment’s educational value, their engagement with GenAI tools, and their critical appraisal of AI-generated content. This systematic and interpretive process supported the development of data-driven suppositions about the assignment’s effectiveness in fostering digital literacy, ethical awareness, and evidence-based evaluation skills among graduate nursing students.

Ethical Considerations
A retrospective review of deidentified student coursework posed no greater than minimal risk. No personal identifiable or contact information was collected. All GenAI assignments had been graded, with final grades submitted to the university registrar prior to the initiation of data analysis.

Results

Participants
All participants were female registered nurses with 3-10 years of professional nursing experience (mean = 7.8 years, SD = XX). (LEIGH Can you provide standard deviations for this and any other means? Thanks! Jackie) Four participants were practicing nurse practitioners with 2-3 years’ experience. Four students were enrolled in the Doctor of Nursing Practice (DNP) program, while one student was pursuing a Master of Science in Nursing (MSN) degree with a concentration in Adult-Gerontology Primary Care Nurse Practitioner.

Experiential GenAI Assignment
All submitted assignments focused on fictitious adult patient scenarios. Four of the five students chose to develop educational materials for patients diagnosed with Type 2 Diabetes Mellitus (T2DM): one scenario described a Hispanic male, one a Hispanic female, one an Asian female, and one patient’s ethnicity was not specified. A fifth paper centered on an African American male patient diagnosed with hypertension.

Each student used a GenAI tool to generate patient education materials addressing key aspects of disease management, including diet, lifestyle modifications, exercise recommendations, and pharmacologic therapy. One student incorporated broader social determinants of health by highlighting financial stressors, language barriers, culturally influenced dietary habits, and limited access to reliable health education resources.

Table 1 provides an overview of each fictitious patient profile, the primary goal of the patient education content, and key findings regarding the accuracy and appropriateness of the GenAI-generated outputs.

Table 1. Results of Fictious Patient Education Information Obtained Using GenAI

Evaluation of GenAI Assignment
Of the five students, four used ChatGPT as their primary GenAI tool, while one student used Google Gemini. The four students who used ChatGPT reported encountering bias in the AI-generated responses, whereas the student using Google Gemini did not report similar concerns.

Overall, four students agreed that the GenAI platforms generally produced accurate information about the chosen health conditions and appropriate recommendations for patient education plans. However, the same four students noted that the GenAI tools occasionally fabricated and falsified content, produced inaccurate or misleading information, and provided outputs with incorrectly cited or unverifiable sources. Students emphasized that rigorous fact-checking, validation, and cross-referencing with credible, evidence-based resources were necessary to ensure the accuracy of the AI-generated content. This additional step effectively doubled the workload but was seen as a valuable exercise in developing digital literacy and clinical judgement skills.

Despite these challenges, all students agreed that the assignment deepened their understanding of how emerging technologies are influencing patient education and healthcare delivery. They described the activity as innovative, relevant, timely, and valuable for their future roles. Table 2 summarizes students’ evaluations of the GenAI tools and their reflections on the assignment experience.

Table 2. Student Evaluation of Generative AI Assignment

Discussion

The findings of the experiential use of GenAI in a self-enrolled graduate nursing course demonstrated that GenAI integration can support adult learners in developing digital competence, clinical reasoning and judgement skills, and confidence in navigating emerging technologies. This assignment confirmed that GenAI tools can promote student confidence and support the development of patient education materials through ethical decision-making and clinical reasoning (Chang & Su, 2025; Kim et al., 2025; Shin et al., 2023). The challenges of output accuracy, citation reliability, and embedded bias were noted ambiguities. This coincides with the broader concerns that GenAI tools may generate convincing but inaccurate or fabricated information and can unintentionally perpetuate social biases if their outputs are not thoroughly evaluated (Dorin & Atkinson, 2024; Jallad et al., 2024; Srinivasan et al., 2024).

Student evaluations of GenAI tools highlighted potential benefits and challenges when utilized for patient education. While the platforms produced generally accurate information on the medical conditions explored, students frequently encountered biased phrasing, fabricated citations, and incomplete or no citations. This reinforces the need for vigorous validation of GenAI content for accurate interpretation and clarification when applying this content in clinical and educational settings (Srinivasan et al., 2024).

The assignment design required students to be self-directed, autonomous learners, characteristics central to effective graduate level education. This assignment actively fostered analytical thinking, digital literacy, and ethical clinical judgment by requiring students to validate the AI-generated patient education information. As more patients independently use AI tools for health information, healthcare professionals should be prepared to evaluate the validity of AI-generated content and educate patients about its limitations. Developing these competencies in nursing students will ensure that these practitioners are prepared to support safe, equitable, and evidence-based care in a digital healthcare culture (DeGagne, 2023a; DeGagne et al., 2024; Dorin & Atkinson, 2024). These outcomes are vital given the expanding presence of AI in patient care.

More patients are likely to use GenAI tools for health information, so nurses should be confident in evaluating the accuracy of this information and supporting patients in its interpretation and use (Chang & Su, 2025; Martinez-Ortigosa et al., 2023; Ng et al., 2022). Participants shared how this assignment helped develop their digital literacy skills and develop a deeper appreciation for the importance of accurate evidence-based practice and patient education. Nurses must have a strong understanding of potential risks and limitations of GenAI content to ensure patient safety and uphold professional standards.

When GenAI tools are introduced without adequate training or safeguards, the risk of fabricated or falsified information, bias stereotyping, and academic integrity violations increases (DeGagne et al., 2024). While students appreciated the innovative design of this assignment, they also noted the increased workload required to verify GenAI outputs, emphasizing that these tools should augment, and not replace, nurse practitioners’ professional reasoning and judgment. The content verification process can be labor intensive yet valuable, demonstrating contradictory efficiencies of GenAI use. This necessitates that organizational leaders in the profession of nursing adopt clear frameworks, policies, and ethical standards for the inclusion of AI use in all settings (Dorin & Atkinson, 2024; Kim et al., 2025; Srinivasan et al., 2024; Watson, 2024).

My own journey to gain knowledge of GenAI and skills before integrating this assignment illustrated the importance of faculty development and readiness in AI literacy. Without sufficient educator confidence and competence, even well-designed AI-integrated activities may fail to meet learning objectives or adequately address ethical challenges (Dorin & Atkinson, 2024; Kim et al., 2025). Educators must be confident and competent in using AI tools to design learning experiences that challenge students to question machine-generated content, address bias, and uphold ethical principles (Kim et al., 2025). My learning journey highlights the need for the faculty development that is so foundational to meaningful integration of GenAI, or any new technology, into nursing curricula. These insights contribute to the growing dialogue about how nurse educators can responsibly incorporate generative AI tools to better prepare graduates for the rapidly evolving technological workplace and academic settings. Future research could build on this assignment of GenAI integration with larger student cohorts, comparing different AI tools, or exploring long-term effects on clinical reasoning and judgement with patient care.

Strengths and Limitations
A key strength of this study was its timely focus on the experiential integration of generative AI in graduate nursing education, which addresses an emerging area of nursing informatics. This practice-oriented assignment strengthens both the relevance and applicability of the findings. The project assessment contributes practical insights into how advanced nurse practitioners can appraise and validate AI-generated content, highlighting essential digital literacy skills that nurse educators must foster.

The small sample size (n=5) and lack of triangulation are significant limitations to generalizability. All participants self-enrolled in the course, which could introduce selection bias and reflect students who were already motivated to engage with emerging technologies. The retrospective design relied solely on student-submitted assignments; no direct follow-up interviews or surveys were conducted to triangulate or deepen the qualitative insights. Finally, as this was the first implementation of this pilot GenAI assignment, results may differ with a larger cohort or refinements to the assignment design. Future work with a larger, more diverse cohort would strengthen the evidence and broaden its impact.

Conclusion

This discussion has addressed the timely and relevant topic of integrating generative AI into graduate nursing education through an experiential assignment. The goal of this assignment was to inform best practices and contribute to the evolving discourse on technology-enhanced nursing education, while preparing graduate nursing students to understand, evaluate, and apply GenAI tools in the clinical and educational setting. Generative AI can be an effective tool to foster digital literacy while highlighting the importance of rigorous validation when using such technologies. Student knowledge, skills and confidence should be appropriately developed to detect bias, inaccuracies, falsified or fabricated content within AI-generated outputs.

Evidence-based integration of GenAI in nursing curricula represents a timely area for continued research and scholarly inquiry to ensure that future nurses are prepared to integrate emerging technologies safely, ethically, and effectively in patient care. The findings of this small-scale experiential pilot assignment contribute to the emerging literature on integrating generative artificial intelligence into nursing education. The evaluation of the student experiences offers an early look into useful insights about benefits and challenges of the use of AI for patient education.

Disclosure: The author has disclosed no significant relationships with, or financial interest in, any commercial companies pertaining to this article. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author

Leighsa Sharoff, EdD, PMHNP/CNS, AHN-BC, CNE, ANEF
Email: lsharoff@hunter.cuny.edu
ORCID ID: https://orcid.org/0000-0001-9843-1519

Leighsa Sharoff is an experienced educator, practitioner, nurse leader, mentor and researcher with numerous publications, presentations, invited lectures, and funded research grants and projects to her credit. Sharoff’s extensive expertise in a plethora of nursing specialties provides opportunities for research focusing on nursing education, including holistic nursing, simulation, genetics and genomics and innovative teaching-learning strategies. She was an early adaptor and innovator of online teaching and provides an extensive assortment of teaching-learning strategies to accommodate all learners. She received a bachelor's in nursing degree from Adelphi University, master's degree in nursing from Hunter College/City University of New York as a psychiatric mental health nurse practitioner and clinical nurse specialist, and a doctorate in education from Columbia University/Teachers College. Dr. Sharoff is a Fellow in National League for Nursing Academy of Nursing Education and the New York Academy of Medicine. She is a Certified Nurse Educator and an Advanced Certified Holistic Nurse. She is a tenured Associate Professor at Hunter-Bellevue School of Nursing at Hunter College.