Background: Therapeutic communication (TC) is a fundamental nursing competency, yet students often struggle to apply TC skills with limited individual feedback in clinical settings. Emerging technologies such as artificial intelligence (AI) and virtual reality (VR) simulation offer new avenues to support TC development. However, evidence examining the value of these experiences for students’ TC development remains limited. Methods: This study used a convergent mixed-methods, post-test–only, descriptive design with prelicensure BSN nursing students (n=33). Quantitative data were collected using the Technology Acceptance Model (TAM2) to assess nursing students perceived usefulness, ease of use, and behavioral intention to use VR for TC practice. Qualitative data were gathered through open ended student reflections exploring perceived strengths and limitations of the learning experience. Results: Quantitative findings demonstrated highly perceived usefulness, ease of use, and intention to use VR-based technologies for TC practice. Qualitative findings converged with these results as students described the VR scenarios as realistic and reflective of real-time clinical communication experiences. Divergence occurred with confidence; however, this reflected student perceived emotional difficulty of the VR experience and self-reflection of skill gaps. Conclusion: AI-augmented VR represents a promising complementary approach for TC training in healthcare education. By providing realistic, emotionally authentic scenarios in a user-friendly environment, VR may address persistent challenges in teaching and learning TC. Keywords: simulation, virtual reality, artificial intelligence, nursing, educational technology

Background: Generative artificial intelligence (GAI) offers opportunities to enhance learning in nursing education yet raises concerns about academic integrity and critical thinking. Limited research exists on nursing students' ethical understanding and prior GAI exposure. Aim: To explore freshman nursing students' understanding of ethical versus unethical uses of GAI, their foundational AI literacy, and prior exposure to AI training. Methods: A cross-sectional descriptive study was conducted using a researcher-developed survey administered to 119 freshman BSN students at a large Midwestern university. The survey assessed knowledge of GAI ethics, GAI use, and perceptions of university-led GAI training. Results: Students demonstrated a strong ability to differentiate between ethical and unethical uses of GAI (93 % accuracy). However, gaps were noted in understanding when AI-generated content crosses into academic dishonesty. Many students reported limited AI training and expressed strong interest in AI learning modules. Conclusions: Freshman nursing students are eager to use GAI responsibly but lack foundational training. AI literacy education is essential to support ethical decision-making, preserve academic integrity, and prepare students for responsible AI use in nursing practice. (c) 2025 The Authors. Published by Elsevier Inc. on behalf of Organization for Associate Degree Nursing. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Since the middle of the 20th century, the public image of nurses has undergone significant transformation. Shifts in social and gender norms, media portrayal of stereotypes, and evolving healthcare roles for nurses have all contributed to the public perception of nursing. With technological advancements and the explosion of generative artificial intelligence (GAI), images of nurses can now be created in seconds with text-to-image generators; however, these images may contain biases and stereotypes which can lead to continued misperceptions which potentially harm the nursing profession. The purpose of this research study was to empirically and systematically examine GAI images of nurses (n = 288) from three AI image generators using quantitative content analysis. Statistically significant differences were found between the three image generators regarding gender, ethnic diversity, and the sexualization of nurses in images. The study findings highlight the need for critical evaluation of AI-generated imagery to address biases and stereotypes, with implications for leveraging AI technologies responsibly to promote an accurate and diverse representation of the contemporary nursing profession.

Background Nursing students increasingly are working as nursing assistants (NA) while pursuing their nursing education. The literature discusses both the stressors and benefits of students working as an NA during nursing school; however, there is limited research on the direct effects of students having NA experience during nursing school. Method This repeated measures study examined the effects of NA experience on grade point average, anxiety levels, and clinical judgment in simulation among undergraduate nursing students. Results No significant differences were found in grade point average or anxiety levels between nursing students with and without NA experience. However, nursing students with at least 12 months of NA experience demonstrated significantly higher clinical judgment in individual simulations, particularly in the responding phase of Tanner's model. Conclusion Although NA work experience may not significantly affect grade point average or anxiety, it may enhance nursing students' clinical judgment, particularly when they respond to clinical situations. [ J Nurs Educ . 2025;64(2):91–97.]

Background:Developing engaging presimulation learning materials that provide contextualized patient information is needed to best prepare students for nursing simulation. One emerging strategy that can be used by educators to create visual images for storytelling is generative artificial intelligence (AI).Purpose:The purpose of this pilot study was to determine how the use of generative AI-created patient backstories as a presimulation strategy might affect student engagement and learning in nursing simulation.Methods:A qualitative cross-sectional survey with content analysis was completed with undergraduate nursing students following an acute care simulation.Results:Student surveys point to positive pedagogical outcomes of using AI image generation as a strategy to prepare for simulation such as decreased anxiety in simulation, increased preparatory knowledge, and increased emotional connection with the patient's story.Conclusions:Images created with generative AI hold promise for future research and transforming nursing education.

The rapid advancement of Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, has sparked widespread discussion among educators and researchers regarding their potential implications for education. This study presents a comprehensive taxonomy of GenAI in academia and education, encompassing a wide range of applications, challenges, ethical considerations, and future prospects. Drawing on a scoping review of 453 articles, including the 50 most cited works throughout 2023, the taxonomy provides a state-of-the-art analysis of the current landscape of GenAI in education. The taxonomy offers a theoretical framework that aligns with the current discourse in GenAI and education, providing a critical evaluation of the existing literature and proposing innovative perspectives and solutions. The practical implications of the taxonomy for educators, researchers, and policymakers are highlighted, emphasizing the need for ethical considerations and informed policies to maximize the benefits of GenAI while minimizing its risks and negative impacts.

Background: Anxiety in simulations can be influenced by various factors that either motivate or immobilize students. Understanding simulation anxiety is crucial for educators to design appropriately challenging scenarios without overwhelming students. No instruments have yet been tested to differentiate between debilitating and facilitating anxiety within nursing simulations. Methods: A quantitative repeated measures design was used to examine students’ baseline and pre-simulation anxiety with 90 pre-licensure junior level nursing students. The Achievement Anxiety Test (AAT) was administered to differentiate levels of debilitating and facilitating anxiety. Results: The revised AAT demonstrated preliminary validity and reliability for measuring debilitating and facilitating anxiety when used in nursing simulation. Linear regression showed only debilitating anxiety significantly predicted pre-simulation state anxiety. Baseline anxiety has a significant impact on students, increasing debilitating anxiety in simulated settings. Conclusions: To ensure success in practice settings, it is important to address students baseline anxiety to support a successful transition into practice. This study was prospectively determined to be exempt with the University’s Institutional Review Board on 6 December 2022 and was not prospectively registered in a formal registry.

Role assignment in nursing simulation is a time met with great anxiety due to the fear of the unknown, performing in front of faculty and peers, and social evaluation anxiety. Using role rubrics and expert modeling videos may better prepare students for their role in simulation, reducing these barriers and promoting student learning. A convenience sample of 13 junior-level Bachelor of Nursing students enrolled in a summer medical surgical nursing course. quantitative cross-sectional design with a content analysis of students open-ended responses. All participants (n = 13) reported reading the role rubric and role-playing to prepare, as well as believing that the expert modeling video reduced their simulation anxiety. Providing students with role rubrics and role demonstrations through expert modeling videos may reduce students' anxiety and enhance preparation for simulated learning experiences. Cite this article: Dodson, T.M., & Reed, J.M. (2024, Month). Enhancing Simulation Preparation: Presimulation Role Rubrics and Expert Modeling Videos. Clinical Simulation in Nursing, 87, 101498. https://doi.org/10. 1016/j.ecns.2023.101498 . (c) 2023 International Nursing Association for Clinical Simulation and Learning. Published by Elsevier Inc. All rights reserved.

Background and Purpose: Nursing education lacks an easily accessible, valid, and reliable short instrument to support researchers and instructors in quickly assessing student anxiety. The purpose of this research was to respond to this need by assessing the reliability and validity of a short-form anxiety instrument adapted from psychology which could measure state and trait anxiety. Methods: Using a one-group, repeated measures design, 51 sophomore level undergraduate nursing students had their state and trait anxiety levels measured at three time points over the course of a semester. Results: Results showed that the anxiety instrument was valid and reliable for use in nursing education with coefficient α ranging from .65 to .88. Conclusion: Future nurse researchers and educators should consider using the short-form anxiety instrument when a quick differentiation is needed to measure state and trait anxiety.

Background: Expert modeling videos (EMVs) have shown promise in improving students’ performance in simulation. However, research evaluating the impact of EMVs is limited to major performance areas, lacking exploration into specific student competency behaviors. Purpose: This study evaluated the effect of an EMV intervention on undergraduate nursing students’ behavioral competencies as measured by the Creighton Competency Evaluation Instrument (CCEI). Methods: Using a quasi-experimental pragmatic evaluation design, students in a medical surgical nursing course (n = 160) viewed either an expert model demonstration video (experimental) or expert model discussion video (control). Students’ behavioral competencies were measured and compared between groups using the CCEI. Results: Students who viewed an expert model demonstration video performed at a higher level of competency in 11 of the 18 CCEI behaviors. Conclusion: Using EMVs in nursing simulation may improve students’ ability to achieve clinical competency in nursing specific behaviors.

Background: New nurse attrition jeopardizes patient safety, devastates career plans, and negatively impacts costs to hospitals and patients. Employers and educators recognize the widening transition-to-practice gap in meeting expectations for practice-ready, resilient nurses. Purpose: The purpose of this study was to test the use of computer-based simulation activities (CBSAs) to measure processes in Aller's Development of Decision-Making and Self-Efficacy Model (ADD-SEM). Methods: BSN students (N = 50) in a multisite, cross-sectional study used CBSAs to provide data on decision making (Virtual Patient Lasater's Clinical Judgment Rubric), psychological capital (Nursing Anxiety and Self-Confidence in Decision-Making Scale©), and reflection. Results: Repeated-measures analysis of variance and reflexive thematic analysis revealed that decision-making (DM) scores were highest in noticing and lowest in interpreting with significant differences in cohorts (DM) (juniors: P < .001; seniors: P = .013) and self-confidence/anxiety (P < .001). Conclusion: The CBSAs are an effective means of measuring student development of DM and psychological capital needed to retain resilient nurses.

Effective communication is essential to safe and competent nursing practice. Literature surrounding nursing students' communicative patterns in high-fidelity simulation is limited and dated. There is a need to determine if new challenges in communication are present or if nurse educators are recognizing the same ineffective patterns over time. Qualitative thematic analysis using existing data from a junior-level nursing acute care simulation was completed. Data consisted of 22 clinical group transcripts representing 171 undergraduate nursing students. Themes identified include poor word choice, depersonalization, and intra/interprofessional communication breakdowns. Many of the findings were consistent with previous literature; however, a new finding of poor word choice was identified. This was identified in students' inability to place patient education in appropriate and professional layman's terms. Ineffective communication can negatively affect patient safety and outcomes. Opportunities to develop therapeutic communication should be woven throughout the nursing curriculum through deliberative practice opportunities. (c) 2023 Organization for Associate Degree Nursing. Published by Elsevier Inc. All rights reserved.

Generative artificial intelligence (AI) is a type of AI that allows for the creation of brand-new content. It uses machine learning from massive data sets to create images, video, and text. With AI on the rise in society and in health care, nurse educators need to find ethical ways to integrate these technologies into educational practices. Most discussion on the use of AI in education has centered on chat-based tools such as ChatGPT or Bing Chat, mostly due to concerns with cheating. Less attention, however, has been paid to AI image generation using tools such as Midjourney, DALL∙E 2, and Stable Diffusion. These AI-based image generators provide a way for users to easily create never-before-seen images from a variety of artistic genres using simple text prompts. Although research is in its infancy early benefits of using images in healthcare education include increasing students' self-reflection, emotional intelligence, critical analysis, and dialogue on complex topics, which are all essential skills in building clinical judgment. Pragmatically, nursing educators can use these unique images to support visual learning and reflection in the classroom, laboratory, simulation, and clinical settings. Generative AI can also allow students and educators to explore mental representations and the public image of nursing in the culture. Generative AI technologies, and particularly AI-based image generation, can be a powerful teaching method to connect art, emotional processing, and reflection, which, when combined, can provide meaningful learning experiences for students.

One way to increase the number of RNs during a global nursing shortage is to recruit those currently not working in health care to rejoin the workforce. The goal of this project was to assess the attitudes and perceived learning needs of nurses who are not working in health care. An online survey was distributed via social media nursing groups to a self-selected sample of nurses not working in health care for the previous 2 years. Although the response rate was low (n = 18), there was interesting discussion on re-entry to nursing practice. Top reasons stated for not re-entering the workforce included burnout/stress, workplace conditions, lack of education/skills, and pay. Pharmacology, skills, and technology were the top three self-identified learning needs of the participants. Limited programs offer education for re-entry to practice. Nurse educators should develop learning materials to meet the needs of this special population. [J Contin Educ Nurs. 2022;53(11):486-490.].

Background High anxiety during simulation has been well documented with calls to reduce students' anxiety. Simulation anxiety is often assumed to be harmful to students and a variety of anxiety-reducing interventions have been suggested. The purpose of this study was to explore the effect of different types of anxiety on the clinical judgment of undergraduate nursing students in simulation. Methods This research used a one-group repeated measures quantitative design using the conceptual framework of Tanner's (2006) model of clinical judgment. Results Anxiety did not have a significant impact on clinical judgment, both overall and within each of the four phases of Tanner's (2006) model. Conclusion The findings imply a changed focus to reframe anxiety and how we think about its effects. Understanding that not all anxiety is debilitating but some is facilitative challenges the assumption that faculty need to attempt to lower students' anxiety in simulation. Rather than seeking to lower anxiety for all students, nursing educators should help students function despite anxiety, in order to prepare them for real world nursing practice.

BACKGROUND:Anxiety accompanying educational simulations is a complex issue impacting nursing students and their learning. Research has provided evidence that some pre-simulation activities can increase student comfort with the simulation environment and may also reduce anxiety. Studies have also provided evidence of promising outcomes for gaming use in nursing education.PURPOSE:This pilot study explored a medical-surgical escape room game as an introductory simulation experience for nursing students, particularly focused on determining student anxiety levels, and both faculty and student perception of the experience.METHODS:A quasi-experimental one group pretest-posttest design was used with a convenience sample to explore student anxiety levels and perceived enjoyment of the game.RESULTS:Student anxiety levels significantly decreased (p = .013); however, anxiety levels remained high at posttest. Students reported high enjoyment of the game and provided positive comments.CONCLUSION:Escape rooms can provide an engaging, interactive way to teach nursing concepts in the simulation environment.

BACKGROUND:Game-based learning has attracted much attention in education in recent years due to its ability to increase student motivation and engagement in learning. This study reviewed the literature to answer the research questions: What learning outcomes have been linked to games in nursing education? What are potential gaps in the field's knowledge regarding games in nursing education?METHOD:A systematic literature search was completed in CINAHL and Google Scholar from 2009-2019 with the keywords of games, gaming, and nursing education.RESULTS:A total of 49 papers were identified; of these, 34 were excluded, and 15 empirical studies were evaluated. The majority reported beneficial learning outcomes, such as increased knowledge, higher test scores, and positive student comments. Several methodological weaknesses were noted, such as small sample sizes, convenience samples, and lack of control groups or randomization.CONCLUSION:Games have the potential to prepare new nurses for improved clinical decision making. More robust research methodologies are needed to confirm best practices for educators. [J Nurs Educ. 2020;59(7):375-381.].