Issue #1 Week of Jun 26, 2026 – Jul 10, 2026
49Papers
This issue at a glance
This issue leans heavily toward nursing education, with 29 of the papers examining AI literacy, attitudes, and teaching tools for students and faculty, from scales measuring AI and informatics competency to studies of chatbots, podcasts, and virtual reality simulations. A second thread covers clinical decision support, including triage systems, ECMO outcome prediction, diabetes risk models, and a pressure injury alert tool, often finding nurses treat AI as a helpful reminder rather than a decision-maker. A smaller set of papers looks at patient-facing AI tools like chatbots for caregivers and health information, and at generative AI applied to documentation and care plan writing.
The issue mixes original studies and reviews: many are cross-sectional surveys of students or nurses, alongside several scoping reviews, systematic reviews, and meta-analyses of nursing education literature. Settings span multiple countries, including Taiwan, Mexico, Palestine, China, Turkey, Saudi Arabia, South Korea, and France, though several papers flag unclear population or setting details. A cluster of related work addresses AI literacy scales and competency frameworks for students and nurse practitioners, while another cluster focuses on generative AI care plan and documentation quality. Study designs range from small pilot feasibility work to larger pooled analyses.
AI literacy and attitudes measurement 8Generative AI in teaching and simulation 10AI-enabled clinical decision support and trust 10Generative AI for documentation and patient-facing information 7
How this digest is made: Papers retrieved bi-weekly from the community-configured sources (PubMed, arXiv, medRxiv, and/or CINAHL — see Digest Settings for this issue's exact sources) using search terms agreed at AINurse-26. Results are restricted to English-language papers; both title and abstract are checked independently, and either being predominantly non-English excludes the paper. Papers are then classified against the community's chosen topics — anything that doesn't clearly match is left out of the issue rather than shown as "Other." Summaries are generated by Claude Sonnet, constrained to report only what the abstract states, with no speculation or inference. The "At a Glance" section above is written under the same constraints — the model synthesizes themes only from the summaries included in this issue, and every number in it (counts, changes since the previous issue) is computed directly from the archive, not by the model. Flagged items indicate incomplete abstracts or unverified peer-review status.
All (49)Clinical Decision Support (9)NLP & Generative AI (4)AI Ethics & Governance (1)EHR & Workflows (1)Workforce & Education (29)Patient-Facing AI (4)AI Methods & Evaluation (1) Flagged (26)
Clinical Decision Support9 papers
Clinical Decision Support
From Triage to Intensive Care: A Qualitative Study of Nurses' Experiences with AI-Enabled Decision Support.
Nursing in critical care · Jul 2026
This qualitative study interviewed 16 emergency and critical care nurses in three Saudi Arabian public hospitals about their experiences using AI-enabled decision support systems for triage, deterioration prediction, and escalation. Nurses described AI as both helpful and burdensome, saying it increased interpretive work, accountability, and ethical tension rather than reducing responsibility. The findings suggest a need for human-centred governance, AI literacy training, and clear accountability frameworks to support safe AI use in acute care.
Clinical Decision Support
Externally Tested AI Models for Malignancy Classification of Lung Nodules at CT: A Systematic Review and Meta-Analysis.
Radiology. Artificial intelligence · Jul 2026
This systematic review and meta-analysis pooled 21 studies (7454 lung nodules) testing AI deep learning models for classifying lung nodule malignancy on chest CT. Pooled sensitivity was 88% and specificity 75%, suggesting AI models may help rule out malignancy, but high heterogeneity, inconsistent reporting, and bias risks limit conclusions. For nursing informatics, this highlights the need for standardized reporting before clinical adoption.
Clinical Decision Support
Predicting Nosocomial Infections in Hematologic Patients: A Machine Learning Model Based on Dynamic Body Temperature Trajectories.
Journal of clinical nursing · Jul 2026
Researchers studied 6,989 hematology patients at a Chinese hospital from 2014–2023 to find body temperature patterns linked to nosocomial infections. They identified four temperature trajectories and built an XGBoost model (AUROC 0.801) using temperature patterns plus clinical markers like procalcitonin and neutropenia to predict infections. This tool could help nurses spot infection risk earlier and support proactive, data-driven monitoring in vulnerable patients.
Clinical Decision Support
Enhancing Community-Based Nursing Decision Support: Machine Learning Models for Diabetes Risk Prediction Using Home Health Nursing Notes.
Public health nursing (Boston, Mass.) · 2026
Researchers analyzed 7,896 home health care records from 1,747 adult patients in South Korea over 10 years to find risk factors for type 2 diabetes and build a machine learning prediction model. Using nursing notes alongside structured data, they identified factors like female sex, depression, hypertension, and dysuria, and found the Random Forest model performed best (AUC = 0.985). This suggests nursing documentation can help support early diabetes screening and more personalized home care.
Clinical Decision Support
Physical therapists' perspectives on a large language model-powered knowledge translation tool for guideline adherence: A qualitative focus group study.
Physiotherapy Theory & Practice · Jul2
Researchers held six focus groups with 20 German physical therapists (inpatient and outpatient) to explore their experiences using a prototype LLM-based tool designed to support clinical guideline use. Therapists responded positively overall but noted slow response times, and usage plans differed by setting: inpatient therapists wanted it for personal learning between sessions, while outpatient therapists saw it aiding patient education during sessions. This suggests such tools could support guideline adherence, but further work on speed, content quality, and ethical use is needed.
Clinical Decision Support
Enhancing Community‐Based Nursing Decision Support: Machine Learning Models for Diabetes Risk Prediction Using Home Health Nursing Notes.
Public Health Nursing · Jul2
This retrospective study used 7,896 home health care records from 1,747 patients in South Korea to build machine learning models predicting type 2 diabetes risk, combining structured data with nursing notes analyzed through natural language processing. The Random Forest model performed best (AUC = 0.985), and factors like female sex, depression, hypertension, and certain nursing services were linked to prediabetes symptoms. This suggests nursing documentation can support early, personalized diabetes screening in home care settings.
NLP & Generative AI4 papers
NLP & Generative AI
Dictionary-Augmented Large Language Model Postprocessing for Bilingual Code-Switched Medical Speech Recognition: Development and Evaluation Study.
Journal of medical Internet research · Jul 2026
Researchers tested a hybrid approach to improve automatic speech recognition for Korean nursing notes that mix Korean and English medical terms. Combining a medical terminology dictionary with large language model postprocessing cut character error rate by 64.9% compared to baseline speech recognition, with Claude Sonnet 4 performing best. This method needs no model retraining, offering a practical way to improve documentation accuracy in multilingual clinical settings.
NLP & Generative AI
Evaluating Artificial Intelligence–Generated Nursing Care Plans: A Scenario‐Based Comparative Study of Accuracy, Completeness, Quality, and Readability.
Journal of Clinical Nursing (John Wiley & Sons, Inc.) · Jul2
Researchers tested three AI tools (ChatGPT, Gemini, DeepSeek) on ten clinical scenarios across five nursing specialties to see if they could generate accurate, complete nursing care plans using standard taxonomies. All tools produced moderate-to-high quality plans, with DeepSeek slightly more accurate and complete, and surgical scenarios performing best. However, all outputs were incomplete and written at a college reading level, suggesting AI-generated care plans need expert review before clinical use.
EHR & Workflows1 paper
EHR & Workflows
The Impact of Electronic Health Records on Nurses and Nursing Care in Low- and Middle-Income Countries: A Scoping Review.
Nursing open · Jul 2026
This scoping review examined 41 studies from 2015–2024 on how electronic health records affect nurses and nursing care in low- and middle-income countries. Researchers found EHRs improved documentation and data access but also caused data quality problems, higher workload, workflow disruption, technostress, and less personal patient care. The review calls for more theory-based, nurse-led research, especially in Sub-Saharan Africa, to guide better digital health implementation and policy.
Workforce & Education29 papers
Workforce & Education
Generative Artificial Intelligence Literacy Scale for Nurses: Development and Psychometric Evaluation.
Journal of medical Internet research · Jul 2026
Researchers in Taiwan developed and tested the GenAILS, a new tool measuring nurses' generative AI literacy, using a two-phase survey of 1122 registered nurses. The final 24-item scale covers six areas, including risk identification and ethics, and showed strong reliability and validity. This tool can help nursing programs assess AI knowledge gaps and design targeted training on safe, ethical GenAI use.
Workforce & Education
The Students Nurse Informatics Competency Self-Assessment Scale (IECIEEs): From Translation to Pilot Test.
Studies in health technology and informatics · Jun 2026
Researchers translated the Students Nurse Informatics Competency Self-Assessment Scale (IECIEEs) into Spanish and tested it with 300 nursing students in Mexico, after expert validation by 15 specialists in nursing informatics and computer science. The tool showed acceptable content validity and high internal consistency, with cut-off points set to identify low, medium, and high competency levels. This gives Spanish-speaking nursing programs a validated tool to assess informatics skills and guide education planning.
Workforce & Education
Use of a Conversational Agent for Training Mental Health Professionals in Suicide Safety Planning: Pilot Feasibility and Acceptability Study.
JMIR mental health · Jun 2026
This pilot study tested a GenAI-based chatbot simulator that trains nurses and nursing assistants in psychiatric units to build suicide safety plans. Twenty participants at a French university hospital rated the tool highly for acceptability, realism, and usefulness, and their willingness to manage patients with suicidal ideation increased after the session. These early findings suggest the simulator is feasible for training, though larger controlled trials are needed to confirm effects on training and patient outcomes.
Workforce & Education
Knowledge, attitudes, and practices toward artificial intelligence among health sciences students: A cross-sectional study in Palestine.
PloS one · 2026
This cross-sectional study surveyed 666 nursing, medicine, and dentistry students at a Palestinian university about their AI knowledge, attitudes, and practices. Students showed limited AI knowledge (42.9% accuracy) but generally positive attitudes and moderate use of AI tools for schoolwork; formal AI training linked to higher knowledge and slightly better attitudes, but not more frequent use. This suggests structured AI literacy training could benefit health sciences curricula, including nursing education.
Workforce & Education
Teaching with intelligence; AI-enhanced pedagogies in nursing education A scoping review.
Nurse education in practice · Jul 2026
This scoping review examined nine studies (2023–2025) on AI tools like ChatGPT used in nursing education across undergraduate, postgraduate, and faculty settings. The review found five themes, including gains in knowledge, confidence, clinical reasoning, communication, and engagement, when AI supported simulation and reflective feedback. This suggests AI can enhance nursing education across thinking, emotional, and behavioral learning areas when used with ethical oversight.
Workforce & Education
Evaluation of artificial intelligence generated immersive virtual reality simulation on skill mastery.
Journal of professional nursing : official journal of the American Association of Colleges of Nursing · 2026
This study tested AI-generated immersive virtual reality (IVR) simulations with 42 undergraduate nursing students, evaluating medication administration and IV insertion skills across three checkpoints. Scores in critical thinking, clinical decision-making, and skill proficiency improved over time, and students reported high satisfaction with the IVR experience. This suggests AI-generated IVR simulation may help expand clinical training opportunities amid clinical site shortages.
Workforce & Education
The relationship between AI self-efficacy and critical thinking among nursing students: A network analysis.
Nurse education in practice · Jul 2026
This cross-sectional study surveyed 611 Chinese nursing students to map how different aspects of AI self-efficacy connect to critical thinking, using network analysis. Anthropomorphic interaction (AI seeming human-like) was the most central AISE feature, while perceived controllability of AI bridged self-efficacy and critical thinking, with effects varying across thinking dimensions. This suggests nursing informatics education should address how students perceive AI interaction, not just efficiency gains, to support critical thinking.
Workforce & Education
AI Literacy as a Mediator Between Education Climate and Learning Agency in Nursing Students.
Nursing education perspectives · 2026
This cross-sectional survey of 1,867 nursing students from seven junior colleges in China examined how educational climate and AI literacy relate to student learning agency in generative AI-supported learning. Both educational climate and AI literacy showed positive links to learning agency, and AI literacy partially explained the connection between educational climate and learning agency. This suggests nursing programs may want to build supportive learning environments alongside AI literacy training to prepare students for AI-integrated education and practice.
Workforce & Education
Nursing Educators' Utility, Preparedness, and Attitudes Toward Generative Artificial Intelligence in Teaching and Assessment: A Scoping Review.
Nurse educator · 2026
This scoping review looked at 54 peer-reviewed studies on how nursing educators in tertiary settings view and use generative AI in teaching and assessment. It found four themes: GenAI's utility, educators' preparation, their attitudes, and recommendations for the future, showing potential for simulation and assessment support alongside a need for digital literacy training and ethical guidance. This matters because successful GenAI adoption in nursing education depends on institutional support and clearer understanding of its long-term effects on learning.
Workforce & Education
Nursing Student and Faculty Perceptions and Uses of Generative AI Chatbots in Nursing Education: A Mixed Methods Study.
The Journal of nursing education · Jul 2026
This mixed methods survey studied 254 BSN students and faculty to identify factors that shape their willingness to adopt generative AI chatbots in nursing education. Participants reported moderately high usefulness, ease of use, and intent to use, with higher tech proficiency linked to stronger scores; open-ended responses raised concerns about accuracy, ethics, and overreliance. This suggests targeted AI literacy and ethical guidance could support thoughtful chatbot adoption while protecting core nursing values.
Workforce & Education
A structural equation modeling based on the conservation of resources theory: Analyzing the relations between artificial intelligence attitude, employability, and career stress in nursing students.
Nurse education today · Jul 2026
This study surveyed 521 third- and fourth-year nursing students at two Turkish universities to examine links between AI attitudes, perceived future employability, and career stress. Positive AI attitudes were tied to higher perceived employability, while negative AI attitudes were linked to lower employability and higher career stress; employability partly explained the connection between negative AI attitudes and career stress. This suggests nursing curricula could build AI-related skills and confidence to help ease career stress.
Workforce & Education
Nursing Students' Perceptions and Attitudes on the Application of Artificial Intelligence in Nursing Education: A Mixed‐Methods Systematic Review.
Journal of Advanced Nursing (John Wiley & Sons, Inc.) · Jul2
This mixed-methods systematic review combined 28 qualitative and quantitative studies to examine nursing students' perceptions and attitudes toward artificial intelligence in nursing education, using the Technology Acceptance Model. Findings showed students were generally willing to use AI, but they also noted concerns about its ease of use and practicality. This synthesis helps nursing informatics researchers understand student attitudes that may shape future AI adoption in nursing curricula.
Workforce & Education
The Development of Telelearning as an Effort to Improve Nurses' Readiness to Adopt Artificial Intelligence.
Journal of Patient Experience · 7/3/
This cross-sectional study surveyed 219 clinical nurses at a Ministry of Health Hospital to examine telelearning development and readiness to adopt AI. Most nurses showed high levels of both telelearning development (58.9%) and AI readiness (59.4%), with a significant positive correlation between the two (ρ=0.485; p=0.001). This suggests strengthening telelearning environments may support nurses' preparedness for AI adoption, though self-report and cross-sectional design limit conclusions.
Patient-Facing AI4 papers
Patient-Facing AI
Investigating patients' perceptions of ChatGPT as a health information resource: A qualitative study.
Nutrition & Health · Jul2
This qualitative study interviewed 28 outpatients at a public hospital who used ChatGPT daily for two weeks to explore health topics. Most participants reported benefits like improved health literacy, efficiency, and cost-effectiveness, while barriers included lack of personalized information, limited reliability, and reduced human interaction. For nursing informatics, this highlights both the potential and limits of AI chat tools as patient-facing health resources, warranting further study across populations.
Flagged Papers26
Clinical Decision Support 3 Flagged
Clinical Decision Support
Population unclear Setting unclear
Artificial Intelligence in Disaster Triage: Enhancing Emergency Nursing Practice Through Decision Support.
Journal of emergency nursing · Jul 2026
This paper discusses how AI-powered tools, like tele-triage and injury prediction systems, can support emergency nurses during mass casualty incidents when traditional triage methods like JumpSTART may fall short under stress. The authors note benefits like improved accuracy and resource allocation, but also risks such as data bias, low transparency, and over-reliance on automation. They stress that nurses need a role in system design, override options, and training to keep AI tools safe and trustworthy.
Clinical Decision Support
Population unclear
Use of Artificial Intelligence to Predict Outcomes in Critically Ill Patients Receiving Extracorporeal Membrane Oxygenation: A Systematic Review.
Journal of cardiothoracic and vascular anesthesia · Jul 2026
This systematic review examined 14 studies describing 31 machine learning models built to predict outcomes, like mortality and neurologic complications, in patients receiving ECMO. About half of the models showed good to excellent predictive performance, but all had a high risk of bias, and only three were tested in outside populations. For nursing informatics, this shows these tools need stronger validation before they can support ECMO care decisions.
Clinical Decision Support
Population unclear Setting unclear
From Algorithms to Trust: Re‐Engineering Nursing Practice With AI...Spoon D, de Vroed A, Greup S, et al. Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation. J Clin Nurs. 2026 Jan 20. doi: 10.1111/jocn.70212.
Journal of Clinical Nursing (John Wiley & Sons, Inc.) · Jul2
This pilot study looked at how nurses used DRAAI, an AI tool that predicts pressure injury risk, in clinical practice. Nurses saw it as a helpful reminder rather than a decision-maker, and success depended more on workflow and trust than on technical accuracy. The study points to a need for clearer AI explanations, safeguards against skill loss, and more patient involvement in care decisions.
NLP & Generative AI 2 Flagged
NLP & Generative AI
Population unclear Setting unclear
Generative artificial intelligence in nursing care research: Applications across the research process.
Enfermeria clinica · 2026
This article examines how generative AI can support nursing research across its different stages, from design to dissemination. The authors describe potential uses and limitations, review relevant ethical and regulatory frameworks, and propose a ten-point plan for responsible use. They conclude AI can aid research reasoning but requires human oversight and verification, not replace professional judgment.
NLP & Generative AI
Setting unclear
Evaluating Artificial Intelligence-Generated Nursing Care Plans: A Scenario-Based Comparative Study of Accuracy, Completeness, Quality, and Readability.
Journal of clinical nursing · Jul 2026
Researchers tested ChatGPT, Gemini, and DeepSeek on generating nursing care plans for ten clinical scenarios across five nursing specialties, using standardized taxonomies. All three tools produced moderate-to-high quality plans, with DeepSeek slightly more accurate and complete; surgical scenarios scored highest, but all outputs were incomplete and written at a college reading level. This suggests AI tools may help draft care plans but need expert review before clinical use.
AI Ethics & Governance 1 Flagged
AI Ethics & Governance
Population unclear
Explainable Artificial Intelligence in Critical Care Nursing: A Discussion Paper.
Nursing in critical care · Jul 2026
This discussion paper examines explainable AI (XAI) in critical care nursing, where opaque AI tools raise ethical and safety concerns. It argues XAI's transparency, interpretability, and contestability are essential for nurses who must assess, communicate, and challenge AI recommendations, though value depends on explanations being clinically meaningful at the bedside. This matters because it positions nurses as key stakeholders in AI design, governance, and policy to protect accountability and patient safety.
Workforce & Education 16 Flagged
Workforce & Education
Population unclear
The ethical integration of generative artificial intelligence in Doctor of Nursing Practice project planning: A formative approach.
Journal of professional nursing : official journal of the American Association of Colleges of Nursing · 2026
Faculty at an Upper Midwestern university piloted two formative assignments for DNP project practicum students, using generative AI to help plan project goals, objectives, tasks, and evaluation plans. Students found GenAI helpful for generating ideas and improving efficiency, but recognized its flaws and risks of overreliance; faculty noted the need for ongoing review of prompts. This suggests structured GenAI activities can support self-directed learning and build AI literacy in DNP planning.
Workforce & Education
Population unclear Setting unclear
Preparing the nursing workforce for the health AI era: Insights from the Macy Foundation Conference on AI in Medical Education.
Journal of professional nursing : official journal of the American Association of Colleges of Nursing · 2026
This article draws on the 2024 Macy Foundation Conference on AI in Medical Education, where three nursing scholars joined 50+ interdisciplinary participants. The authors offer recommendations for nursing education, research, and leadership, urging AI competencies in curricula, interdisciplinary research, and policy advocacy. This matters because it calls on nurse leaders to actively shape AI implementation so it serves patients and the nursing profession.
Workforce & Education
Population unclear Setting unclear
What AI Cannot Teach: An Epistemological Reconceptualisation of the Nurse Educator Role Based on an Analysis of Carper's Ways of Knowing.
Nursing inquiry · Jul 2026
This discursive paper examines how artificial intelligence reshapes the nurse educator role, using Carper's patterns of knowing (empirical, aesthetic, personal, ethical) as a framework. The author proposes four reimagined dimensions of the role: epistemic mediator, relational anchor, ethical navigator, and emancipatory facilitator. This matters because it reframes AI in nursing education as an epistemological shift rather than just a tool choice, with implications for curriculum and professional identity.
Workforce & Education
Population unclear Setting unclear
Artificial intelligence in the teaching practice of nursing professionals: opportunities, applications, and challenges.
Enfermeria clinica · 2026
This article discusses how AI can support nursing education, covering five areas: intelligent tutoring, immersive simulation, automated assessment, adaptive curriculum design, and faculty support tools. The authors argue these tools can personalize learning and reduce faculty workload, but note challenges like digital literacy gaps, algorithmic bias, and data protection. This matters because it calls for ethical governance and research on how these tools actually transfer to clinical practice.
Workforce & Education
Population unclear Setting unclear
Levels of Artificial Intelligence Literacy Among Nursing Students: A Systematic Review and Meta-Analysis.
Nurse educator · 2026
This systematic review and meta-analysis of 11 studies examined AI literacy among nursing students. The pooled score of 4.80 shows moderate AI literacy overall, with lower scores among first-year students and those without prior AI training. Nurse educators may want to consider tiered, targeted AI training to address these gaps.
Workforce & Education
Population unclear
From Using AI to Relating Through AI: A Postphenomenological and Reflexive Inquiry Into Nursing Education in Developing Country Contexts.
Nursing philosophy : an international journal for healthcare professionals · Jul 2026
This conceptual paper examines how AI shapes nursing education in developing countries, where infrastructure and knowledge access are often unequal. Using postphenomenology and a decolonial lens, the author argues AI does not just support learning but changes how students perceive patients, interpret knowledge, and develop ethical judgment. This matters because it highlights the need for context-sensitive, reflexive approaches when integrating AI into nursing education globally.
Workforce & Education
Setting unclear
A Pilot Study Exploring the Use of Generative Artificial Intelligence in Nursing Education.
Nursing education perspectives · 2026
This pilot study had baccalaureate nursing students use AI to create a case scenario and nursing care plan, then analyze the content for accuracy, bias, and holistic nursing practices. Students showed statistically significant gains in their perceived ability to spot accurate content, bias, and holistic concepts. The findings offer nurse educators recommendations for teaching students to critically evaluate AI-generated content.
Workforce & Education
Setting unclear
Nurse Transitions From Classroom to Clinical: Defining and Prioritizing Nursing Informatics Competencies.
Nursing education perspectives · 2026
This study surveyed nurse educators and health care employers to identify gaps in nursing informatics competencies among BSN graduates, using surveys and focus groups. Graduates showed strong skills in data privacy, patient engagement, and evidence-based practice, but lacked competence in system evaluation, database management, and clinical workflow integration. These findings suggest nursing programs may need to update informatics education to better prepare graduates for clinical practice.
Workforce & Education
Population unclear
Blinded But Biased: Students Prefer Chatbot Until They Know It Is One.
The Journal of nursing education · Jul 2026
This pilot study asked seven DNP students to rate blind statistical help from an LLM chatbot, a graduate assistant, and a professor on helpfulness, satisfaction, and likelihood of use. The chatbot scored highest when students didn't know the source, but ratings dropped once students suspected AI was involved. This bias against AI, even when its responses were preferred, may affect how nursing programs adopt AI tools for academic support.
Workforce & Education
Setting unclear
Prompting Progress: Using Generative Artificial Intelligence to Enhance Nursing Students' Understanding of Social Determinants of Health.
Nurse educator · 2026
This study compared 55 nursing students who used generative AI-enhanced simulations versus traditional teaching methods to learn about social determinants of health (SDOH), measuring knowledge and competence before and after the intervention. Students in the GAI group showed nearly double the improvement compared with the traditional group, a difference the researchers considered practically meaningful. This suggests GAI tools may offer an affordable way to strengthen SDOH education in nursing curricula.
Workforce & Education
Population unclear Setting unclear
Development and Validation of the Nurse Practitioner Clinical Competency Progression Rubric.
Nurse educator · 2026
Researchers developed the NP-Clin-CPR, a rubric that combines Dreyfus novice-to-expert stages with entrustable professional activity levels to track nurse practitioner students' clinical competency over time. Eight NP experts reviewed 40 behavioral descriptors, and after two rounds of feedback, all descriptors met validity thresholds for relevance and clarity. This tool gives NP education programs a structured, evidence-based way to monitor skill progression and assess readiness for practice.
Workforce & Education
Population unclear Setting unclear
Reimagining critical thinking in the age of AI: Implications for nursing education and practice.
Nurse education today · Jul 2026
This commentary discusses the debate over generative AI (GenAI) use in undergraduate nursing education and its effects on critical thinking and clinical judgement. The author argues GenAI can support deeper analytical skills when students critique its outputs, but warns it may marginalize tacit knowledge and ethical reasoning if used uncritically. This matters because it calls for deliberate curriculum design and AI literacy training so nursing students can use GenAI tools thoughtfully in practice.
Workforce & Education
Population unclear Setting unclear
Generative AI Podcasts for Learning: Perceptions of Faculty and Graduate Nursing Students.
The Journal of nursing education · Jul 2026
This descriptive, cross-sectional pilot study looked at graduate nursing students' views on AI-generated podcasts used to deliver course content. Students liked that the podcasts were convenient, engaging, and helpful for different learning styles, but they raised concerns about quality, trustworthiness, and added coursework. The findings suggest AI podcasts may offer a flexible teaching tool while easing faculty workload, though concerns remain unresolved.
Workforce & Education
Population unclear Setting unclear
Experiences and Perspectives of Nursing Students and Educators on Artificial Intelligence in Nursing Education: A Qualitative Systematic Review and Meta-Synthesis.
Nurse educator · 2026
This qualitative systematic review synthesized 34 studies on nursing students' and educators' experiences with AI-based educational tools. Researchers identified five themes: AI supports deep learning, clinical reasoning, professional confidence, and ethical reflection, but also raises concerns about data security and whether faculty feel prepared. These findings can help guide thoughtful, ethical integration of AI into nursing education programs.
Workforce & Education
Population unclear
Artificial intelligence in nursing education: A scoping review of intelligent tutoring systems-evidence and implementation gaps.
Nurse education today · Jul 2026
This scoping review looked at 9 studies on intelligent tutoring systems (ITS) in nursing education, drawn from 881 initial records. The review found evidence is limited and inconsistent, with most ITS designs incomplete and lacking user-needs evaluation, theoretical grounding, or curricular integration testing. This highlights a need for more rigorous research before nursing programs adopt ITS tools widely.
Workforce & Education
Population unclear Setting unclear
The Evolution and Future of Nursing Education: Bridging Evidence‐Based Practice and Hybrid Intelligence.
Journal of Advanced Nursing (John Wiley & Sons, Inc.) · Jul2
This article traces how nursing education changed over the past 50 years, moving from vocational training toward evidence-based, technology-integrated, and globally connected learning. It highlights milestones like evidence-based practice in the 1970s, advanced practice roles in the 1990s, and simulation and digital tools since the 2000s. For nursing informatics, it points to future challenges like AI integration and the need to balance technology with ethical, compassionate care.
Patient-Facing AI 3 Flagged
Patient-Facing AI
Setting unclear
Assessment of Large Language Models for Tracheostomy Care in Pediatrics: ChatGPT vs Claude.
Journal of pediatric health care : official publication of National Association of Pediatric Nurse Associates & Practitioners · 2026
Researchers compared ChatGPT-4.o and Claude 4.0 Sonnet on 17 caregiver questions about pediatric tracheostomy care, with nine pediatric otolaryngologists rating the answers. ChatGPT-4.o scored higher on accuracy and readability, while Claude scored higher on completeness; clarity and relevance were similar. Both tools may offer some educational value for caregivers, but their use in urgent situations was untested, and clinical oversight remains needed.
Patient-Facing AI
Population unclear Setting unclear
Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.
Journal of Medical Systems · 7/1/
Researchers developed Starmate, a lightweight AI assistant built for autism caregivers, combining sentiment analysis, knowledge-graph retrieval, and a fine-tuned language model. In blinded testing against commercial LLMs, Starmate scored higher overall and showed advantages in empathy, hands-on guidance, and logical clarity, results supported by automated benchmarks. This suggests smaller, domain-specific AI tools may offer accurate, empathetic support for caregivers, though real-world usability and clinical testing are still needed.
Patient-Facing AI
Population unclear Setting unclear
Readability and Linguistic Characteristics of Alzheimer's Disease and Related Dementias Prevention, Symptom, and Treatment Information from Generative Artificial Intelligence Chatbots.
Journal of Gerontological Nursing · Jul2
Researchers analyzed 66 outputs from free GenAI chatbots covering Alzheimer's disease prevention, symptoms, and treatment information. The text, especially treatment information, required college-level reading skills, and language analysis showed high analytical thinking but low authenticity, clout, and emotional tone. Nurses should know these chatbot outputs may be hard to read and lack warmth, which could affect how ADRD caregivers understand and trust the information.
AI Methods & Evaluation 1 Flagged
AI Methods & Evaluation
Setting unclear
Beyond block time: a head-to-head comparison of reinforcement learning, genetic algorithms, and predict-then-optimize scheduling for operating room workflow using discrete-event simulation.
International journal of medical informatics · Jul 2026
Researchers used a computer simulation of a general surgery operating room to compare four scheduling strategies: a rule-based baseline, predict-then-optimize, genetic algorithm, and reinforcement learning (RL). All AI methods reduced delays and overtime and improved utilization compared to the baseline, with RL performing best across all measures. This suggests AI scheduling tools may support OR workflow decisions, but the authors note results depend on the simulation's design and need local testing before real-world use.