Issue #5 Week of Aug 24, 2026 – Sep 07, 2026
30Papers
This issue at a glance
This issue centers heavily on nursing education, with 19 papers on how students and educators use generative AI tools, virtual tutors, and AI-based teaching models for topics like ECG interpretation, pediatric care, and clinical reasoning. A second thread examines clinical decision support across 8 papers, covering AI early warning systems, deterioration prediction, and discharge planning, generally finding potential benefits alongside calls for more validation and human oversight. Several papers also probe student attitudes and readiness, from AI literacy scales to dependency concerns, while just 2 papers address ethics and governance frameworks and 1 compares AI-generated versus nurse-written discharge instructions.
Papers jumped from 6 to 30 this issue, and the leading topic shifted from NLP & Generative AI to Workforce & Education, which rose from 1 paper last issue to 19 now, while NLP & Generative AI fell from 3 papers to 1. The workforce theme of AI dependency and reduced focus among nursing students continues from last issue. Clinical decision support and ethics threads, absent last issue, reappear with 8 and 2 papers respectively. The prior issue's patient-facing chatbot and EHR workflow threads are not present this issue.
Student AI use, attitudes, and literacy 8AI in nursing curricula and teaching models 8AI clinical decision support and trust in practice 8Governance and policy frameworks 5
Compared withIssue #4
Papers30 ▲ 24
Leading topicWorkforce & Education (was NLP & Generative AI)
Rising▲ Workforce & Education
Cooling▼ NLP & Generative AI
Flagged14 ▲ 12
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 (30)Clinical Decision Support (8)NLP & Generative AI (1)AI Ethics & Governance (2)Workforce & Education (19) Flagged (14)
Clinical Decision Support8 papers
Clinical Decision Support
Trusting the Algorithm or Trusting the Nurse? Critical Care Nurses' Experiences of Automation Bias and Professional Autonomy in AI-Assisted Early Warning.
Nursing in critical care · Sep 2026
This qualitative study interviewed 23 critical care nurses across four Saudi Arabian hospitals about their experiences using an AI-assisted early warning system integrated with electronic health records. Nurses treated algorithmic alerts as useful but incomplete, calibrating trust through experience and sometimes overriding alerts without documentation under workload pressure, revealing a governance gap. This suggests safe AI use in critical care needs supportive governance, training on trust calibration, and nursing leadership involved in implementation.
Clinical Decision Support
Harnessing Artificial Intelligence to Strengthen Acute and Critical Care Nursing Practice: A Systematic Review.
Nursing in critical care · Sep 2026
This systematic review examined seven studies (about 75,000 patients) on nurse-used AI clinical decision support systems in acute and critical care, covering areas like sepsis, deterioration, and delirium prevention. Nurse-facing AI-CDSS were linked to lower hospital mortality, along with improved length of stay and protocol adherence, though evidence was limited and nurse-reported outcomes were sparse. This suggests potential benefits for nursing practice, but careful, nurse-centred evaluation is needed before wider use.
Clinical Decision Support
Validation of a machine learning model for predicting early deterioration in the emergency department.
The American journal of emergency medicine · Sep 2026
Researchers studied 17,481 adult ED visits over six months, combining structured triage data with AI-processed text from nursing notes to predict early deterioration (ICU admission or death within 7 days). A version weighted to prioritize high-risk patients improved recall to 0.77 and ROC-AUC to 0.90, with age, respiratory rate, and blood pressure as top contributors. The authors state this tool could support—not replace—clinical judgment, but needs real-time testing before clinical use.
Clinical Decision Support
Effect of a Machine Learning Algorithm to Guide Goal-Directed Therapy After Cardiac Surgery.
American Journal of Critical Care · Sep2
This single-center study compared cardiac surgery patients (coronary artery bypass, ejection fraction ≥45%) using a machine learning-guided goal-directed fluid therapy program to historical control patients. Patients in the goal-directed therapy group had lower rates of acute kidney injury on postoperative day 2, day 7, and at discharge. This suggests machine learning tools may offer a less invasive way to monitor fluid status in ICU patients after cardiac surgery.
NLP & Generative AI1 paper
NLP & Generative AI
Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study.
Nursing open · Sep 2026
This vignette-based study compared discharge instructions written by GPT-4 versus registered nurses, judged blindly by 15 clinical experts and 38 patients. AI produced more complete information but included safety risks, while nurses scored higher on empathy and readability; older, less-educated patients trusted AI text less. This suggests AI may assist nurses with drafting but needs mandatory nurse review before use.
AI Ethics & Governance2 papers
AI Ethics & Governance
Artificial Intelligence in Family Nursing: An Evidence-Informed Policy Framework for Safe and Equitable Integration.
International nursing review · Sep 2026
This paper reviews literature on artificial intelligence, digital health, and family nursing to propose a policy framework for safely integrating AI into family-centered nursing care, using Japan as an illustrative example. The authors identify five policy domains: ethical and data governance, workforce development, interdisciplinary co-design, adaptive regulation and accountability, and sustainable funding. This matters because it stresses AI should support, not replace, nurses' relational judgment and family communication.
Workforce & Education19 papers
Workforce & Education
AI Use, Perceptions, and Perceived Impact Among Nursing Students: Cross-Sectional Study.
JMIR nursing · Aug 2026
This cross-sectional study surveyed 279 nursing students in Elbasan, Albania, about their use and perceptions of AI tools. Most students used AI, especially virtual assistants like ChatGPT, mainly for information searching, and higher GPA was linked to more AI use. AI helped with understanding lectures and exam prep but showed limited impact on self-confidence, empathy, and critical thinking, raising questions for how AI is integrated into nursing curricula.
Workforce & Education
AI Dependency and Brain Rot Among Nursing Students: A Mixed-Methods Study.
The American journal of nursing · Sep 2026
This mixed-methods study surveyed and interviewed 222 undergraduate nursing students in Türkiye to explore the link between AI dependency and 'brain rot,' a term referring to reduced mental sharpness. Researchers found a moderate positive correlation between AI dependency and brain rot scores, and interviews revealed that heavy AI use during exam prep sometimes reduced focus and dulled thinking. This suggests nursing programs may need to build AI literacy and balance AI use with traditional teaching methods to protect students' critical thinking skills.
Workforce & Education
Nurses' and Nursing Students' Experiences With Generative Artificial Intelligence in Educational and Clinical Settings: A Scoping Review.
Nursing & health sciences · Sep 2026
This scoping review looked at 20 studies from 2020 to 2025 on how nurses and nursing students experience using Generative AI in school and clinical settings. Nurses mostly had positive experiences, while students had mixed but generally positive views, shaped by their attitudes, social norms, and sense of control over the technology. The findings suggest nurse leaders play a key role in creating policies and ethical guidelines for responsible GenAI use.
Workforce & Education
Validity and Reliability of the Medical Artificial Intelligence Readiness Scale, Korean Version: A Methodological and Cross-Sectional Study.
Nursing & health sciences · Sep 2026
Researchers translated and adapted the Medical Artificial Intelligence Readiness Scale into Korean and tested it with 317 nursing students in South Korea. The final 15-item K-MAIRS scale covers four areas—cognition, ability, vision, and ethics—and showed valid, reliable results. This tool can help nursing educators measure and build AI readiness in nursing curricula.
Workforce & Education
Enhancing Learning in Graduate Nursing Education Through a Co-Designed AI Virtual Tutor: A Mixed-Methods Evaluation.
Journal of clinical nursing · Sep 2026
This pilot study tested a co-designed AI virtual tutor built into a graduate nursing course, tracking usage logs, surveys, and interviews with students and teaching assistants. Students used the tutor mostly for lower-level learning tasks early on, shifting to more application and analysis later, and reported high AI self-efficacy though usefulness varied. The tutor was feasible and valued for its accuracy and immediacy, but usability, privacy, and over-reliance concerns highlight the need for careful pedagogical integration.
Workforce & Education
Generative artificial intelligence in undergraduate paediatric nursing education: A scoping review of current applications and ethical considerations.
Nurse education today · Sep 2026
This scoping review examined six quantitative studies on generative AI (GenAI) used to teach paediatric content in undergraduate nursing programs, all from Asian or Middle Eastern settings. GenAI supported scenario-based learning, ethical reasoning, and feedback, with improved engagement, but studies were small, single-site, and relied on self-report, with little evaluation of competence or safety. This matters because nursing informatics research needs larger, rigorous, ethically-guided studies before GenAI is widely used in paediatric nursing education.
Workforce & Education
Development and application of an artificial intelligence agent-based case teaching model for health assessment: A quasi-experimental study.
Nurse education today · Sep 2026
This quasi-experimental study tested an AI agent-based case teaching model for health assessment among 102 sophomore nursing students at a Chinese university. Students using the AI agent scored higher in clinical reasoning, knowledge, and teaching satisfaction than those using a traditional platform, and used more simulated assessment interactions overall. The findings suggest AI agent-based teaching may support nursing skill development, offering a reference point for future AI-enabled nursing education research.
Workforce & Education
Toward an AI-integrated nursing curriculum: A Kano model analysis of generative AI competency needs.
Nurse education today · Sep 2026
This cross-sectional survey of 1219 clinical nurses at a tertiary hospital in China used the Kano model to rank generative AI (GenAI) learning needs. Most nurses had used GenAI (87.69%) and felt positive about it (97.10%), but only 7.39% had formal training; practical skills like teaching materials and patient education ranked as top priorities, while foundational and ethics topics ranked lower. Findings suggest nursing curricula could phase in practical GenAI skills first, then build toward ethics and advanced competencies.
Workforce & Education
Enhancing Learning in Graduate Nursing Education Through a Co‐Designed AI Virtual Tutor: A Mixed‐Methods Evaluation.
Journal of Clinical Nursing (John Wiley & Sons, Inc.) · Sep2
Researchers piloted a co-designed AI virtual tutor, trained on course materials, in a graduate Master of Nursing course with about 120 students. Usage logs, surveys, and interviews showed students mostly used it for lower-level learning tasks, valued its immediacy and accuracy, but raised concerns about usability, privacy, and over-reliance. This suggests AI tutors can support graduate nursing learning when designed carefully, but need ongoing attention to ethical and pedagogical integration.
Workforce & Education
Optimists, Realists, and Traditionalists: Profiling Nursing Students’ Engagement with Artificial Intelligence (AI) in Pediatric Nursing Education.
Journal of Education & Research in Nursing / Hemşirelikte Eğitim ve Araştırma Dergisi · Sep2
This cross-sectional study surveyed 241 nursing students in a pediatric nursing course to explore how they use AI tools and their attitudes toward AI. Cluster analysis identified three profiles—Optimists, Realists, and Traditionalists—with Optimists showing the highest AI use and trust, and Traditionalists the lowest. The findings suggest nursing educators may need differentiated teaching approaches based on students' attitudes toward AI.
Flagged Papers14
Clinical Decision Support 4 Flagged
Clinical Decision Support
Population unclear
Mapping artificial intelligence applications for clinical and operational decision support in prehospital emergency medical services: A scoping review.
International emergency nursing · Sep 2026
This scoping review examined 37 studies on artificial intelligence use in prehospital emergency medical services, covering dispatch, triage, and transport coordination. Most studies used machine learning and were observational or proof-of-concept, showing potential to support early diagnosis and operational decisions, but lacking external validation and real-world testing. For nursing informatics, this highlights the need for rigorous validation, workflow integration, and training before these tools can be safely adopted.
Clinical Decision Support
Population unclear
Development and Evaluation of a Standardized Nursing Language Clinical Decision Support System for Long-Term Care Nurses Using GPT-4.0-Generated Nursing Scenarios.
Journal of gerontological nursing · Sep 2026
Researchers built a clinical decision support system for skilled nursing facility nurses, using standardized nursing language and GPT-4.0-generated resident scenarios. Four participants tested the system, giving a moderate usability score (SUS = 60) and a higher usefulness/satisfaction score (USE = 6.62), with ease of learning rated well. This early work suggests such tools could support nursing care, though testing involved only a small group at one facility.
Clinical Decision Support
Population unclear
Development and Evaluation of a Standardized Nursing Language Clinical Decision Support System for Long-Term Care Nurses Using GPT-4.0–Generated Nursing Scenarios.
Journal of Gerontological Nursing · Sep2
Researchers built a clinical decision support system for skilled nursing facility nurses, using GPT-4.0-generated resident scenarios and standardized nursing language, then tested it with four participants at one facility. Usability scores were moderate to good, with high ratings for ease of learning, and nursing faculty experts gave overall positive feedback. This early evaluation suggests such tools may help support nursing care decisions in long-term care settings.
Clinical Decision Support
Population unclear Setting unclear
Machine Learning in Discharge Planning for Stroke Patients: A Review of Feature Representation and Aggregation Strategies With Suggestions for Improvement...Cui Y, Xiang L, Zhao P, et al. Machine learning decision support model for discharge planning in stroke patients. Journal of Clinical Nursing (John Wiley & Sons, Inc). 2024;33(8):3145-3160.
Journal of Clinical Nursing (John Wiley & Sons, Inc.) · Sep2
This article reviews a machine learning model that helps plan discharge for acute stroke patients by combining clinical severity, function, and social factors like family income. A random forest algorithm performed best, using predictors such as NIHSS score, Barthel Index, and FRAIL score. The authors suggest better feature aggregation methods could improve accuracy, but stress these tools should support, not replace, nurses' clinical judgment.
AI Ethics & Governance 1 Flagged
AI Ethics & Governance
Population unclear Setting unclear
Ethical AI Readiness in Vocational Rehabilitation: A Process Framework for Practice.
Journal of Vocational Rehabilitation · Sep2
This article proposes a conceptual framework to help vocational rehabilitation counselors handle ethical challenges from AI use in practice. The Ethical AI Readiness Framework outlines three capacities—AI literacy, ethical attentiveness, and ethical action readiness—shaped by organizational context and refined through experience. For nursing informatics, this offers a model for thinking about ethical AI use in other health fields, though it does not report tested outcomes or a specific care setting.
Workforce & Education 9 Flagged
Workforce & Education
Population unclear
Turning Pages Into Pixels: Leveraging Technology and Artificial Intelligence in Nursing Professional Development.
Journal for nurses in professional development · 2026
This article describes how a multi-campus nursing professional development center replaced paper-based workflows with QR codes, electronic competencies, cloud-based resources, e-learning, and generative AI. The team reported improved access, streamlined documentation, increased engagement, and reduced instructional design time, while applying ethical governance to AI use. This shows how digital tools can ease administrative burden in nursing education and support responsible AI adoption.
Workforce & Education
Population unclear Setting unclear
Architecting Enterprise Digital Infrastructure for Nursing Professional Development: The Registered Nurse Experience Platform as a Scalable Model for Standardizing Peer Feedback and Workforce Evaluation.
Journal for nurses in professional development · 2026
This article describes the RN Experience Platform, a digital infrastructure built to standardize peer feedback, self-assessment, and evaluation in nursing professional development. The platform combines structured data collection, longitudinal analytics, and AI-assisted narrative synthesis to support reflective practice and leadership decisions. It shows one way NPD practitioners might use AI tools to improve efficiency while keeping professional judgment and ethics central.
Workforce & Education
Population unclear Setting unclear
Efficiency in Action: How AI Drives Advances in Nursing Professional Development.
Journal for nurses in professional development · 2026
This article discusses how AI can support nursing professional development practitioners in designing education, communicating change, and managing workflows. It highlights practical AI uses that may reduce rework, improve consistency, and expand educator capacity while maintaining ethical safeguards and human oversight. This matters because it points to ways AI might improve efficiency without compromising clinical judgment or professional standards.
Workforce & Education
Population unclear
Navigating the Artificial Intelligence Integration Challenge: One Nursing School's Journey From Uncertainty to Systematic Action.
Nurse educator · 2026
One nursing school describes how its faculty and staff addressed the lack of clear guidance for integrating AI into nursing education. The team reviewed existing AI literacy frameworks, selected the Digital Education Council framework, and adapted an AI use framework to build a phased plan covering foundational literacy, teaching alignment, and course-level guidance. This approach may help other nursing programs build structured, discipline-specific AI integration while balancing different stakeholder views.
Workforce & Education
Population unclear Setting unclear
Beyond the Prompt: Leveraging Generative AI and Tanner's Model to Decode Clinical Logic in Undergraduate Nursing.
Nurse educator · 2026
This article describes a teaching approach for undergraduate nursing students that combines Generative AI with Tanner's Clinical Judgment Model to build clinical reasoning skills. Students use an 'If This, Then That' framework and AI as a 'thinking partner' to practice noticing, interpreting, responding, and reflecting on clinical scenarios in a low-stakes setting. The approach aims to help students move from memorization to pattern recognition, which may better prepare them for real clinical situations.
Workforce & Education
Population unclear
Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy.
Nurse educator · 2026
Researchers compared four generative AI models on an 88-item ECG exam and then tested one model, GoodNurse, in a nursing ECG course. GoodNurse had the highest accuracy (85.3%), fewest waveform errors, and best cost-efficiency; course students who used it scored higher (95.1%) than nonusers (88.8%). This suggests domain-specific AI tools may support ECG learning and could inform future nursing education technology choices.
Workforce & Education
Population unclear
Eroding scholarly integrity: Confronting the misuse of generative AI in nursing education.
Nurse education today · Sep 2026
This paper examines a case in undergraduate nursing education where a student submitted fabricated citations and DOIs and misattributed authorship, using generative AI tools like ChatGPT. The author argues this type of error differs from traditional plagiarism, is hard to detect, and reveals gaps in students' understanding of evidence-based scholarship. Nursing programs need AI literacy training, updated integrity policies, and stronger citation skills to protect scholarly standards.
Workforce & Education
Population unclear
Shaping the Future of Radiography Education: Lessons From ChatGPT and Generative AI.
Journal of Medical Radiation Sciences · Sep2
This narrative review looks at how ChatGPT and other generative AI tools are used in radiography education, covering image critique, communication training, simulation, and reflective practice. The review finds AI can support structured learning and self-assessment, but it lacks emotional depth, relational nuance, and professional judgment. This matters for nursing informatics because it highlights how AI's educational value depends on careful framing, educator oversight, and learner critical thinking, not just the technology itself.
Workforce & Education
Setting unclear
The Relationship Between Artificial Intelligence Literacy and Attitudes Toward Artificial Intelligence Among Nursing Students.
Journal of Education & Research in Nursing / Hemşirelikte Eğitim ve Araştırma Dergisi · Sep2
This cross-sectional study surveyed 478 nursing students to examine how AI literacy relates to attitudes toward AI. Students showed generally positive AI literacy and attitudes, with higher literacy linked to more positive attitudes; scores also varied by academic year and prior AI knowledge. This suggests nursing education programs could consider building AI literacy to help prepare students for future practice.