Global Nursing AI Alliance and American Board of Nursing Artificial Intelligence Present Nursing AI Research at AAAI

Vanessa Riley, GNAA President presents a paper on building an AI Ready Global Nursing Workforce

Dr. Delaney W. La Rosa, Introduces Symposium

Poster Presentations

The inaugural symposium examined how to measure nursing AI competency and evaluate human oversight of clinical AI.

ARLINGTON, VA, UNITED STATES, November 11, 2026 /EINPresswire.com/ -- The Global Nursing AI Alliance (GNAA) and the American Board of Nursing Artificial Intelligence (ABNAI) presented five papers at the inaugural Agentic and Trustworthy AI for Health and the Global AI-Ready Nursing Workforce symposium (AT-AI4H-NW 2026). The meeting took place November 5–7 at the Westin Arlington Gateway as part of the Association for the Advancement of Artificial Intelligence’s 2026 Fall Symposium Series. Organizers reported that 23 papers were accepted and presented.

“Nurses should help shape clinical AI decisions from the beginning—before procurement, when organizations define their needs and evaluate potential systems. Their expertise is essential to setting practical safety criteria, understanding how a tool will affect care, and identifying risks that may not appear in a vendor demonstration. After implementation, nurses and nursing scientists should continue evaluating whether the system supports sound decisions, how quickly teams can respond to failures, and whether those failures recur.” Vanessa Riley, MSN, RN, FNP-C, eFACHDM, Founder and President, Global Nursing AI Alliance.

ABNAI Nursing AI Competency Framework

Delaney W. La Rosa, EdD, MSN Ed, RN, a teaching professor at Florida State University College of Nursing and an ABNAI director at large, presented “Artificial Intelligence Competencies in Nursing: A Narrative Review of Current Evidence, Frameworks, and Gaps,” co-authored with Jennifer Rossetti, Sally Carlisle, Vanessa Riley, and Simmy King. The authors mapped the literature to ABNAI’s seven competency domains and identified gaps in validated measures of applied AI skills, including equity-related competencies. The symposium agenda linked those competencies to nursing responsibilities from system design through deployment and monitoring.

Simmy King, Delaney W. La Rosa, and Vanessa Riley used the ABNAI framework to develop a 105-item self-assessment for an exploratory cross-sectional study. Riley presented the analysis of responses. Respondents who reported prior AI education rated themselves higher than those without it (2.65 versus 1.80), and data literacy, analytics, and governance was the lowest-rated domain.

GNAA Framework for Global Nursing Workforce Readiness

Riley also presented “Building an AI-Ready Global Nursing Workforce: An Interdisciplinary, Expert-Informed Framework.” The GNAA team combined a targeted literature review with input from 29 contributors with expertise in nursing specialties, informatics, data science, and other disciplines. The authors identified eight themes for workforce readiness, including nursing leadership in AI governance, accountability for clinical decisions, and adaptation to local care settings and resources.

The authors of the GNAA workforce framework are Vanessa Riley, Cory Sessum, Jennifer Ngure, Anthony Conti, Robylin Tweetie Diya, Caroline Bilen, Marife Aczon-Armstrong, Lorrie Davis-Dick, Amy Holton, Faila Santos, Sharonda Davis, Claude Bernard, Rasha Porter, Celia McIntosh, Jordan Howard, Ramkumar Selvaraj, Adrianna Sperkacz, Althea Miller, Jess Schnyders, Sharon Johnson, Bianca Cruickshank, Sara Rogers, Michelle Gilliland, Faith Selchick, Delaney La Rosa, Agezegn Asegi, Kristi Campoe, Lazarus Siafa, and Simmy King.

Nursing AI Education

La Rosa presented two additional papers. One introduced the AI Discernment Framework for Teaching and Learning. The other, co-authored with Beth McManus and Jennifer Rossetti, examined graduate nursing students’ research proficiency after the introduction of an AI research assistant.

“AI is quickly becoming a safety and advocacy issue in nursing. Nurses need to be prepared to understand how these systems influence clinical decision-making and patient outcomes. Further, we need nurses who are prepared to lead when AI systems are designed, evaluated, deployed, and refined,” La Rosa said.

Testing Human Oversight of Clinical AI

In a session chaired by Riley, Zhentao Fan presented work with Zhangfei Yang and Yuheng Zhao that evaluated four safeguards across nine reviewers in a nurse-facing patient deterioration testbed. In a separate paper, Aizierjiang Aiersilan proposed a standard of care for nurse- and physician-facing AI centered on actual clinician review of consequential AI outputs.

Sharonda Davis, RN, GNAA’s co-director of Acute Care and Surgery, co-authored the GNAA workforce framework and served on the symposium organizing committee. “One of the most important discussions at the symposium brought nurses, data scientists, and computer scientists together to consider how clinical and cultural context should shape AI in nursing,” said Sharonda Davis. “We need to test whether an AI recommendation fits the care setting and accounts for the patient’s clinical condition, language needs, and care preferences.”

Riley and La Rosa co-chaired the symposium with Hong Qin of Old Dominion University, Apurva Narayan of Western University, and Catherine Fang of Carnegie Mellon University. Simmy King, president of ABNAI, served on the organizing team. La Rosa also took part in the series’ plenary session for symposium summaries.

Vanessa Riley
Global Nursing AI Alliance Corporation
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