Title : Trust is a competency: Preparing the nursing workforce to question, verify, and lead AI
Abstract:
Background: Artificial intelligence is entering clinical care faster than the nursing workforce is being prepared to use it. Predictive risk scores, early warning algorithms, ambient documentation, and triage chatbots now sit inside everyday nursing practice. Yet adoption is treated as a technology problem, when in fact it is a trust problem. Nurses either over rely on tools they do not understand or reject them outright. Both responses put patients at risk. Trust should not be a feeling that automation earns by default. It should be a teachable, assessable professional competency.
Aim: This presentation reframes trust as a core competency and offers a practical framework that prepares nurses to question, verify, and lead the AI tools used in clinical care, rather than passively consume their output.
Approach: Drawing on an established nursing AI competency framework and current evidence on automation bias and algorithmic safety, the framework defines what trustworthy engagement looks like at the bedside and maps each capability to a concrete nursing touchpoint, from admission risk scoring to discharge planning and follow up. This approach is grounded in the American Nurses Association position on the ethical use of artificial intelligence in nursing practice and the 2025 Code of Ethics for Nurses, which call on nurses to critically question the assumptions behind these technologies and to keep clinical judgment central.
The Competency Domains:
• Question: Recognizing what an AI tool can and cannot do, and identifying when its recommendation conflicts with clinical judgment.
• Verify: Checking AI output against the patient in front of you, spotting automation bias, and knowing when to override.
• Lead: Advocating for safe implementation, surfacing bias that harms underserved patients, and giving frontline feedback that shapes procurement and policy.
Audience Take Away Notes:
• Attendees will be able to describe trust in AI as a measurable nursing competency rather than an attitude.
• Attendees will be able to apply a question, verify, and lead approach to AI tools in their own clinical setting.
• Attendees will be able to identify their role as the human safeguard against algorithmic error and bias.
• Nurse educators and leaders can use the framework to build AI competency into orientation, continuing education, and competency assessment.
• The model is transferable across practice areas and gives faculty and researchers a structure for teaching and studying safe AI adoption in nursing.
Conclusion: Safe AI in nursing care will not come from better algorithms alone. It will come from a workforce equipped to challenge them. Positioning trust as a competency gives nursing leaders a concrete, transferable way to protect patients while embracing innovation.
