Background: Artificial intelligence is entering maternal and community health faster than the workforce is being prepared to use it. Predictive risk scores, fetal monitoring algorithms, ambient documentation, and triage chatbots now sit inside everyday midwifery practice. Yet adoption is treated as a technology problem, when in fact it is a trust problem. Clinicians 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 midwives and nurses to question, verify, and lead the AI tools used in maternal 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 maternal health touchpoint, from intake risk scoring to postpartum follow-up.
The Competency Domains
Learning Objectives: After this session, attendees will be able to: (1) describe trust in AI as a measurable competency rather than an attitude; (2) apply a question, verify, and lead approach to AI tools in their own maternal or community health setting; and (3) identify their role as the human safeguard against algorithmic error and bias.
Conclusion: Safe AI in maternal 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 and midwifery leaders a concrete, transferable way to protect patients while embracing innovation.