Advanced AI Training For Nurses, Midwives and Students

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Overview

Master AI & Machine Learning for 50% Off

Go under the hood of AI — neural networks, real-world applications & more. Designed by UNSW experts.

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Designed for nurses, midwives, and students at all career stages, this advanced training equips healthcare professionals with the critical frameworks and practical protocols needed to navigate AI integration in clinical and academic settings responsibly and safely. Develop independent clinical reasoning before engaging with AI outputs by applying the Student Independent Assessment Protocol (SIAP), and learn to critically evaluate how early AI dependency can undermine skill development in student nurses and midwives. Master structured escalation protocols for situations where AI outputs conflict with clinical observation, and understand appropriate documentation standards for AI-augmented placement portfolios. Examine how AI substitution in academic tasks contributes to reasoning poverty in graduating clinicians, and apply level-specific AI use frameworks that preserve scholarly integrity and clinical competence. Build a career development strategy tailored to an AI-transformed healthcare workforce, including pathways into emerging roles such as clinical informatics, AI governance, and digital health education. Apply the Independent First Protocol and a 5-step documentation audit to maintain defensible clinical records in AI-augmented ward environments, and evaluate AI monitoring systems using a 5-question evidence evaluation framework to manage alert fatigue and reduce patient deterioration risk. Develop active human oversight strategies that keep clinical judgment central to patient care decisions. Engage with the Nursing Advocacy Protocol for algorithmic bias and evaluate midwifery-specific AI risks across intrapartum monitoring, reproductive decision-making, and postpartum care to uphold patient rights and cultural safety. Design nurse-led AI governance strategies and position AI literacy as a professional competitive advantage from new graduate through to nursing leadership. Critically assess the ethical risks of AI deployment in community, disability, Indigenous, and migrant health contexts, and formulate a personal action plan for becoming an ethically discerning practitioner capable of overriding algorithmic recommendations based on clinical intuition, cultural context, and recipient wishes. Apply the Medication AI Safety Principle to maintain independent pharmacological reasoning across AI-assisted prescribing, administration, and monitoring, with clear boundaries for both students and practitioners. Identify clinical risks in AI-generated interprofessional communications, distinguish simulation AI from clinical AI, and ensure safe patient care is maintained during digital system failures — building a foundation where clinical competence is never dependent on system availability.

Syllabus

Apply the Student Independent Assessment Protocol (SIAP) to form and document independent clinical hypotheses before consulting AI outputs, and critically evaluate the impact of early AI dependency on student skill development.
Apply a structured student escalation protocol when AI outputs conflict with clinical observation and evaluate appropriate documentation standards for AI-augmented placement portfolios.
Evaluate how AI substitution in clinical academic tasks produces reasoning poverty in graduating nurses and midwives, and apply a level-specific AI use framework to preserve scholarly and clinical development.
Design a level-specific career development strategy for an AI-transformed healthcare workforce and evaluate emerging AI-related nursing and midwifery roles including clinical informatics, AI governance, and digital health education pathways.
Apply the Independent First Protocol and 5-step documentation audit to maintain independent clinical reasoning and produce legally defensible records in AI-augmented ward environments.
Evaluate AI monitoring systems using the 5-question evidence evaluation framework and apply active human oversight strategies to manage alert fatigue and patient deterioration risk.
Apply the Nursing Advocacy Protocol for algorithmic bias and evaluate midwifery-specific AI risks in intrapartum monitoring, reproductive decision-making, and postpartum care to protect patient rights and cultural safety.
Design a nurse-led AI governance engagement strategy and apply a career-stage framework to position AI literacy as a professional competitive advantage from new graduate through to nursing leadership roles.
Critically evaluate the ethical risks of AI deployment in community, disability, Indigenous, and migrant health contexts, and formulate a personal action plan for becoming an ethically discerning user of AI who can override algorithmic recommendations based on clinical intuition, cultural context, and recipient wishes.
Apply the Medication AI Safety Principle to maintain independent pharmacological reasoning across AI-assisted prescribing, administration, and monitoring, while articulating student-specific and practitioner-specific safety boundaries.
Identify the clinical risks in AI-generated interprofessional communications, distinguish simulation AI from clinical AI, and maintain safe patient care during digital system failures ensuring clinical competence is never dependent on system availability.