Deep Dive into Nursing Education topics using AI

Healthcare AI is being marketed as a safety net, but the legal and clinical risk often lands on the nurse. Grounded in the American Academy of Nursing's position statement on AI in healthcare, this episode unpacks why AI literacy is a patient-safety competency, not a software tutorial: the difference between hallucination and algorithmic drift, why one-time training fails, and what hold-harmless clauses mean for the clinician signing the chart.
 
Link to position statement: https://aannet.org/page/AI-position-statement-2026
Link to Webinar on 17 June 2026: https://www.youtube.com/watch?v=Q_t4-y-BG8w
 

In 2021, the World Health Organization published its foundational guidance, Ethics and Governance of AI for Health-WHO Guidance, establishing the international consensus on algorithmic safety, equity, and human oversight. Approximately five years later, the rapid proliferation of generative models and predictive analytics in clinical environments has transformed these guidelines from a forward-looking policy statement into an immediate curricular necessity. This podcast explores the importance of this guidance for nurse educators. 
Link to obtain full publication: https://www.who.int/publications/i/item/9789240037403

This podcast addresses the findings of the American Nurses Association (ANA) think tank dedicated to the integration of artificial intelligence in nursing. The collaborative event brought together diverse experts to establish ethical guardrails and ensure that technology enhances rather than replaces human clinical judgment. The reports identify significant hazards, such as algorithmic bias, unclear legal liability, and the potential for increased cognitive burden on staff. The podcast explores why this information is important to nurse educators.
Link: https://www.nursingworld.org/news/news-releases/2026-news-releases/american-nurses-association-calls-for-nurse-led-guardrails-on-artificial-intelligence-in-healthcare/

A deep dive into the 2026 Stanford HAI AI Index Report's education findings, what they reveal about how students are really using AI, and what nursing educators need to do now.
Link to report: https://hai.stanford.edu/ai-index/2026-ai-index-report (Chapter 7 focuses on Education)

This episode is a focused synthesis of current evidence on AI in nursing education. Within this deep dive, benefits, risks, and practical implications are examined for educators navigating AI integration while protecting the core of clinical reasoning. The discussion pulls from 17 AI-themed articles from the 1st Quarter 2026 of Teaching and Learning in Nursing, the official journal of the Organization for Associate Degree Nursing.

As generative AI becomes a primary tool in the classroom, we face a critical choice: are we augmenting human intelligence or outsourcing it? This episode explores through a debate format, the "Performance Paradox,” the phenomenon where AI allows students to complete tasks more efficiently while potentially bypassing the "desirable difficulties" necessary for deep learning. We dive into the psychological nuances of "metacognitive laziness" and the "illusion of competence," discussing how educators can move from cognitive atrophy to genuine augmentation.
Source: Lodge J. M. and Loble L (2026). Artificial intelligence, cognitive offloading and implications for education, University of Technology Sydney, doi.10.71741/4pyxmbnjaq.31302475

Artificial intelligence is accelerating faster than public perception. In this episode, we examine the emerging “intelligence explosion” and why technical capability alone is not the real risk. Drawing on recent analysis and Anthropic’s AI Fluency Index, we explore how polished AI outputs can quietly suppress critical thinking, amplify automation bias, and create new hazards for professional judgment. This conversation reframes AI fluency as an intentional cognitive skill, not a passive byproduct of exposure, with specific implications for education, healthcare, and workforce readiness.
Anthropic's Report: https://www.anthropic.com/research/AI-fluency-index?utm_source=substack&utm_medium=email
Matt Shumer's Essay: https://shumer.dev/something-big-is-happening

Generative AI is increasingly used in nursing education, but it is not neutral. In this short episode explores how bias enters AI systems, why it matters for teaching clinical judgment and equity, and how educators can prepare students to critically evaluate AI-supported tools used in education and practice.
Source:
Teaching AI Ethics: A Guide for Educators Copyright © 2026 by Leon Furze Published by Leon Furze, leonfurze.com First Edition ISBN (PDF): 978-1-7645082-0-9
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

The 2026 RN Test Plan is more than a testing document. It reflects how nursing itself is evolving. This episode connects the updated language on health equity, infection prevention, digital confidentiality, and workplace safety to real teaching decisions in prelicensure programs.
NCSBN 2026 RN Test Plan: https://www.nclex.com/test-plans.page

AI in healthcare is no longer a single story. It is a set of diverging strategies with very different consequences for nursing education.
In this episode, we break down how Anthropic, OpenAI, and Google are each approaching healthcare AI. One is optimizing clinical workflows. One is empowering patients directly. One is pushing the frontier of multimodal diagnostics.
For nurse educators, this matters immediately. Students will not encounter “AI” as a generic tool. They will encounter AI-generated documentation that requires auditing, patient-facing insights that demand interpretation, and increasingly automated diagnostics that intensify the nurse’s role as a synthesizer and safety guardrail.
This conversation is about moving beyond awareness toward true AI literacy. Not teaching nurses to trust AI. Teaching them when, how, and why to question it.