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Related Experiment Video

Updated: Jul 11, 2026

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

Transforming Health Care AI Education Through Micro-learning: A Novel Partnership Model for Nursing Workforce

Amy K McCarthy¹, Jonathan D Agnew^1,2, Jill Price³

¹Hippocratic AI, Palo Alto, CA.

Computers, Informatics, Nursing : CIN

|April 2, 2026

Summary

This study shows that a micro-learning program significantly improved artificial intelligence (AI) literacy among nursing students and faculty. The nursing-specific AI education demonstrated high satisfaction and knowledge gains, addressing a critical need in healthcare.

Keywords:

Artificial intelligence Computer literacy Continuing Curriculum Education Faculty development Nursing

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Last Updated: Jul 11, 2026

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

Area of Science:

Healthcare Education
Artificial Intelligence in Medicine
Nursing Informatics

Background:

Healthcare professionals require urgent artificial intelligence (AI) literacy due to rapid technological integration in clinical settings.
A significant gap exists in nursing-specific educational resources for AI.
The transformative potential of AI in healthcare necessitates targeted training for nursing professionals.

Purpose of the Study:

To evaluate the effectiveness of a novel micro-learning AI education program tailored for nurses.
To assess knowledge gains, participant satisfaction, and the applicability of AI concepts in nursing practice.
To determine the impact of an academic-industry partnership in delivering AI education.

Main Methods:

An 11-course micro-learning program (4-5 hours each) was developed and delivered, covering foundational, application, and advanced AI competencies.
The curriculum included nursing-specific content on professional scope and leadership in AI.
Data from 478 students and faculty (612 course completions) were analyzed, focusing on enrollment, learning outcomes, and satisfaction.

Main Results:

Participants demonstrated significant knowledge gains in AI literacy (Cohen's d=0.65, P<.001).
High satisfaction scores were reported by both students (mean=4.58/5.0) and faculty (mean=4.67/5.0).
Registered nurses constituted 49% of participants, and 99% of faculty committed to applying their learning.

Conclusions:

The micro-learning AI education program effectively addresses critical gaps in healthcare AI literacy for nurses.
The nursing-specific curriculum, developed via an academic-industry partnership, is scalable and practical.
This model provides a replicable framework for preparing the healthcare workforce for AI-enhanced delivery.