Introduction

Change is inevitable. Some changes can be positive, while others may have negative consequences. Often change brings the potential for both. Individual perceptions usually shape whether we view the anticipated outcomes of a particular change as positive or negative. A perfect example of differing perspectives of a new innovation is the expanding use of artificial intelligence technology in nursing education. Many nurse educators believe this technology has the potential to transform education by providing more personalized and efficient learning experiences for students (DeGagne, 2023). Despite this optimism, others are fearful about the rapid pace of AI innovation and the lack of knowledge related to the potential risks and unintended consequences of this technology (Glauberman, 2023). Wickstrom (2024) suggests that nurse educators may not be integrating AI into their practice at a rapid rate because of a lack of nursing education research in this area.

Hesitancy regarding the faculty’s ability to develop competency in this area also contributes to negativity toward artificial intelligence in nursing education. De Gagne (2023) suggests that faculty may have concerns about the impact of AI on their workload (How long will it take to learn to use AI in the classroom or clinical?) and in their role as faculty (Will AI partly or completely replace my job?).

This hesitancy resonates with me. Although usually interested in trying new things, my limited experience in AI makes me apprehensive of ways that it might be used in nursing education. I suspect that I might not be alone in this concern, so over the next several months, the Collaborative Momentum Blog will attempt to de-mystify the use of AI, so those of us who are hesitant can feel more comfortable in using some form in nursing education.

First, Some Definitions

Below are some basic definitions to get us started.

Artificial Intelligence (AI)– a broad discipline of computer science that aims to develop systems capable of performing tasks that traditionally requires human intelligence (Shepherd, Griesheimer, 2024). AI is an umbrella term for any machine that can replace some aspect of human intelligence. The system uses inputs to reason, learn and process (Wickstrom, 2024). Types of AI include: Non-generative or traditional AI, which creates patterns and makes predictions and excels at analyzing data and performing specific tasks such as spam filtering and medical diagnoses. Generative AI, which focuses on creating new content based on the information used to train it, such as text, images and music (Shepherd, Griesheimer, 2024).

Machine-Learning– Computers can learn without human programing. Learning algorithms make predictions after identifying patterns and trends. Ultimately, they can program themselves through experience. Amazon shopping recommendations and Netflix suggestions are examples of machine-learning (Wickstrom, 2024).

Natural Language processing (NLP)- aims to bridge the gap between humans and machines by enabling them to communicate effectively through natural language. NLP uses advanced algorithms and techniques to process and analyze complex human language (Shepard, Griesheimer, 2024).

Large Language Model (LLM)- an advanced AI system program that is trained on huge data sets from many disparate sources, including the internet, to recognize and generate responses to questions and prompts. ChatGPT is an example of Large Language Model AI (Shepard, Griesheimer, 2024).

Prompt engineering– the deliberate and strategic formulation of instructions given to an AI system to produce the desired result. Prompt engineering works within a generative AI system to allow it to use past interactions to improve future content generation. It is similar to a Google search, except a Google search delivers links to information, and in generative AI, the process involves refining the question or command to ensure clarity and specificity, with the goal of more accurate and relevant responses (Shepard, Griesheimer, 2024).

Neural Network– a series of algorithms that seek to identify relationships in a data set via a process that mimics the way the human brain works.

Prediction Models– predicts best outcomes based on data form previous events, calculating probability of events based on earlier data on similar events and hidden trends. Examples pf nursing practice-related predictions include risk assessment of falls or skin breakdown risks (Wickstrom, 2024).

ChatGPT– an AI chatbot with natural language processing (NLP) which allows a human-like conversation to complete various tasks. This generative AI can answer questions, assist in composing, emails, essays, and code, among other things (https://www.zdnet.com/article/what-is-chatgpt-and-why-does-it-matter-heres-everything-you-need-to-know/).

Ways AI may Impact Students and Faculty

The list below describes ways that AI can enhance student learning and provide assistance to faculty in their work. Continue reading “Artificial Intelligence in Nursing Education: Exploring the Basics”