In this episode we discuss the shift to CBE. Tune in to hear expert advice to guide your program forward. Listen on Spotify, Apple Podcasts or YouTube.
Many nursing programs are working to integrate competencies into their curriculum to meet new accreditation standards — but the shift to competency-based education (CBE) can feel overwhelming. In this episode Susy talks with Dr. Jan Jones-Schenk, a nationally recognized expert in competency-based nursing education.
Dr. Jones-Schenk shares practical advice to help nurse educators align curricula with the AACN Essentials, meet accreditation requirements, and support student success through a CBE model. Whether you’re just starting or already deep into the transition, this episode offers clarity, direction, and expert insight you can trust.
Tune in and subscribe Nurse Educators Now on YouTube , Apple Podcasts, or Spotify for expert insights and actionable advice to stay ahead in the ever-evolving world of nursing education.
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).
Building Upon Comprehensive Remediation Programs: Focusing on Individual Student Success in Nursing Education
In May 2021 our Collaborative Momentum Consulting blog featured a discussion on remediation programs for nursing students, titled “Setting Students Up for Success” We addressed the pressing need to develop remediation programs tailored to meet the diverse needs of your entire student body. While we hope these strategies were helpful, we recognize there is also a need for individualized support and remediation for nursing students facing academic challenges. Often, educators find themselves assisting individual students who demonstrate trouble achieving success in faculty-made or standardized tests or other assignments.
This month we aim to equip educators with practical approaches to assist individual students in identifying areas of difficulty, understanding specific knowledge gaps and developing personalized improvement plans. Continue reading “Effective Remediation for Nursing Students”
The impact of the coronavirus is being felt everywhere. As I write this, schools, colleges, and universities across the country are temporarily closed and planning to move classes to online instruction when they reopen. For faculty in nursing programs, this directive is manageable, as many face-to-face courses involve some level of interaction online. However, as I talk to faculty from across the country, I hear concerns regarding the loss of a caring learning environment among students and faculty in courses delivered only online. I also hear concerns that implementing teaching-learning strategies to stimulate critical thinking is more difficult in an online environment that in face-to-face instruction. Perhaps during this national emergency it would be helpful for all of us to think about “best practices” for online instruction, particularly those that involve creating a caring environment that encourages critical thinking. Continue reading “Online Learning: Creating a Caring Environment that Encourages Critical Thinking”
by Susan Sportsman, PhD, RN, ANEF, FAAN and Cathy Converse
“Courage is the first of human qualities because it’s the quality that guarantees the others.” –Aristotle
Introduction
Leadership is on the hearts and minds of most of us as we navigate the complexities of our personal and professional lives. Although volumes have been written about effective leadership using a variety of perspectives, the need for courage in times of conflict and discord, particularly in situations of perceived high risk, seems particularly relevant. Few environments represent higher risk than health care. Continue reading “The Case for Courageous Leadership”
