The challenge of preparing novice nurses to deliver safe, effective care is not new. New graduates, faculty and practice partners alike have all recognized that safety should be the hallmark of a nurse entering the profession- regardless of their practice setting. However, in today’s complex health care environment, accurately assessing whether a novice nurse is truly prepared is no simple task. With increasing patient acuity, expanding scopes of practice, and evolving healthcare roles, nursing education must continuously adapt to ensure graduates have the necessary capabilities for real-world practice. For the last 25 years, a variety of efforts have been made by the profession to quantify specific competencies essential for nurses. Table A gives examples of efforts to evaluate the behaviors of a competent nurse in various roles and settings.
Table A: Selected Efforts to Support Integration of Nursing Competency into Practice
Current literature suggests the Nurse Competency Scale (both the original and shortened version) is the most commonly used self-assessment instrument to measure the generic competence of registered nurses. The scale, based on Benner’s from Novice to Expert model, is used to evaluate the nurse’s ability to integrate knowledge, skills, attitudes, and values in specific context. The nursing behaviors identified in the scale includes the nurse’s helping role, teaching-coaching, diagnostic functions, managing situations, therapeutic interventions, ensuring quality, and worker role. (Meretoja, et. al, 2004).
The Shift Toward Competency Based Nursing Education
In response to the 2021 of American Association of Colleges of Nursing (AACN) The Essentials: Core Competencies for Professional Nursing Education, many baccalaureate and graduate nursing programs seeking accreditation by the Commission on Collegiate Nursing Education (CCNE) today are in the process of integrating Competency Based Education into their curriculum. AACN recognizes that complete integration of these competencies into baccalaureate and graduate education will take time. As a result, Continue reading “Competency-Based Education (CBE)”
In this episode we discuss the pros and cons of ChatGPT in the classroom and unpack practical strategies that nurse educators can use today.
Curious about how AI can streamline the workload for nurse educators? Join us as we talk with Dr. Hovar about how AI can support and challenge traditional teaching. We’ll explore the pros, cons, and practical ways to use ChatGPT in your program. Listen on YouTube or Spotify.
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Analyzing NGN NCLEX Exam Results: Key Insights for Educators
Susan Sportsman, PhD, RN, ANEF, FAAN
The National Council of State Boards of Nursing hosts an annual meeting to provide nurse educators with information regarding the results of NCLEX-RN and PN examinations. On September 12th, I joined the virtual audience to hear the most recent updates about the implementation of the NGN NCLEX. For those unable to attend the conference, I’m providing a brief update about this meeting as a guide to help you stay ahead in preparing students for future NCLEX Exams. Continue reading “Analyzing NGN NCLEX Exam Results”
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).
Over the last several years, as we prepared for the implementation of the Next Generation NCLEX, the Collaborative Momentum Blog has intermittently focused on strategies to write test questions that mirror clinical practice. Now that the NGN has been implemented, we believe it might be helpful to review some of the types of questions the students must answer. This month we will focus on one of the clinical judgment standalone questions, the Trend question. This type of question provides an opportunity for the test-taker to Continue reading “Writing NGN-Style Trend Questions”
