Nurse Educator最新文献

Background: The performance of GPT-4 in nursing examinations within the Chinese context has not yet been thoroughly evaluated.

Objective: To assess the performance of GPT-4 on multiple-choice and open-ended questions derived from nursing examinations in the Chinese context.

Methods: The data sets of the Chinese National Nursing Licensure Examination spanning 2021 to 2023 were used to evaluate the accuracy of GPT-4 in multiple-choice questions. The performance of GPT-4 on open-ended questions was examined using 18 case-based questions.

Results: For multiple-choice questions, GPT-4 achieved an accuracy of 71.0% (511/720). For open-ended questions, the responses were evaluated for cosine similarity, logical consistency, and information quality, all of which were found to be at a moderate level.

Conclusion: GPT-4 performed well at addressing queries on basic knowledge. However, it has notable limitations in answering open-ended questions. Nursing educators should weigh the benefits and challenges of GPT-4 for integration into nursing education.

Background: Evidence-based practice (EBP) competency is an expected outcome of baccalaureate education; however, research indicates nursing graduates enter practice with competency gaps.

Purpose: The purpose of this study was to explore the challenges accelerated baccalaureate students experience when learning the steps of EBP.

Methods: A qualitative, descriptive design was used to identify bottlenecks to student learning.

Results: Learning the steps of EBP in an accelerated format was challenging for students. Data analysis revealed 3 types of bottlenecks that stalled learning: cognitive, procedural, and emotional.

Conclusions: Faculty build EBP competency by making explicit the steps that help students progress. Scaffolded learning, collaborative partnerships, and reflective practices are recommended to support student learning in accelerated formats.

Background: Escape room simulations are an innovative teaching methodology that may be useful in addressing pediatric-specific content and clinical placement limitations of generalist nursing programs.

Problem: This project studied if a pediatric escape room simulation improved students' knowledge and confidence in pediatric nursing to support the development of the pediatric nursing workforce.

Approach: A pediatric escape room simulation was developed for a large Midwestern university's prelicensure nursing program. Students completed surveys to assess knowledge and confidence in pediatric nursing. Simulation quality was assessed using the Simulation Learning Effectiveness Inventory.

Outcomes: Students' (N = 37) knowledge and confidence in pediatric nursing concepts significantly increased after the simulation. Students responded positively to the simulation and recommended it be included in the undergraduate curriculum.

Conclusions: The pediatric escape room simulation offers a promising alternative for students to gain pediatric-specific knowledge and confidence for prelicensure nursing programs in the context of limited pediatric clinical placements.

Background: Many nursing faculty lack formal training in item writing and test construction.

Purpose: This integrative review synthesizes the evidence to gain a comprehensive understanding of the developmental needs and interventions employed to help nursing faculty gain skills to develop course examinations.

Methods: Original research and non-research reports published since 2012 were included. Independent quality appraisals were completed based on the Johns Hopkins Nursing Evidence-based Practice Model. Data were analyzed using the constant comparison method.

Results: Nineteen reports met inclusion criteria. Themes related to test development skills were standards support practice, educational preparation, evolving technologies, and writing for clarity. Themes related to faculty development were continuous improvement, professional development plans, peer and expert collaboration, and up-to-date resources.

Conclusions: Nursing programs need to establish methods to ensure faculty use best practices and are competent in test development. Nursing needs future research to understand optimal faculty development approaches.

Background: Recent research at a single-purpose nursing institution has suggested a means to authenticate student writing by distinguishing it from artificial intelligence (AI)-generated text through the detection of key terms.

Purpose: The purpose was to replicate and expand the research that identified key terms present in student writing but absent from AI-generated text.

Methods: A total of 5 generative AI writing tools were fed prompts to collect 14 787 words. Using the Search function on word processing software, the frequency of the terms, because, since, so, then, thing, think , and too , was measured and compared against earlier published findings from AI and students.

Results: The replication study was successful for the terms since, then, thing, think, and too.

Conclusions: Measuring key term frequency may be a path to authenticate student writing. While no tool can provide certainty of original authorship, the absence of key terms in a student submission may suggest AI authorship.

Background: The integration of generative artificial intelligence (AI) tools like OpenAI's ChatGPT into nursing education marks a transformative advance in personalized learning and interactive engagement.

Problem: Variability in faculty's experience with AI outputs highlights the need for well-crafted prompts that align with educational objectives, maximize learning outcomes, and ensure contextual relevance. Effective prompting is a key to eliciting accurate, relevant responses from AI, fostering a dynamic learning environment that bolsters student comprehension of complex topics.

Approach: This article examines the critical role of prompt engineering in optimizing AI-generated content's effectiveness within academic settings. With a detailed guide and strategies specifically designed for nursing education, the article prepares faculty to proficiently use generative AI.

Conclusions: By mastering prompt engineering, educators can leverage AI tools as powerful aids, potentially significantly enhancing teaching effectiveness, work efficiency, and student learning outcomes.