Prompt Engineering for Nurse Educators.

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.

The ChatGPT Storm and What Faculty Can Do.

BACKGROUND: ChatGPT, an artificial intelligence-driven, pretrained, deep learning language model, can generate natural language text in response to a given query. Its rapid growth has led to concerns about ethical use in academia. PROBLEM: The exponential rise in the popularity of ChatGPT, and concerns of academic integrity with its use, has raised concerns among faculty for how to best address this issue. APPROACH: Faculty should understand the potential benefits and limitations of ChatGPT and create assignments that emphasize self-reflection, critical thinking, problem solving, and independent learning. Students must be taught how to critically evaluate information and how to make informed decisions. CONCLUSIONS: ChatGPT has the potential to revolutionize nursing education. However, it is critical for faculty to be familiar with its capabilities and limitations in order to foster effective, yet ethical and responsible utilization, while preparing students for the dynamic, rapidly advancing technological landscape in nursing and health care.

Using the Logic Model Framework to Standardize Quality and Rigor in the DNP Project.

BACKGROUND: Since the implementation of the DNP degree, there has been controversy, confusion, and inconsistency about the DNP project. To this day, the profession lacks a consensus of what the DNP project should be. PROBLEM: Despite much in the literature regarding these issues, there is no systematic approach to the development of DNP project guidelines. Consequently, there is now an escalating interest in increasing the quality and rigor of DNP projects. APPROACH: Many issues related to the DNP project can be addressed using a logic model approach and incorporating current best practices. CONCLUSIONS: This article demonstrates how a logic model can be used for the development or improvement of DNP project processes, incorporating best practices and evaluation methods to ensure quality and rigor.