The Use of Artificial Intelligence in Designing Case-Based Learning to Promote Analytical Thinking Among Nursing Students
Keywords:
Artificial Intelligence, Critical Thinking, Case-Based Learning, Nursing Education, ChatGPTAbstract
In the 21st century, nursing education requires a transformation in learning approaches to develop nursing students’ critical thinking skills and prepare them for the rapidly evolving and complex healthcare system. This article aims to present an instructional approach integrating Artificial Intelligence (AI), particularly ChatGPT, in the design of Case-Based Learning (CBL) to enhance critical thinking among nursing students. Drawing from Constructivist Learning Theory and Critical Thinking Theory, this paper explores how AI can simulate realistic clinical scenarios, generate open-ended questions, adjust case complexity, and provide immediate feedback. Literature reviews indicate that AI-enhanced learning significantly improves academic performance, student engagement, and critical thinking skills. However, concerns remain regarding the reliability of AI-generated content, the potential erosion of students’ clinical judgment, and ethical implications. The paper emphasizes the instructor's role in supervising, tailoring, and validating AI-generated learning content to ensure alignment with course objectives and learning outcomes. Rather than replacing human reasoning, AI should be a supportive tool that facilitates deep learning and encourages reflective clinical decision-making. Integrating AI with CBL is thus a promising strategy for fostering analytical and reflective competencies in nursing students, equipping them with the cognitive agility and judgment required to meet the challenges of modern healthcare practice.
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