Using ChatGPT to Create Engaging Problem-Based Learning Scenarios in Anatomy: A Step-By Guide

Abstract

Background: Problem-based learning is widely recognized for its ability to foster active learning and critical thinking in medical education. However, creating effective problem-based learning scenarios demands a high level of expertise. Leveraging the natural language capabilities of ChatGPT, educators can now receive assistance in designing engaging anatomy problem-based learnings. Objective: This paper aims to provide a comprehensive guide on collaborating with ChatGPT to generate ideas, develop content, and create supporting materials for anatomy problem-based learning scenarios. Material and methods: Our methodology involved an analysis of literature on problem-based learning best practices and experimentation on content creation using ChatGPT. The outputs were refined based on valuable feedback obtained from both educators and students. Results: This guide emphasizes crucial aspects such as defining clear learning objectives, ensuring academic rigour, and aligning the problem-based learning scenarios with the curriculum. By harnessing ChatGPT's conversational abilities, educators can collaboratively co-create problem-based learning scenarios that are engaging and effective. Conclusion: This human-artificial intelligence collaborative approach to anatomy problembased learning design underscores the importance of maintaining oversight over the content generated by ChatGPT. Further research is necessary to quantify the impact of ChatGPT as a supplementary resource. Purposeful integration of ChatGPT, in alignment with pedagogical goals, has the potential to enhance engagement and learning outcomes, particularly for digitally native students.