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AI-assisted case builder: Making PBL more scalable and adaptable.

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Developing medical education cases is hard. AI-powered tools like the PBL Case Builder can help educators create problem-based learning (PBL) cases more efficiently, reducing workload and improving consistency.

Keywords:
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Area of Science:

  • Medical Education
  • Artificial Intelligence in Education

Background:

  • High-quality problem-based learning (PBL) case development is resource-intensive and challenging for medical institutions.
  • Faculty time constraints impact the generation of sufficient cases for active learning.

Purpose of the Study:

  • To introduce an AI-assisted tool, the PBL Case Builder, to streamline the creation of medical education cases.
  • To reduce faculty workload and enhance the efficiency and consistency of PBL case development.

Main Methods:

  • Developed a customized ChatGPT application, the PBL Case Builder.
  • The builder requires educators to input target audience, curriculum context, core topic, and complexity.
  • The tool generates cases with progressive triggers, learning objectives, and tutor prompts for refinement.

Main Results:

  • The PBL Case Builder ensures contextualization and pedagogical alignment of generated cases.
  • Educator roles shift from case authors to reviewers, significantly reducing workload.
  • AI-assisted case building enhances efficiency, consistency, and transparency in pedagogical design.

Conclusions:

  • AI-assisted case building offers a viable solution to challenges in medical education.
  • This approach streamlines case development, promotes adaptability, and improves pedagogical transparency.
  • Future research should assess educational impact, user perceptions, and explore global scaling through repositories.