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Related Concept Videos

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RLC Series Circuit: Problem-Solving

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Related Experiment Video

Updated: Jul 4, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

LLMs in Problem-Based Learning: The Grounding Issue.

Murat Sariyar1

  • 1Bern University of Applied Sciences, IODA Institute, Bern, Switzerland.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can enhance medical problem-based learning (PBL) by summarizing cases and suggesting diagnoses. However, LLMs cannot replace the crucial development of clinical judgment fostered through human interaction in PBL.

Keywords:
groundinglarge language modelsmedical educationproblem-based learning

Related Experiment Videos

Last Updated: Jul 4, 2026

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques
13:44

Project-Based Learning Guidelines for Health Sciences Students: An Analysis with Data Mining and Qualitative Techniques

Published on: December 9, 2022

Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare

Background:

  • Large language models (LLMs) are increasingly integrated into medical education.
  • Problem-based learning (PBL) is a key pedagogical approach in medical training.

Purpose of the Study:

  • To conceptually analyze whether LLMs can replace or merely support PBL.
  • To explore the capabilities and limitations of LLMs in the context of PBL.

Main Methods:

  • Conceptual analysis using a renal-tumor PBL case.
  • Distinguishing between semantic support, representational uncertainty, and situated uncertainty.

Main Results:

  • LLMs effectively provide semantic support and address representational uncertainty by reformulating cases and suggesting diagnostic possibilities.
  • LLMs do not replicate situated uncertainty, which is critical for developing clinical judgment through peer and tutor interaction.

Conclusions:

  • LLMs can significantly augment PBL by offering valuable support in case analysis and information synthesis.
  • LLMs are powerful tools for enhancing medical education but do not replace the essential role of PBL in cultivating clinical judgment.