LLMs in Problem-Based Learning: The Grounding Issue...24th International Conference of Informatics, Management and Technology in Healthcare (ICIMTH), July 3-5, 2026, Athens, Greece.

Large language models (LLMs) are increasingly used in medical education, including in problem-based learning (PBL). Their ability to summarize cases, generate differential diagnoses, and structure discussion raises the question of whether they might eventually replace PBL rather than merely support...

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Publicado en:Studies in Health Technology & Informatics Vol. 338; pp. 698 - 703
Autor principal: SARIYAR, Murat
Formato: proceedings Journal Article
Publicado: Sage Publications Inc. 2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2026
      vid: 338
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: LLMs in Problem-Based Learning: The Grounding Issue...24th International Conference of Informatics, Management and Technology in Healthcare (ICIMTH), July 3-5, 2026, Athens, Greece.
      aug:
        au: SARIYAR, Murat
        affil: Bern University of Applied Sciences, IODA Institute, Bern, Switzerland
      sug:
        subj:
          Natural Language Processing
          Problem-Based Learning
          Kidney Neoplasms Education
          Chatbot
          Teaching
          Education, Medical
          Congresses and Conferences Greece
          Greece
          Human
          Judgment
          Uncertainty
          Decision Support Techniques
          Diagnosis, Differential
          Medical Informatics
      ab: Large language models (LLMs) are increasingly used in medical education, including in problem-based learning (PBL). Their ability to summarize cases, generate differential diagnoses, and structure discussion raises the question of whether they might eventually replace PBL rather than merely support it. This paper offers a conceptual analysis of that question, using a renal-tumor PBL case derived from recent ChatGPT-assisted teaching research as an illustrative use case. It distinguishes among semantic support, representational uncertainty, and situated uncertainty. LLMs can contribute substantially at the first two levels: they can reformulate cases, identify missing information, keep multiple diagnostic possibilities in play, and suggest coherent next steps. What they do not reproduce is the situated uncertainty through which PBL forms judgment in interaction with peers and tutors. More tools, retrieval, modalities, or sensor input may enrich representation, but they do not by themselves close this gap. The paper therefore argues that LLMs can augment PBL in important ways but do not replace its role in the formation of clinical judgment.
      pubtype: Academic Journal
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        proceedings
        Journal Article
      ougenre: Article
    language: English
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