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Reasoning under uncertainty in graduate health education

A scaffolded framework using large language models

Dados Bibliográficos

ID22166282
AutoresCarlos Ugrinowitsch (0000-0001-8547-419X, Hungarian University of Sports Science), Leonardo Lamas (Universidade de Brasília), Cleiton Augusto Libardi (0000-0002-9003-7610, Universidade Federal de São Carlos)
Ano2026
Volume11
Data de publicação2026-06-30
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Education (JOURNAL)
Identificadores do periódicoISSN: 2504-284X • E-ISSN: 2504-284X
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2026.1857036
OpenAlexW7166725011
IdiomaEN
Referências citadas32

The management of non-communicable diseases increasingly demands reasoning that integrates multiple mechanisms, disciplinary perspectives, and contextual constraints. Although graduate training in the health sciences is highly effective in helping learners apply stabilized evidence through Evidence-Based Practice (EBP), clinical guidelines, and decision pathways, it often gives less explicit attention to how learners should reason when evidence is incomplete, conflicting, or poorly aligned with the problem at hand. These tools compress scientific knowledge into clinical schemas that, through repeated use, support heuristic decision-making in routine contexts. The challenge emerges when evidence is sparse, conflicting, or misaligned with the clinical problem. At that point, schema-based strategies become insufficient, and uncertainty becomes a structural feature of decision-making rather than a temporary lack of data. This Hypothesis and Theory article proposes a pedagogical framework for training cross-silo integrative reasoning under uncertainty in graduate health education. The framework distinguishes three layers of clinical reasoning and operationalizes the third (i.e., the Uncertainty Layer) through five adaptive simulators that can train both horizontal and vertical integration, in which large language models function as constrained epistemic adversaries. A non-normative rubric, intended for developmental monitoring rather than summative judgment of correctness, assesses how learners deal with uncertainty across simulator sessions. The framework does not replace EBP, clinical expertise, or mentorship; it formalizes the reasoning processes required when stabilized evidence reaches its limits, making expert judgment under uncertainty explicit, inspectable, and teachable within graduate health-sciences education

Clinical judgment · Conceptual framework · Discipline · Graduate students · Heuristic · Summative assessment · Clinical Reasoning and Diagnostic Skills · Innovations in Medical Education · Simulation-Based Education in Healthcare

  • The Ecology of Team Science

    Open Access•Daniel Stokols, Shalini Misra et al.•American Journal of Preventive…•2008

  • Fostering the Development of Master Adaptive Learners

    William B Cutrer, Bonnie M Miller et al.•Academic Medicine•2017

  • Evidence-Based Medicine

    Gordon Guyatt, Gordon Guaytt•JAMA•1992

  • Clinical Practice Guidelines and Quality of Care for Older Patients With Multiple Comorbid Diseases

    Cynthia M Boyd, Jonathan Darer et al.•JAMA•2005

  • Bayes Factors

    Robert E Kass, Adrian E Raftery•Journal of the American…•1995

  • Education of clinical reasoning in patients with multimorbidity

    Open Access•Fabrizio Consorti, Maria Carola Borcea et al.•Frontiers in Education•2023

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