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Improving Clinical Reasoning Skills With Machine Learning K-Means Algorithm.

Nadia Hachoumi1, Mohamed Eddabbah2, Ahmed Rhassane El Adib1,3

  • 1Biosciences and Health, Faculty of Medicine and Pharmacy of Marrakesh, Cadi Ayyad University, Marrakesh, Morocco.

Journal of Evaluation in Clinical Practice
|August 20, 2025
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Summary

Machine learning, using K-means clustering, effectively identifies student errors in clinical reasoning. This enables personalized educational interventions to improve learning and address specific cognitive needs in health sciences.

Keywords:
clinical reasoninghealth sciencesintelligent machine learningk‐means algorithm

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Area of Science:

  • Health Sciences Education
  • Artificial Intelligence in Education
  • Cognitive Science

Background:

  • Enhancing clinical reasoning is vital for training competent health practitioners.
  • Identifying specific reasoning deficits in students is a persistent educational challenge.
  • Current assessment methods may not fully capture the nuances of clinical problem-solving.

Purpose of the Study:

  • To investigate the efficacy of machine learning, specifically K-means clustering, in detecting technical and conceptual errors in student problem-solving.
  • To determine the extent to which machine learning facilitates personalized educational interventions for reasoning deficits.
  • To explore the integration of machine learning with established educational frameworks like Bloom's taxonomy.

Main Methods:

  • Developed a novel method combining K-means clustering with Bloom's taxonomy to classify students based on clinical reasoning skills.
  • Grouped learners into clusters representing different cognitive levels, from basic recall to complex clinical reasoning.
  • Utilized these clusters to inform the design of targeted pedagogical strategies.

Main Results:

  • K-means clustering revealed performance patterns in student behavior beyond traditional assessment capabilities.
  • The approach enabled educators to understand student reasoning abilities on a continuum, facilitating individualized learning paths.
  • Interventions based on these insights can be implemented at scale for targeted instruction, effectively closing reasoning gaps.

Conclusions:

  • The synergy of machine learning (K-means clustering) and educational theory (Bloom's taxonomy) enables scalable, evidence-based, personalized clinical training.
  • Machine learning offers a powerful tool for tailoring teaching and learning experiences across diverse cognitive domains.
  • This approach advances the potential for individualized support in health sciences education.