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  3. Developing And Validating A Coding Scheme For Clinical Reasoning In History Taking Using Generative Ai-based Virtual Patients: Systematic Text Condensation Approach.
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  3. Developing And Validating A Coding Scheme For Clinical Reasoning In History Taking Using Generative Ai-based Virtual Patients: Systematic Text Condensation Approach.

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Developing and Validating a Coding Scheme for Clinical Reasoning in History Taking Using Generative AI-Based Virtual

Naping Chen1, Luzhen Tang2, Yang Liu1

  • 1Department of Clinical Skills Training Center, Shantou University Medical College, Shantou, Guangdong, China.

JMIR Medical Education
|April 13, 2026

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
clinical reasoningcoding schemegenerative artificial intelligencehistory takingmedical educationvirtual patients

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This study developed a coding scheme to analyze medical students' history-taking behaviors with generative artificial intelligence (GenAI) virtual patients. Key clinical reasoning skills positively correlated with performance, offering insights for medical education.

Area of Science:

  • Medical Education
  • Artificial Intelligence in Healthcare
  • Clinical Reasoning Assessment

Background:

  • Generative artificial intelligence (GenAI)-based virtual patients (VPs) are vital tools for practicing clinical history taking.
  • A gap exists in effectively identifying and providing feedback on students' clinical reasoning during VP interactions.
  • This limitation hinders the development of targeted instructional strategies.

Purpose of the Study:

  • To develop and validate a coding scheme for assessing medical students' history-taking behaviors.
  • To identify specific behaviors during interactions with GenAI-based VPs.
  • To correlate these behaviors with academic performance metrics.

Main Methods:

  • A coding scheme was inductively developed using systematic text condensation on 1030 dialogues from 210 second-year medical students across 4 cases.
  • The scheme was validated for interrater reliability (κ≥0.85).
  • Case 5 dialogues were analyzed to correlate history-taking behaviors with diagnostic accuracy, checklist scores, knowledge tests, and postencounter forms.

    • Main Results:

    • A 12-behavior coding scheme across clinical reasoning, information gathering, and social interaction dimensions was established with high reliability.
    • Clinical reasoning behaviors like summarizing and logical organization strongly correlated with performance metrics.
    • Information gathering behaviors were linked to knowledge and thoroughness but not diagnostic accuracy.

    Conclusions:

    • A reliable, theory-informed coding scheme effectively identifies students' questioning behaviors and cognitive strategies during GenAI VP interactions.
    • This scheme offers valuable insights into developing clinical reasoning in medical students.
    • The approach enables scalable, real-time feedback for personalized learning and competency-based assessment in medical training.