Generative AI-Based Evaluation of Clinical Thinking and Decision-Making in Medical Students
Construction of an Intelligent Evaluation System for Clinical Thinking and Decision-making Ability of Medical Students Based on Generative Artificial Intelligence
The goal of this study is to develop and evaluate an intelligent system based on generative artificial intelligence (AI) for assessing clinical thinking and decision-making abilities in medical students.
The study will examine whether the AI-based evaluation system can provide reliable and valid assessments of medical students' performance in clinical scenarios. Participants will complete clinical case-based tasks designed to assess their clinical reasoning and decision-making abilities. Their responses will be evaluated using the AI-based system and compared with assessments made using established evaluation methods and/or expert ratings.
The findings of this study may help determine whether generative AI can be used as a practical tool for assessing clinical thinking and decision-making skills in medical education.
A tanulmány áttekintése
Állapot
Körülmények
Beavatkozás / kezelés
Részletes leírás
This study aims to develop and evaluate a generative artificial intelligence-based system for assessing clinical thinking and decision-making abilities in medical students. Clinical reasoning and decision-making are core competencies in medical education, but conventional assessment methods often require substantial faculty time and may be influenced by differences among evaluators.
The study will develop an intelligent evaluation framework using generative artificial intelligence to analyze medical students' responses to clinical case-based tasks. The system will assess key aspects of clinical thinking and decision-making, such as information interpretation, diagnostic reasoning, selection of appropriate management strategies, and overall decision-making logic.
Medical students participating in the study will complete standardized clinical scenarios or case-based assessment tasks. Their performance will be evaluated using the artificial intelligence-based system and compared with evaluations made by qualified clinical experts and/or established assessment methods. The study will examine the reliability, validity, consistency, and feasibility of the artificial intelligence-based evaluation approach.
The purpose of this study is to determine whether generative artificial intelligence can serve as a reliable and practical tool for evaluating clinical thinking and decision-making abilities in medical education. The system is intended for educational assessment and is not designed to replace clinical diagnosis or treatment decisions.
Tanulmány típusa
Beiratkozás (Tényleges)
Fázis
- Nem alkalmazható
Kapcsolatok és helyek
Tanulmányi helyek
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Zhejiang
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Hangzhou, Zhejiang, Kína, 310000
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
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Részvételi kritériumok
Jogosultsági kritériumok
Tanulmányozható életkorok
Egészséges önkénteseket fogad
Tanulmányozható nemek
Leírás
Inclusion Criteria:
- Aged 18 to 30 years.
- Fourth- or fifth-year undergraduate students majoring in Clinical Medicine at Zhejiang University School of Medicine, or trainees who have completed at least one year of standardized residency training at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine.
- Have completed basic surgery courses and theoretical learning related to breast diseases.
- Voluntarily agree to participate in the study and provide written informed consent.
- Have adequate communication and comprehension abilities.
Exclusion Criteria:
- Have previously participated in similar generative artificial intelligence (GAI)-based teaching intervention studies.
- Are unable to complete the entire study because of rotation schedules or other conflicts.
- Belong to vulnerable populations other than employees of the study institution or subordinates of the investigators, including individuals with mental illness, cognitive impairment, critical illness, pregnancy, or illiteracy.
- Refuse or are unwilling to provide informed consent.
Tanulási terv
Hogyan készül a tanulmány?
Tervezési részletek
- Elsődleges cél: Egyéb
- Kiosztás: Véletlenszerűsített
- Beavatkozó modell: Párhuzamos hozzárendelés
- Maszkolás: Egyetlen
Fegyverek és beavatkozások
Résztvevő csoport / kar |
Beavatkozás / kezelés |
|---|---|
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Aktív összehasonlító: Control Group: Traditional Teaching
the standardized surgical oncology teaching curriculum, utilizing standardized breast cancer teaching PowerPoint courseware. |
Participants will receive standard traditional teaching, including routine classroom instruction, case-based learning, and teacher-guided discussion, without the use of the generative artificial intelligence-assisted learning component. |
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Kísérleti: Intervention Group: Traditional Teaching Plus AI Assistance
the independently developed "Clinical Reasoning and Decision-Making Dialogue Evaluation System" on the basis of the same traditional teaching. |
Participants will receive standard traditional teaching, including routine classroom instruction, case-based learning, and teacher-guided discussion, without the use of the generative artificial intelligence-assisted learning component. The intervention consists of traditional medical education supplemented with a generative artificial intelligence-based learning tool. The tool will support students in clinical case analysis, reasoning, and decision-making exercises. The control group will receive the same traditional teaching without access to the AI-assisted learning component. |
Mit mér a tanulmány?
Elsődleges eredményintézkedések
Eredménymérő |
Intézkedés leírása |
Időkeret |
|---|---|---|
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Change in Clinical Reasoning Score on Objective Structured Clinical Examination (OSCE)
Időkeret: Baseline to Week 4
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The total score ranges from 0 to 100. A higher score indicates better clinical thinking and decision-making ability |
Baseline to Week 4
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Másodlagos eredményintézkedések
Eredménymérő |
Intézkedés leírása |
Időkeret |
|---|---|---|
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Intraclass Correlation Coefficient (ICC) Between GAI-Based and Expert Assessment Scores
Időkeret: Baseline to Week 4
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Both Generative AI (GAI) and human experts will evaluate the participants using the clinical Reasoning Assessment Rubric. The agreement between the GAI-based scores and expert assessment scores will be evaluated using the Intraclass Correlation Coefficient (ICC) . The coefficient ranges from 0 to 1, where higher values indicate better agreement. |
Baseline to Week 4
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Time to Complete the Assessment
Időkeret: During the clinical thinking and decision-making assessment at Week 4 (completion time measured in minutes).
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Assessment completion time is defined as the duration, measured in minutes, from the initiation of the clinical thinking and decision-making assessment to its final submission by the participant. The time will be automatically recorded by the evaluation platform. Each participant's assessment completion time will be measured during the assessment conducted at Week 4. |
During the clinical thinking and decision-making assessment at Week 4 (completion time measured in minutes).
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Participant Satisfaction Score on the GAI-Assisted Evaluation System
Időkeret: Week 4
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Participant satisfaction will be measured using a 5-point Likert scale (ranging from 1 = Very Dissatisfied to 5 = Very Satisfied). A higher score indicates higher satisfaction with the GAI-assisted evaluation system. |
Week 4
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Együttműködők és nyomozók
Szponzor
Tanulmányi rekorddátumok
Tanulmány főbb dátumok
Tanulmány kezdete (Tényleges)
Elsődleges befejezés (Tényleges)
A tanulmány befejezése (Tényleges)
Tanulmányi regisztráció dátumai
Először benyújtva
Először nyújtották be, amely megfelel a minőségbiztosítási kritériumoknak
Első közzététel (Tényleges)
Tanulmányi rekordok frissítései
Utolsó frissítés közzétéve (Tényleges)
Az utolsó frissítés elküldve, amely megfelel a minőségbiztosítási kritériumoknak
Utolsó ellenőrzés
Több információ
A tanulmányhoz kapcsolódó kifejezések
Kulcsszavak
Egyéb vizsgálati azonosító számok
- Research 2025734
Terv az egyéni résztvevői adatokhoz (IPD)
Tervezi megosztani az egyéni résztvevői adatokat (IPD)?
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