Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions

This paper offers an in-depth review of the latest advancements in the automatic generation of medical case-based multiple-choice questions (MCQs). The automatic creation of educational materials, particularly MCQs, is pivotal in enhancing teaching effectiveness and student engagement in medical edu...

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Bibliographic Details
Published in:Big data and cognitive computing 2024-10, Vol.8 (10), p.139
Main Authors: Al Shuraiqi, Somaiya, Aal Abdulsalam, Abdulrahman, Masters, Ken, Zidoum, Hamza, AlZaabi, Adhari
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
automatic question generation (AQG)
Automation
Case studies
case-based multiple-choice questions (MCQs)
Cognition & reasoning
Cognitive ability
Computational linguistics
Critical thinking
Decision making
deep learning (DL)
Education
Educational objectives
Effectiveness
Equipment and supplies
Human error
Innovations
Knowledge
Language processing
Machine learning
machine learning (ML)
Medical education
Medical materials
Medical personnel
Natural language interfaces
Natural language processing
natural language processing (NLP)
ontology
Problem solving
Quantitative psychology
Questions
Semantics
Students
Teaching
Technology application
Technology assessment
Training
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Summary:This paper offers an in-depth review of the latest advancements in the automatic generation of medical case-based multiple-choice questions (MCQs). The automatic creation of educational materials, particularly MCQs, is pivotal in enhancing teaching effectiveness and student engagement in medical education. In this review, we explore various algorithms and techniques that have been developed for generating MCQs from medical case studies. Recent innovations in natural language processing (NLP) and machine learning (ML) for automatic language generation have garnered considerable attention. Our analysis evaluates and categorizes the leading approaches, highlighting their generation capabilities and practical applications. Additionally, this paper synthesizes the existing evidence, detailing the strengths, limitations, and gaps in current practices. By contributing to the broader conversation on how technology can support medical education, this review not only assesses the present state but also suggests future directions for improvement. We advocate for the development of more advanced and adaptable mechanisms to enhance the automatic generation of MCQs, thereby supporting more effective learning experiences in medical education.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc8100139