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Keywords = automatic question generation (AQG)

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32 pages, 614 KB  
Review
Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions
by Somaiya Al Shuraiqi, Abdulrahman Aal Abdulsalam, Ken Masters, Hamza Zidoum and Adhari AlZaabi
Big Data Cogn. Comput. 2024, 8(10), 139; https://doi.org/10.3390/bdcc8100139 - 17 Oct 2024
Cited by 8 | Viewed by 6454
Abstract
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, [...] Read more.
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. Full article
(This article belongs to the Special Issue Advances in Natural Language Processing and Text Mining)
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