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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/10/139 |
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| author | Somaiya Al Shuraiqi Abdulrahman Aal Abdulsalam Ken Masters Hamza Zidoum Adhari AlZaabi |
| author_facet | Somaiya Al Shuraiqi Abdulrahman Aal Abdulsalam Ken Masters Hamza Zidoum Adhari AlZaabi |
| author_sort | Somaiya Al Shuraiqi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-87b5fb52f99a4b19a9551da01389da44 |
| institution | OA Journals |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-87b5fb52f99a4b19a9551da01389da442025-08-20T02:11:01ZengMDPI AGBig Data and Cognitive Computing2504-22892024-10-0181013910.3390/bdcc8100139Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future DirectionsSomaiya Al Shuraiqi0Abdulrahman Aal Abdulsalam1Ken Masters2Hamza Zidoum3Adhari AlZaabi4Department of Computer Science, College of Science, Sultan Qaboos University, P.O. Box 243, Muscat 123, OmanDepartment of Computer Science, College of Science, Sultan Qaboos University, P.O. Box 243, Muscat 123, OmanMedical Education and Informatics Department, College of Medicine and Health Sciences, Sultan Qaboos University, P.O. Box 243, Muscat 123, OmanDepartment of Computer Science, College of Science, Sultan Qaboos University, P.O. Box 243, Muscat 123, OmanDepartment of Human and Clinical Anatomy, College of Medicine & Health Sciences, Sultan Qaboos University, P.O. Box 243, Muscat 123, OmanThis 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.https://www.mdpi.com/2504-2289/8/10/139automatic question generation (AQG)case-based multiple-choice questions (MCQs)ontologynatural language processing (NLP)machine learning (ML)deep learning (DL) |
| spellingShingle | Somaiya Al Shuraiqi Abdulrahman Aal Abdulsalam Ken Masters Hamza Zidoum Adhari AlZaabi Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions Big Data and Cognitive Computing automatic question generation (AQG) case-based multiple-choice questions (MCQs) ontology natural language processing (NLP) machine learning (ML) deep learning (DL) |
| title | Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions |
| title_full | Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions |
| title_fullStr | Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions |
| title_full_unstemmed | Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions |
| title_short | Automatic Generation of Medical Case-Based Multiple-Choice Questions (MCQs): A Review of Methodologies, Applications, Evaluation, and Future Directions |
| title_sort | automatic generation of medical case based multiple choice questions mcqs a review of methodologies applications evaluation and future directions |
| topic | automatic question generation (AQG) case-based multiple-choice questions (MCQs) ontology natural language processing (NLP) machine learning (ML) deep learning (DL) |
| url | https://www.mdpi.com/2504-2289/8/10/139 |
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