Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks
This work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs)...
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| Format: | Article |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10843681/ |
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| author | Muhammad Shoaib Ghassan Husnain Nasir Sayed Yazeed Yasin Ghadi Masoud Alajmi Ayman Qahmash |
| author_facet | Muhammad Shoaib Ghassan Husnain Nasir Sayed Yazeed Yasin Ghadi Masoud Alajmi Ayman Qahmash |
| author_sort | Muhammad Shoaib |
| collection | DOAJ |
| description | This work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs), our model provides a versatile approach to addressing the need for diversity and context in relevant MCQ generation across proficiency levels, topic areas. Resulting MCQs inspire implementations within a variety of educational environments - from classrooms, to online courses, and finally exams - equipping teachers with an instrument that could be easily adapted based on the specific needs o students. The model is trained on a carefully constructed dataset that includes material from more than 20 subareas in computer science, consisting of materials such as textbooks, online encyclopedias and Q&A websites. Through rigorous evaluation using comprehensive performance metrics, including Question Relevance Score (QRS), Diversity Index (DI), and Difficulty Alignment Accuracy (DAA), we demonstrate the efficacy and robustness of our framework in generating high-quality MCQs. Moreover, we address ethical considerations inherent in AI-driven educational assessment, ensuring fairness, transparency, and accountability in the MCQ generation process. The cGAN architecture facilitates the generation of contextually relevant MCQs across various proficiency levels and subject domains, enhancing the educational assessment process. The comprehensive dataset developed for this study encompasses diverse computer science topics curated from authoritative textbooks, online resources, question banks, and instructor-generated content. Additionally, a user-friendly QT application has been developed, enabling seamless integration of the cGAN model into educational environments. Through rigorous evaluation and ethical considerations, this framework demonstrates its efficacy, ensuring fairness, transparency, and accountability in MCQ generation. This interdisciplinary work represents a significant advancement in computer science education, providing educators with a powerful tool to enhance student engagement and learning outcomes. |
| format | Article |
| id | doaj-art-c618dfc3c2c247ee9ffd53ff76b1efe6 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c618dfc3c2c247ee9ffd53ff76b1efe62025-01-31T00:01:58ZengIEEEIEEE Access2169-35362025-01-0113166971671510.1109/ACCESS.2025.353047410843681Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial NetworksMuhammad Shoaib0https://orcid.org/0000-0001-9274-9577Ghassan Husnain1https://orcid.org/0009-0005-9727-7114Nasir Sayed2Yazeed Yasin Ghadi3Masoud Alajmi4Ayman Qahmash5https://orcid.org/0000-0003-2558-9475Department of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, PakistanDepartment of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, PakistanDepartment of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, PakistanDepartment of Computer Science and Software Engineering, Al Ain University, Al Ain, United Arab EmiratesDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaDepartment of Informatics and Computer Systems, King Khalid University, Abha, Saudi ArabiaThis work presents a novel perspective towards generating automated multiple-choice questions (MCQs)-a task fundamentally different due to the highly dynamic nature of computer science education, which spans several sub-domains. Taking advantage of Conditional Generative Adversarial Networks (cGANs), our model provides a versatile approach to addressing the need for diversity and context in relevant MCQ generation across proficiency levels, topic areas. Resulting MCQs inspire implementations within a variety of educational environments - from classrooms, to online courses, and finally exams - equipping teachers with an instrument that could be easily adapted based on the specific needs o students. The model is trained on a carefully constructed dataset that includes material from more than 20 subareas in computer science, consisting of materials such as textbooks, online encyclopedias and Q&A websites. Through rigorous evaluation using comprehensive performance metrics, including Question Relevance Score (QRS), Diversity Index (DI), and Difficulty Alignment Accuracy (DAA), we demonstrate the efficacy and robustness of our framework in generating high-quality MCQs. Moreover, we address ethical considerations inherent in AI-driven educational assessment, ensuring fairness, transparency, and accountability in the MCQ generation process. The cGAN architecture facilitates the generation of contextually relevant MCQs across various proficiency levels and subject domains, enhancing the educational assessment process. The comprehensive dataset developed for this study encompasses diverse computer science topics curated from authoritative textbooks, online resources, question banks, and instructor-generated content. Additionally, a user-friendly QT application has been developed, enabling seamless integration of the cGAN model into educational environments. Through rigorous evaluation and ethical considerations, this framework demonstrates its efficacy, ensuring fairness, transparency, and accountability in MCQ generation. This interdisciplinary work represents a significant advancement in computer science education, providing educators with a powerful tool to enhance student engagement and learning outcomes.https://ieeexplore.ieee.org/document/10843681/Automated MCQ generationconditional generative adversarial networks (cGANs)computer science educationdataset curationeducational assessment |
| spellingShingle | Muhammad Shoaib Ghassan Husnain Nasir Sayed Yazeed Yasin Ghadi Masoud Alajmi Ayman Qahmash Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks IEEE Access Automated MCQ generation conditional generative adversarial networks (cGANs) computer science education dataset curation educational assessment |
| title | Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks |
| title_full | Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks |
| title_fullStr | Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks |
| title_full_unstemmed | Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks |
| title_short | Automated Generation of Multiple-Choice Questions for Computer Science Education Using Conditional Generative Adversarial Networks |
| title_sort | automated generation of multiple choice questions for computer science education using conditional generative adversarial networks |
| topic | Automated MCQ generation conditional generative adversarial networks (cGANs) computer science education dataset curation educational assessment |
| url | https://ieeexplore.ieee.org/document/10843681/ |
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