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), 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.