Utilizing Large Language Models for Developing Automatic Question Generation in Education

The increasing workload of educators, particularly in manual question creation, poses a significant challenge in modern education. Manual question creation demands time, effort, and a deep understanding of the material to ensure contextual and curriculum-aligned questions. To address this, an Automatic Question Generation (AQG) system was developed using extractive summarization combined with the PEGASUS and TextRank methods. The system leverages Natural Language Processing (NLP) and transformer-based large language models (LLMs) to efficiently generate relevant questions. The primary data source for this system was digital social studies (IPS) books from the Indonesian Ministry of Education. The evaluation was conducted using ROUGE Score metrics and human assessments. ROUGE analysis yielded an average F1 score of 0.87 (ROUGE-1), 0.83 (ROUGE-2), and 0.84 (ROUGE-L), demonstrating the system's effectiveness in capturing essential information. Human evaluations involving educators and students highlighted the relevance and contextual accuracy of generated questions, particularly for structured materials. The system generated questions within 2 to 6 minutes, showcasing its efficiency in reducing educators' workload. However, challenges remain in handling materials with implicit semantic relationships or nonlinear narratives, as PEGASUS struggles to maintain contextual relevance. This limitation may lead to irrelevant questions and answers, indicating a need for improved semantic understanding. This study concludes that the PEGASUS+TextRank AQG system is a promising tool to streamline question generation. Future improvements in semantic algorithms and broader training data are crucial to enhancing the system's reliability and adaptability to diverse educational contexts.