Scalable Educational Question Generation with Pre-trained Language Models
This addresses the need for scalable self-assessment in personalized online learning, but appears incremental as it builds on existing pre-trained language models.
The paper tackles the problem of automatically generating educational questions to scale online education by developing EduQG, a model that adapts a large language model, and demonstrates it produces superior questions through further pre-training and fine-tuning on scientific data.
The automatic generation of educational questions will play a key role in scaling online education, enabling self-assessment at scale when a global population is manoeuvring their personalised learning journeys. We develop \textit{EduQG}, a novel educational question generation model built by adapting a large language model. Our extensive experiments demonstrate that \textit{EduQG} can produce superior educational questions by further pre-training and fine-tuning a pre-trained language model on the scientific text and science question data.