Short Answer Questions Generation by Fine-Tuning BERT and GPT-2

Abstract

In educational research, artificial intelligence (AI) is suitable for many situations, such as exploring student learning paths and strategies. However, most of them cannot reduce the workload of teachers. In the course, teachers need to spend a lot of e ffort on setting exams, because exams are the most direct way to understand students' learning performance. In this research, we use modern artificial intelligence model, BERT and GPT -2 to generate questions to reduce the work of teachers frequently settin g questions. The type of questions we generate is short answer questions. The main reason is that many researches prove that short- answer questions can enhance students' long -term memory and improve learning performance. We also compare the performance of BERT before and after fine -tuning. The results show that BERT can be used for general reading comprehension questions before fine -tuning, but in the field of domain knowledge, fine -tune BERT's performance is better .