A Comparison of Google and ChatGPT for Automatic Generation of Health-related Multiple-choice Questions

Critical to producing accessible content is an understanding of what characteristics affect understanding and comprehension. To answer this question, we are producing a large corpus of health-related texts with associated questions that can be read or listened to by study participants to measure the difficulty of the underlying content, which can later be used to better understand text difficulty and user comprehension. In this paper, we examine methods for automatically generating multiple-choice questions using Google's related questions and ChatGPT. Overall, we find both algorithms generate reasonable questions that are complementary; ChatGPT questions are more similar to the snippet while Google related-search questions have more lexical variation.