Exercise Generation and Student Cognitive Ability Research Based on ChatGPT and Rasch Model

Abstract

In the context of generative artificial intelligence (AI), AIGCP (content generation-based AI products), represented by ChatGPT, have attracted extensive attention in the field of education. This study focuses on the discipline of university operating systems and adopts the Rasch model as the theoretical foundation. By combining ChatGPT with existing question banks and using the bidirectional fine-grained table method, it compiles questions that match the corresponding abilities for three different levels of student groups. This aims to explore personalized question matching and student cognitive ability analysis methods to support personalized teaching. The research findings indicate that ChatGPT is capable of matching exercises of similar difficulty under the Rasch model, but its accuracy in generating exercise content is relatively low, and the variety of exercise content is limited. Students' performance in overall competency requires improvement. This study aims to leverage the combined strengths of ChatGPT and traditional educational assessment methods to introduce an innovative approach to support personalized instruction. It aims to establish the routine utilization of exercise creation by ChatGPT and personalized analysis of student cognitive abilities, thereby better fulfilling the demands of education within the classroom setting.