Mathematical stories can enhance students' motivation and interest in learning mathematics, thereby positively impacting their academic performance. However, due to resource constraints faced by the creators, generative artificial intelligence (GAI) is employed to create mathematical stories accompanied by images. This study introduces a method for automatically assessing the quality of these multimodal stories by evaluating text-image coherence and textual readability. Using GAI-generated stories for grades 3 to 5 from the US math story learning platform "Read Solve Create" (RSC), we extracted features related to multimodal semantics and text readability. We then analysed the correlation between these features and student engagement levels, measured by average reading time per story (behavioural engagement) and average drawing tool usage per story (cognitive engagement), derived from browsing logs and interaction metrics on the platform. Our findings reveal that textual features such as conjunctive adverbs, sentence connectors, causal connectives and simplified vocabulary positively correlate with behavioural engagement. Additionally, higher semantic similarity between text and images, as well as the number of operators in the stories, is associated with increased cognitive engagement. This study advances the application of GAI in mathematics education and offers novel insights for instructional material design.