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Mathematical stories have demonstrated the ability to bolster the motivation and interest of students in learning mathematics, thereby exerting a positive influence on their academic performance. However, due to a lack of adequate resources and the desire to engage students in the creation process, Generative Artificial Intelligence (GAI) is utilized to generate mathematical stories accompanied by images. This study presents a framework for automatic quality assessment to evaluate the coherence of multimodal text and images generated by GAI, as well as the appropriateness of the stories for different grade levels. The dataset comprises mathematical stories generated by GAI for grades 3, 4, and 5, obtained from an American online learning platform, each story consisting of titles, bodies, and image illustrations generated by GP4, images generated by DALL-E3. Initially, a method is devised based on CLIP model and Mini-GPT4 for extracting multimodal semantic features to establish the relationship between text and images in mathematical stories and their generation parameters. Subsequently, mathematical text features are designed, including nine mathematical text attributes and ten traditional text readability indicators, followed by collinearity feature selection and statistical testing. Finally, five machine learning grade regressor for mathematical stories were trained, and the correlation between these 19 features and grades is explored using genetic algorithm-based factor mining and the interpretable artificial intelligence method SHapley Additive exPlanations (SHAP). To further understand advanced text features, the latest natural language processing (NLP) readability indicators are also integrated into the analysis. Through multimodal features, traditional text readability metrics, and NLP readability indicators, this method introduces a novel approach for automatically assessing the quality of GAI-generated multimodal mathematical stories, providing a tool for grade predictor and shedding light on the factors (Image-text relevance and textual features) influencing the grade level of analyzed stories, thereby offering new insights for leveraging GAI in mathematical education.
Li et al. (Tue,) studied this question.