The rapid advancements in artificial intelligence (AI) have transformed various domains, including education. Generative AI models have garnered significant attention for their potential in educational settings, but image-generative AI models need to be more utilized. This study explores the potential of integrating generative AI, specifically Stable Diffusion, into art-focused STEAM classes. To this end, we combined STEAM education with image-generative AI. We designed and implemented a learning activity in which students used image-generative AI to generate creative images and wrote imaginative diaries inspired by AI-generated pictures. Using a mixed-methods approach, we collected and analyzed data from surveys, interviews, and prompt datasets to gain insights into students' perceptions, motivations, and behaviors concerning the integration of generative AI in art education. Our study found that incorporating generative AI into art-focused STEAM classes may reduce the gap between male and female students' interest in art practices. We also found that the number of subjects in students' prompt datasets is linked to their divergent and convergent thinking. Finally, we discovered a meaningful correlation between the number of unique words in students' diaries and the image scores that researchers assessed. Our findings shed light on the relationship between students' divergent and convergent behaviors, offering valuable insights into their creative thinking processes. This study underscores the potential of integrating image-generative AI models in STEAM education.