Exploration of Image-Generating AI from Text in Creative Fields for a Sustainable Arts-Integrated Approach to STEAM Education

Abstract

This study explores the feasibility of integrating image-generating AI into a sustainable STEAM education, Science, Technology, Engineering, Arts, and Mathematics, with an arts-centered approach. Using Microsoft Copilot to conduct an AI image generation competition involving 206 participants from various demographic groups, we investigated creative engagement patterns and educational potential. Participants were tasked with producing images inspired by thematic keywords including ‘spring,’ ‘Korean traditional house,’ ‘robot,’ and ‘aerospace.’ The participants were divided into three groups for analysis: undergraduates within four years of admission, undergraduates who had been enrolled for four or more years, and non-undergraduate participants. The results revealed notable differences in creative tendencies across groups. Non-undergraduate participants demonstrated a more diverse use of colors and themes as opposed to undergraduate students enrolled for more than four years. The inclusion of certain lexical items, such as ‘space’, was significantly higher among specific groups. Text-to-image generation observed varied thematic preferences and engagement levels indicating possibilities for future resource design. These findings underscore the potential role of generative AI in fostering creativity, bridging art and technology, and promoting sustainability in education. This research highlights the transformative potential of AI tools in educational settings, offering insights into their potential application for personalized learning experiences. The implications extend to developing tailored educational programs that address diverse learner needs, ultimately contributing to the advancement of arts-integrated STEAM education.