Generative Artificial Intelligence (AI) is rapidly changing the field of education, especially in the application of image generation technology. This study aims to systematically understand the current applications, research hotspots, and future trends of image generative AI in education through bibliometric analysis. The study is based on relevant literature from the Web of Science database between 2017 and 2024. Using CiteSpace software, it conducts a visual analysis and interpretation of trends in publication volume, prevalent keywords, keyword co-occurrence, keyword clustering, keyword bursts, timelines, time zone distribution, and country co-occurrence networks. The results show that the application of image generative AI in education has been increasing year by year. The research hotspots are mainly focused on personalized learning, art education, virtual/augmented reality, and educational assessment. There is also a trend from technical exploration to educational practice, and from single disciplines to interdisciplinary integration. Future research trends include developing image generation models more suitable for educational scenarios, designing personalized learning experiences, studying educational effect evaluation methods, and exploring ethical and social impacts. This research provides a reference for researchers in related fields and provides insights for the future application of image generative AI in education.