TY - CHAP
T1 - The Research Status and Future Trends of Image Generative Artificial Intelligence in Education
AU - Xia, Yuanze
AU - Pang, Patrick Cheong Iao
AU - Liu, Ting
AU - Luo, Yiming
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - 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.
AB - 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.
KW - Bibliometric analysis
KW - Education
KW - Generative artificial intelligence
KW - Image
UR - https://www.scopus.com/pages/publications/105026992970
U2 - 10.1007/978-981-95-2521-8_44
DO - 10.1007/978-981-95-2521-8_44
M3 - Chapter
AN - SCOPUS:105026992970
T3 - Lecture Notes in Educational Technology
SP - 613
EP - 627
BT - Lecture Notes in Educational Technology
PB - Springer Science and Business Media Deutschland GmbH
ER -