The Impact of Generative Artificial Intelligence on Student Creativity in Art and Design Education: A Meta-Analysis

Abstract

The present study is a meta-analysis examining the impact of generative artificial intelligence (GenAI) tools on student creativity in art and design education. The study systematically reviewed publications in the Web of Science, Scopus, and ERIC databases between January 2020-2026. Findings from 31 independent experimental, quasi-experimental, and correlational studies were analyzed. The analysis revealed that GenAI use has a moderate and statistically significant effect on student creativity (d = 0.61). The study also examined the moderating roles of variables such as publication year, study type, cultural context, students' academic level, type of creative output, and type of AI tool used on this effect. The findings showed that publication year was a significant moderator, with the highest effect observed in studies published in 2024. In contrast, study type and cultural context did not have a significant moderating effect. When examined from an academic perspective, it was observed that GenAI's effect on creativity was higher among graduate students, followed by undergraduate and K-12 students. Among the types of creativity, the highest impact was seen in the originality/innovation dimension, followed by general creativity, creative self-efficacy, and divergent thinking. Regarding AI tool types, text-to-image tools provided the highest impact, followed by large language models (LLM), GAN/deep learning-based tools, and hybrid tools. Publication bias analyses showed that the results were reliable and did not contain significant bias. The study reveals that generative AI tools are an effective tool in increasing student creativity in art and design education, and that this effect can vary depending on specific student groups and tool types.