Pusan National University researchers show how AI can help in fashion trend prediction

Fashion trend forecasting helps companies predict which clothes will be popular in upcoming seasons. Traditionally, this has relied on experts’ intuition, experience and creativity. More recently, big-data analysis has been incorporated, offering deeper insights into consumer behavior. However, such methods pose technical barriers and remain out of reach for fashion students or small brands.

Recent developments in artificial intelligence (AI) can balance the scales. Large language models (LLMs) like ChatGPT have made big data analysis readily available to the public.  LLMs draw from vast societal and cultural data and can potentially be used for predicting fashion trends. However, given their current limitations, such as hallucinations and factual errors, it is imperative to verify their suitability and to develop structured prediction methods.

In a new study, Assistant Professor Yoon Kyung Lee and Master’s student Chaehi Ryu, from the Department of Clothing and Textiles at Pusan National University, South Korea, developed a new approach for predicting fashion trends using ChatGPT. “Rather than simply asking ‘What fashion will be popular in the future?’, we designed a systematic strategy for prompting the AI for more specific and consistent answers,” explains Dr. Lee. “We also compared ChatGPT's predictions to an actual trend agency’s report.” Their findings were published in the Clothing and Textiles Research Journal on September 26, 2025.

The researchers first verified ChatGPT’s characteristics in fashion trend prediction using general prompts about 2023 fall/winter men’s fashion trends. Based on the results, they developed a new Top-Down Prompting (TDP) technique based on the Lotus Blossom (LB) brainstorming approach, for more accurate and specific forecasting.

TDP starts with a central problem prompt, a general query to predict ‘Fashion Trends’, and then expands into sub-problem prompts, including queries on “Silhouette,” “Materials,” “Key Items,” “Garment Details,” “Decorative Elements,” “Color,” “Moods,” and “Prints and Patterns.” 

The researchers used this approach to predict men’s fashion trends for fall/winter 2024 using ChatGPT-3.5 and ChatGPT-4 Classic. The responses were compared and validated against the fall/winter 2024 men’s fashion trend predictions by the Official Fashion Trend Information Company (OFTIC), and the analysis was reviewed by two fashion experts.

Analysis showed that ChatGPT’s predictions mostly reflected established or generalized fashion ideas, rather than forward-looking or innovative designs. Moreover, it accurately identified only 9 out of 39 trends predicted in OFTIC’s report. Notably, however, both models predicted emerging themes, including gender fluidity and statement coats.

“While the prediction accuracy of ChatGPT is low, what's intriguing is that it captured new trends not found in existing data,” notes Dr. Lee. “AI can sense cultural shifts and open up new creative directions.”

Although ChatGPT should not yet be viewed as a definitive forecasting tool, it can effectively complement expert-led analysis. This is especially valuable for fashion students and small brands, helping them achieve more accurate and nuanced trend forecasts. For fashion education, the researchers also developed a TDP-based hybrid conceptual framework for fashion trend forecasting, integrating both AI-analysis and expert-knowledge.

Overall, this study shows how AI tools can make fashion trend forecasting more systematic and accessible.

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Reference
DOI: 10.1177/0887302X251371969  
 

About Pusan National University
Pusan National University, located in Busan, South Korea, was founded in 1946 and is now the No. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service and has approximately 30,000 students, 1,200 professors, and 750 faculty members. The university comprises 14 colleges (schools) and one independent division, with 103 departments in all.
Website: https://www.pusan.ac.kr/eng/Main.do
 

About Assistant Professor Yoon Kyung Lee
Dr. Yoon Kyung Lee is an Assistant professor of Fashion Design for Dept. Clothing and Textiles at Pusan National University. Her research interests include Sustainability in fashion, Creativity, Innovative design education, Digital fashion technologies, AI & neuroscience in fashion. Before coming her current position, she worked as an Assistant research professor at Seoul National University. She completed the Postdoctoral training at DeLong’s lab at University of Minnesota. She received a PhD in Aesthetics in Dress from Seoul National University and an MFA in Fashion and Textile Design from the Institute European of Design (IED) Milan, Italy.