TY - GEN
T1 - Insight Through Dialogue
T2 - 16th International Conference on Cross-Cultural Design, CCD 2024, held as part of the 26th HCI International Conference, HCII 2024
AU - Zhao, Xiaoxuan
AU - Qiu, Yue
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - This study systematically reviews the objectives, methodologies, and challenges involved in cross-cultural design research, analyzing the benefits of employing Artificial Intelligence-Generated Content (AIGC) for such studies. It introduces a novel tool that applies AIGC to cross-cultural design research, developed through the use of a fine-tuned ChatGPT-4 model. By creating a specific dataset for the research topic and applying transfer learning techniques, this tool evolves into a chatbot capable of delivering personalized response strategies to users from diverse cultural backgrounds. It leverages natural language interfaces and real-time image generation to meet user needs, conducting research tasks autonomously. Experimental results demonstrate that, compared with conventional cross-cultural research methods such as questionnaires and manual interviews, the chatbot significantly enhances the efficiency of design research and users’ cross-cultural interaction experience, while obtaining more realistic and objective feedback. This study not only underscores the potential application of AIGC in cross-cultural design research but also provides substantial theoretical support and practical guidance for future research in cross-cultural contexts.
AB - This study systematically reviews the objectives, methodologies, and challenges involved in cross-cultural design research, analyzing the benefits of employing Artificial Intelligence-Generated Content (AIGC) for such studies. It introduces a novel tool that applies AIGC to cross-cultural design research, developed through the use of a fine-tuned ChatGPT-4 model. By creating a specific dataset for the research topic and applying transfer learning techniques, this tool evolves into a chatbot capable of delivering personalized response strategies to users from diverse cultural backgrounds. It leverages natural language interfaces and real-time image generation to meet user needs, conducting research tasks autonomously. Experimental results demonstrate that, compared with conventional cross-cultural research methods such as questionnaires and manual interviews, the chatbot significantly enhances the efficiency of design research and users’ cross-cultural interaction experience, while obtaining more realistic and objective feedback. This study not only underscores the potential application of AIGC in cross-cultural design research but also provides substantial theoretical support and practical guidance for future research in cross-cultural contexts.
KW - AIGC
KW - Cross-cultural
KW - Design Research
KW - Large Language Models
UR - http://www.scopus.com/inward/record.url?scp=85196161040&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-60913-8_27
DO - 10.1007/978-3-031-60913-8_27
M3 - Conference contribution
AN - SCOPUS:85196161040
SN - 9783031609121
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 388
EP - 406
BT - Cross-Cultural Design - 16th International Conference, CCD 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
A2 - Rau, Pei-Luen Patrick
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 29 June 2024 through 4 July 2024
ER -