| abstract |
| Background Persona and Customer Journey Map methodologies have been widely used in design research to effectively identify user needs. Recently, generative artificial intelligence (AI) has been introduced into various stages of the design field, such as data analysis, idea generation, and visualization. However, empirical studies comparing generative AI-based research with traditional human researcher-driven approaches remain insufficient. Accordingly, this study aims to compare and analyze the results of applying generative AI in the initial user needs exploration phase of a ‘small sightseeing boat design’ project with those of conventional, human researcher-centered research.
Methods Two parallel research processes were conducted to generate personas and customer journey maps. First, the researcher (designer) created personas and journey maps using traditional methods. For comparative purposes, the same materials were also produced using generative AI tools such as ChatGPT. The results from both methods were then presented to six professional designers, who evaluated them through a survey using a five-point Likert scale and provided open-ended feedback. Additionally, content comparison and qualitative analysis were undertaken to assess the impact of generative AI on design research methods and to identify distinctions from existing approaches.
Result Generative AI demonstrated clear advantages in rapid large-scale data collection, standardized data organization, and repetitive tasks. It was able to produce personas and customer journey maps covering various age groups and occupations in a consistent format within a short period. In contrast, human researcher-driven studies offered greater ‘authenticity’ and ‘creativity’ by delicately capturing real experiences, emotions, and detailed contexts. Survey results indicated that experts considered a combination of both methods to be complementary and effective. However, generative AI tended to produce typical, generalized results, while the human method was time-consuming and labor-intensive. Therefore, AI proves useful for quickly exploring a wide range of cases, with subsequent refinement of details through human reasoning being the most effective approach.
Conclusion The findings reveal that generative AI and human researcher-driven design research each have distinct strengths and limitations. Generative AI excels at rapid information gathering and producing standardized results, whereas human researchers are better at in-depth interpretation of user context and creative approaches. Experts recognized that combining the speed of AI with the deep insights of experts can greatly enhance both the efficiency and quality of research. Further studies utilizing advanced AI techniques in various industries are required, and it is expected that collaborative, hybrid approaches between generative AI and human researchers will enable more precise and in-depth design research in the future. |
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| Key Words |
| 생성형 인공지능 디자인 리서치, 페르소나 기법, 고객여정지도, Generative AI Design Research, Persona, Customer Journey Map |
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