AI-Supported Personas vs Conventional Personas: A comparative Study Based on The Views and Opinions of Designers

Abstract

In product design, understanding the target user group, their habits, preferences, and likes is crucial for ensuring a product meets user needs. User research plays a vital role in the early stages of the design process. The persona method, developed by Alan Cooper, is a widely used technique in the design process where users are grouped based on real data and represented by fictional characters. Conventional persona creation relies on qualitative data and designer intuition, which can be time-consuming and prone to bias. This paper explores the use of AI-driven tools, specifically ChatGPT-4o and DALL-E3, to generate dynamic, data-driven personas, offering a more efficient and precise alternative. The study compares four conventional and four AI-supported personas for mobile music streaming apps both derived from interviews with 24 users. Ten product designers evaluated both persona types, with results indicating that AI-supported personas hold significant potential for enhancing user experience design. The findings demonstrate how AI can enable more adaptive, user-centric designs, bridging the gap between conventional methods and AI-supported approaches.

Keywords: Artificial Intelligence (AI), User Personas, User Experience (UX)

DOI: 10.54941/ahfe1005868