Exploring the Feasibility of Generative AI in Persona Research: A Comparative Analysis of Large Language Model-Generated and Human-Crafted Personas in Obesity Research

This study investigates the perceptions of Persona descriptions generated using three different large language models (LLMs) and qualitatively developed Personas by an expert panel involved in obesity research. Six different Personas were defined, three from the clinical domain and three from the educational domain. The descriptions of Personas were generated using qualitative methods and the LLMs (i.e., Bard, Llama, and ChatGPT). The perception of the developed Personas was evaluated by experts in the respective fields. The results show that, in general, the perception of Personas did not significantly differ between those generated using LLMs and those qualitatively developed by human experts. This indicates that LLMs have the potential to generate a consistent and valid representation of human stakeholders. The LLM-generated Personas were perceived as believable, relatable, and informative. However, post-hoc comparisons revealed some differences, with descriptions generated using the Bard model being in several Persona descriptions that were evaluated most favorably in terms of empathy, likability, and clarity. This study contributes to the understanding of the potential and challenges of LLM-generated Personas. Although the study focuses on obesity research, it highlights the importance of considering the specific context and the potential issues that researchers should be aware of when using generative AI for generating Personas.