###### Abstract  
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. user personas; obesity; large language models; value sensitive design; digital health interventions 2020 academc  
preferences, needs, and goals. This knowledge can guide the development of engaging and effective digital interventions [10]. Finally, the Personas can efficiently capture and reveal the technological and environmental context in which users will interact with digital interventions, making digital interventions accessible, relevant, and acceptable to the target population [11; 12]. While Personas are one of the most widely used methods of user research, their development, traditionally performed using qualitative methods, is facing the critical challenge of being extremely time-consuming and static [13]. Namely, the process involves multiple steps and requires the involvement of various experts and stakeholders to ensure that the Personas accurately represent the target audience [14]. As such, there is a great need for simpler, quicker, and more cost- and resource-effective ways to create them. This gap may be addressed by artificial intelligence (AI), which is often described as \"being able to think like humans\" [15] or \"being able to perform tasks that require human intelligence\" [16]. Large language models (LLMs) can streamline the creation of high-quality Personas [14] by also automating qualitative data analysis, recognizing patterns, generating and refining Personas, personalizing content, and enhancing efficiency and scalability. Thus, employing LLMs in the Persona creation process can save time and resources, allowing for the development of high-quality Personas without extensive manual effort. However, there are several potential weaknesses and limitations that should be considered regarding LLMs, including their generalizability, data quality, interpretability, explainability, and prompt engineering. To understand the limitations further, this study analyzes and compares the quality of generated Personas using multiple LLMs and those generated by an expert human panel. To ensure replicability and the wider applicability of our research, we only analyze those models that can be accessed openly and for free, i.e., Google's Bard (now Gemini), OpenAI's ChatGPT 3.5, and Llama2 70B. Moreover, the prompts used (see Supplementary Materials) are straightforward to prevent bias from expert knowledge or exceptional skills and expertise. We are specifically interested in whether the different Personas will be perceived differently by human experts, and to what extent.