As generative AI (GenAI) is increasingly applied in persona development to represent real users, understanding the implications and limitations of this technology is essential for establishing robust practices. This scoping review analyzes how 81 articles (2022-2025) use GenAI techniques for the creation, evaluation, and application of personas. The articles exhibited good level of reproducibility, with 61% of articles sharing resources (personas, code, or datasets). Furthermore, conversational persona interfaces are increasingly provided alongside traditional profiles. However, nearly half (45%) of the articles lack evaluation, and the majority (86%) use only GPT models. In some articles, GenAI use creates a risk of circularity, in which the same GenAI model both generates and evaluates outputs. Our findings also suggest that GenAI seems to reduce the role of human developers in the persona-creation process. To mitigate the associated risks, we propose actionable guidelines for the responsible integration of GenAI into persona development.

Metaverse environments combined with large language models (LLMs) enable guided interaction through LLM-powered avatars that function as embodied conversational agents. In our study, we examined how scholars interact with an LLM-powered avatar modeled after a real professor during a virtual reality (VR) tour of a research lab. As little is known about how metaverse characteristics shape the user experience (UX) mechanisms that drive acceptance of such technologies, we conducted a 2 (avatar realism: abstract vs. hyperrealistic) × 2 (immersion: desktop vs. headset-based VR) within-subjects study (N= 30), where academic participants engaged in a virtual lab tour guided by the professor avatar. We conducted path analyses on three conceptual models and, based on the results, proposed the Virtual Lab Acceptance Model (VLAM), which features an experiential path (where perceived immersion increases empathy towards the avatar and task enjoyment) and a rational path (where perceived realism increases avatar credibility and task confidence). Flow states amplify these pathways by strengthening task experiences. Task enjoyment is the strongest predictor of behavioral intention. These findings inform HCI research on metaverse characteristics to drive technology acceptance through UX mechanisms, yielding design implications for developing LLM-powered avatars for virtual labs.