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.

The proliferation of artificial intelligence (AI) technologies has led to the rise of conversational decision-making support systems, such as dialogue persona systems that provide conversational access to various user segments. For example, product managers can ask personas about features before implementing them, politicians can learn about the needs of local communities through personas, and so on. Nascent research has looked at challenges when users interact with AI personas, but has not framed it as a principal–agent problem, in which the AI represents a persona that itself represents real people in the data. This setting exposes unique interaction challenges that decision makers face when engaging with AI-generated conversational personas, which we examine through a user study with 56 participants using AI-generated conversational personas. Our results indicate seven interaction challenges: (1) Hidden Information, (2) Hidden Personas, (3) Hidden UI, (4) Lack of AI Agency, (5) AI’s Selective Attention, (6) Confusing Distributional Information, and (7) Conversational Cold Start that we conceptually link with agency theory. We discuss how the interaction challenges could be alleviated and suggest directions for future work. • This article explores how decision makers interact with AI-generated conversational personas derived from real survey data. • It conducts a comparative study between conversational personas and traditional profile personas in decision-support contexts. • The study employs a think-aloud user study with 56 participants to capture interaction experiences and challenges. • It identifies seven specific interaction challenges unique to conversational personas that may hinder effective decision making. • The article provides insights and recommendations for designing conversational decision support systems using AI-generated personas.

With nearly two billion Muslims worldwide, designing technology that serves their needs is a significant task, especially during Ramadan, the season of spiritual reflection and change in lifestyle. We present a data-driven approach to generating personas representing differing religious views of Ramadan, based on a large-scale survey conducted in four Muslim-majority countries: Egypt, Indonesia, the United Arab Emirates, and Saudi Arabia. Our approach furthers the representation of underrepresented groups by embedding religious and cultural factors in persona development, aligning with the principles of value-sensitive design. Via correlation analysis, we identified seven distinct groups that reflect diverse practices and values during the Islamic holy month. These findings informed persona creation, capturing variations in spiritual engagement, digital media consumption, and the planning of Ramadan. The resulting personas provide actionable insights for developers designing inclusive applications such as charitable platforms and ecommerce systems aligned with Muslim values. We discuss the implications of employing personas for culturally aware system design and demonstrate how AI-generated personas can support inclusive design in religiously diverse settings. Ultimately, this work contributes to the growing research on human-centered technologies for value-aligned and context-aware systems.

We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, and demographic attributes are included in nearly all generated personas. Researchers use up to 12 prompts in a single study, though most research uses a small number of prompts. Comparison and testing multiple LLMs is rare. More than half of the prompts require the persona output in structured format, such as JSON, and $74 \%$ of the prompts insert data or dynamical variables. We discuss the implications of increased use of computational personas for user representation.

• Shows how GenAI fundamentally transforms existing persona development issues through evolutionary amplification rather than creating entirely new problems, with traditional biases becoming algorithmic discrimination and manual inconsistencies becoming convincing AI hallucinations. • Reveals how traditional limitations manifest differently in GenAI contexts across transparency, fairness, reliability, and control domains, with expert validation showing 60% of challenges are more problematic for GenAIPs than conventional approaches. • Documents how GenAI transforms not just technical challenges but harm distribution, with persona developers facing operational complexity while target user groups bear severe consequences through systematic misrepresentation and exclusion. • Provides evidence that while GenAIPs appear to solve traditional limitations, they transform existing challenges into more complex forms requiring novel validation approaches and human-AI collaboration frameworks for responsible implementation. Generative AI personas (GenAIPs) promise user-centred design efficiency, but their impact on different persona challenges remains unexplored. Inspired by Dijkstra’s classic essay on harmful programming constructs, we analyze twenty challenges in persona development using Human-Centered AI principles. Through literature review and expert survey (n=17), we find that GenAIPs transform rather than eliminate traditional persona challenges. Experts rated all challenges as problematic for GenAIPs (M > 4.0), with the highest concerns for hallucinations (M=5.94), over-sanitization (M=5.82), and lack of standardization (M=5.59). 12 out of 20 challenges are considered more problematic for GenAIPs than conventional personas, particularly bias amplification, validation challenges, and accessibility without expertise. We provide HCAI-grounded guidelines demonstrating that effective GenAIP implementation requires human-AI collaboration rather than automation and prioritizing user welfare over technical efficiency.