This paper details a novel Bayesian adaptive querying framework that utilizes AI personas to learn user-specific information within limited question budgets. Traditional methods like Computerized Adaptive Testing often struggle with high-dimensional data or "cold-start" scenarios where little is known about a new user or item. This research addresses these gaps by using large language models (LLMs) to generate a dictionary of diverse personas, each with unique response distributions that serve as principled Bayesian priors. By representing a user as a member of this persona dictionary, the system can perform closed-form posterior updates and efficient predictions without expensive computational approximations. Experiments on WorldValuesBench and synthetic data demonstrate that this persona-based approach provides more accurate and interpretable results than classical models. Ultimately, the framework offers a scalable, end-to-end recipe for interactive systems to understand user preferences and behaviors more effectively.