Generative AI and Information Representation
Examines how generative AI transforms the creation, personalization, and presentation of information in consumer environments.
Project Description
Generative AI is fundamentally transforming how information is produced and presented in digital environments. Unlike traditional systems that retrieve and rank existing content, generative models actively construct new representations of products, recommendations, and information, reshaping how individuals encounter and interpret available choices.
This project examines how generative AI changes information representation and, in turn, influences decision-making processes. The research focuses on how AI-generated content—such as product descriptions and personalized recommendations—varies in structure, tone, and level of personalization, and how these variations shape perception, evaluation, and choice.
Rather than treating language and recommendation as separate components, the project conceptualizes them as part of a unified information environment in which generative AI jointly determines what information is shown and how it is presented. This perspective enables the study of both representation (e.g., linguistic framing, persuasive style) and exposure (e.g., product composition, personalization) within a common framework.
By integrating linguistic analysis with the study of information exposure and choice, the project aims to understand how generative AI reconfigures the relationship between information, representation, and behavior in consumer contexts.
Collaborators
- Victoria Hang (Texas Tech University)
Research Papers
- Work in progress.