Generative AI-based shopping assistants leveraging large language models (LLMs) offer new ways to interact with product information in online shops. These assistants can deliver highly personalized product descriptions and tailored recommendations by interpreting both explicit consumer inputs and implicit behavioral signals. This new generation of digital assistance introduces a shift from traditional keyword-based navigation and static filters to dynamic, natural language-based interactions that are contextually aware and adaptive. This technological advancement presents intriguing opportunities for enhancing customer engagement, streamlining decision-making, and driving conversions in e-commerce. However, there is a significant gap in our understanding of how GenAIbased shopping assistants influence consumers’ cognitive processes and decision-making behavior—especially in contrast to traditional online shopping interfaces. While established e-commerce platforms rely on known design patterns such as product listings, search functions, and filtering mechanisms, GenAI shifts the experience toward more conversational and potentially persuasive interactions. To address this gap, our study empirically investigates the effects of GenAI-based shopping assistants on consumer behavior within online shopping environments. We conduct a controlled laboratory experiment in Karlsruhe using a two-condition between-subjects design: One group of participants interacts with a traditional online shop interface, while the other uses a GenAI-assisted shopping experience. Participants engage with customized experimental prototypes, and we collect both subjective data (via structured surveys) and objective data (via eye-tracking and behavioral tracking). This approach enables a comprehensive understanding of the cognitive, emotional, and behavioral outcomes associated with each shopping paradigm.