Traditional recommender systems (RSs) generate suggestions by relying on user preferences and item characteristics. However, they do not to properly involve the user in the decision-making process. This gap is particularly evident in Conversational Recommender Systems (CRSs), where existing methods struggle to facilitate meaningful dialogue and dynamic user interactions. To address this limitation, in my Ph.D. project I will ground on the principles of Symbiotic AI (SAI) to propose a novel approach for CRS. Rather than treating users as passive recipients, this approach aims to engage them in an adaptive dialogue based on their preferences, previous interactions, and personal characteristics, thus fostering collaborative decision-making. To achieve this objective, my research unfolds in three phases. First, I will adapt Large Language Models (LLMs) to effectively handle recommendation tasks in a number of different domains, by also introducing knowledge injection techniques. Second, I will develop a CRS that not only provides accurate recommendations but also offers natural language explanations and responds to user queries, thereby promoting transparency and building user trust. Finally, I will consider users’ personal characteristics to personalize the CRS’s response strategy, ensuring adaptive and effective communication in line with SAI principles.