Conversational Information Retrieval (CIR) has attracted growing research interest in recent years, particularly since the emergence of conversational agents that leverage generative AI methods. Within the information retrieval community, a substantial body of research has emerged, particularly centred around initiatives such as the TREC CAsT and iKAT tracks. These tracks have been instrumental in providing datasets that facilitate research in CIR and enable a comparative analysis of various approaches to conversational search. Most of the existing efforts within these tracks have concentrated on the interactive dialogue between the searcher and the CIR system. The task has generally overlooked the potential contribution of User modelling for effective CIR. Recognizing the importance of this dimension, the goal of the workshop is to create a collaborative framework for investigating user modelling and its evaluation in the context of CIR. We invite participants to share their insights and proposals regarding User modelling in CIR, particularly in relation to algorithm design, system personalization, and the methods through which these models can be simulated and assessed. By fostering dialogue and collaboration among researchers and practitioners, we aim to deepen our understanding of how effective User modelling might enhance conversational search experiences and lead to more refined and user-centred retrieval systems.