# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces  
In this section, we provide an entry point for understanding approaches to state-of-the-art MUCI research. We outline and discuss the key aspects that characterise the prototypes and frameworks presented.  
In the reviewed literature, the term Multi-User (MU) was used inconsistently, leading to ambiguity in its interpretation. For instance, in some studies, 'Multi-User' referred to an interface designed to sequentially engage with multiple users, rather than supporting simultaneous interactions [6,7]. This variation in terminology not only complicates comparative analysis, but also shows the need for clearer definitions and standardised usage within the field. Therefore, in this article, we refer to the definition according to renowned dictionaries such as Merriam-Webster ([[https://www.merriam-webster.com/dictionary/multiuser--last](https://www.merriam-webster.com/dictionary/multiuser--last)]([https://www.merriam-webster.com/dictionary/multiuser--last](https://www.merriam-webster.com/dictionary/multiuser--last)) visit to website 1 May 2025): [the ability] \"to be used by more than one person simultaneously\". This differs in particular from the field of Multi-Party CUIs where multiple system agents may participate in conversations [8]. As this can be seen as an extension, the focus of our analysis is primarily on scenarios in which multiple users interact with the same CUIs.  
In this regard, research on MUCIs is centred on developing technologies that enable natural interaction between a conversational interface and multiple users simultaneously [9]. MUCIs have a wide range of applications, including virtual assistants, customer service chatbots, educational platforms, and collaborative or social robots. However, the design and implementation of MUCIs involve challenging tasks that cover a broad spectrum of technical aspects. The aim of this paper is thus to give an overview of the challenges and implications of patterns within the literature. For a better understanding of conversation analysis in MU scenarios, we recommend reading the survey of [PERSON] et al. [8].  
One of the primary challenges in MUCIs is the need to support multiple users with different conversational styles and preferences. In contrast to traditional conversational agents, it is not sufficient to just adapt to the preferences of a single user, but to join several of them. To achieve this, it seems to be necessary to develop algorithms that can process and analyse large amounts of conversational data in real-time. This includes developing techniques for detecting and classifying different conversational patterns, such as turn-taking, backchannelling, and topic changes, as well as off-topic talk awareness [10,11].  
Another key challenge in capturing MUCIs is to design systems that are able to adapt to user needs and preferences. To achieve this, user-centred evaluation methods have to be developed that can assess the quality and effectiveness of MUCIs from the users' perspective. This involves collecting user feedback and using it to iteratively enhance the system's design, functionality, and performance [12,13].  
Additionally, MUCIs often require the integration of hardware and software components. For instance, in robotics applications, researchers have to implement algorithms that can control both the robot's hardware and software to enable natural and intuitive interactions with multiple users. This involves developing techniques for speech recognition, facial and gaze recognition, gesture recognition, and natural language processing.  
Despite the challenges, research on MUCIs has made significant progress in recent years, with several novel systems and prototypes being developed and evaluated. In the following, we will present the innovations presented in the reviewed papers divided into four main sections: Application Domains and Examples, User and Dialogue Modelling, Multi-Modality, and Evaluation Methods.