# A Survey on Multi-User Conversational Interfaces
## 5 Discussion  
The reviewed literature indicates that the majority of MUCIs are highly tailored to specific application contexts. Across the five main application domains (health care, social conversation, service robots, tutoring, and negotiation and conflict resolution), we observed a diverse spectrum of prototype implementations, user roles, system capabilities, and conversational behaviour. However, several common patterns appear across the domains:  
* Role differentiation: Most system developers assign distinct roles to users and system (e.g., patients and health coach [14], customers and assistant [3], learners and tutor [5]), which guide the flow of dialogues and facilitate to manage turn-taking. One reason for this may be that systems which support clear role distributions tend to manage MU input more effectively and ensure balanced participation. * Limited dialogue management: Several publications, particularly in tutoring and conflict resolution, describe conceptual frameworks or systems that only partially implement dialogue logic [25; 32]. As a result, dynamic conversational capabilities remain underdeveloped in contemporary approaches. Instead of employing data-driven approaches, systems often rely on scripted or semi-structured dialogues with minimal adaptability to unexpected user input or spontaneous conversational shifts. However, suitable training data for MUCIs remains scarce across many applications and use cases. * Rigid interaction behaviour: A significant number of interfaces still rely on pre-defined and fixed conversation structures, which implies that the system is processing user input rather sequentially and separately from the group context. In contrast, some systems (e.g., [11; 22]) demonstrate capabilities to handle truly simultaneous input from multiple users with dynamic turn-taking. Nevertheless, this seems necessary to enable natural interactions in group settings, where users may speak out of turn, overlap, or engage in side conversations. Without support for simultaneous input and context-aware coordination, systems risk selecting inappropriate or intrusive responses. * Prototypical systems: Many systems are developed as research prototypes or simulations. Although the findings of their corresponding user studies offer valuable insights, fully functional and deployable real-world MUCIs have yet to be realised. Exceptions are seen in service robotics and personal assistants that operate in constrained environments, such as [64; 65]. Bridging the gap between experimental prototypes and robust real-world MUCIs remains a critical challenge, requiring advances in dialogue management, multi-user conversation analysis, and multi-modal integration. * Multi-Modality: The use of embodiment and multi-modality through robots or virtual avatars has shown great potential for maintaining engagement and managing attention in groups. Systems that integrate multi-modal input and output (e.g., gaze, speech, and gestures) tend to be more robust in turn-taking or user state tracking [24; 49]. However, implementing and synchronising multi-modal channels remains technically demanding and resource-intensive, which may limit scalability. In addition, such systems may consist of hardware setups that are difficult to replicate and transfer to further use-cases. In addition, more research gaps in MUCIs were identified which slow down their development and applicability. One of the most prominent gaps is the absence of reliable turn-taking mechanisms, which are an essential aspect of enabling natural interaction. Only a few systems incorporate robust conversational cues such as gaze, user state, or gestures to coordinate speaker transitions, leading systems to interrupt and confuse users. As a consequence, user frustration and conversation breakdowns become more likely. Without mechanisms to interpret and respond to these non-verbal cues, systems struggle to manage the timing of their responses, resulting in misalignment between system actions and user intentions.