# A Survey on Multi-User Conversational Interfaces
## 5 Discussion
Furthermore, although many systems operate in social or sensitive contexts, there is minimal modelling of in-group dynamics, such as dominance, engagement, or social roles, which have been shown to be fundamental for ensuring fair and inclusive interaction. Another persistent challenge is the limited number of real-world deployments and long-term evaluations. Most systems are evaluated in controlled lab settings or simulations, leaving open questions about how they perform over time in diverse, dynamic environments. For the research community, the use of fragmented evaluation practices with little standardisation across subjective user metrics, task performance measures, or interaction quality at the individual or group level makes it difficult to compare results and build cumulative knowledge. Moreover, ethical considerations and guidelines are also notably absent within literature, for instance in terms of privacy management, fairness in moderation, and bias in user recognition. Finally, research on MUCIs currently lacks modular and domain-independent architectural frameworks and best practices that could support scalable development across application domains. To address these research gaps and overcome the limitations described, there is a growing need for structured, reusable toolkits that can support the systematic development of MUCIs. In addition to the reviewed literature, we identified IrisTK [66] as one of the few toolkits specifically developed for MU interaction. This toolkit stands out as an early and influential attempt to support real-time, multi-user dialogues by combining a modular architecture, statechart-based dialogue management, and multi-modal input and output channels in face-to-face conversations. While IrisTK provides a framework for understanding the architectural and interactional requirements of MUCIs, it reflects the capabilities and design assumptions of its time. Expanding on this foundation, we integrate findings from the reviewed literature to propose a set of best practices for the design, implementation, and evaluation of MUCIs:  
* Application-orientated design: MUCIs should be adapted to the social and physical context in which they operate. This includes considerations such as the number of users, their spatial arrangement, and the interaction setting (e.g., public spaces, homes, classrooms). A good contextual fit improves relevance, usability, and user engagement. * Adaptive dialogue management: Future systems should be able to handle interruptions, overlapping speech, and changing in-group dynamics. Effective dialogue policies require flexibility and, where possible, adaptive mechanisms that can respond to dynamic user behaviours, leaving behind fixed interaction patterns. While data-driven methods like reinforcement learning and transformer-based dialogue models have shown notable success in single-user CUIs, their potential in multi-user scenarios needs further exploration. * Natural turn-taking strategies: As a characteristic of natural group interaction, users often communicate through a combination of speech, gaze, gestures, and facial expressions. MUCIs should integrate multi-modal channels in a coordinated way to manage turn-taking effectively, taking into account both individual and group-level cues. * Standardised evaluation metrics: The evaluation of MUCIs remains fragmented, with a lack of consistent metrics and methodologies. To support meaningful comparison and progress in the field, it is necessary to apply shared evaluation standards that assess subjective user feedback, task performance, and group-level interaction quality. While short-term, lab-based studies provide valuable initial insights, future research should prioritise long-term, real-world deployments to fully capture user behaviour and social interaction over time.