## 4 Conclusions, Limitations, and Future Directions
This choice is important due to our project's involvement with robotics and socially interactive agents in public service environments in Germany and to advance research in conversational design beyond English. German is particularly suitable because it has more complex grammar and syntax than languages like English. Nevertheless, the diverse nature of languages and the current limitations in the ability of generative artificial intelligence (AI) to handle many other languages, particularly low-resource ones, restrict the broader applicability of our findings. Several areas offer the potential for further development. Enhancing the social robot's ability to distinguish between different speakers (speaker diarization) in group settings could improve its management of multi-party interactions. The integration of advanced voice recognition and turn-taking algorithms may facilitate the generation of more accurate and contextually appropriate responses. It is of the utmost importance to guarantee that these improvements are implemented in a manner that adheres to the strictest standards of user privacy, particularly when handling sensitive voice data in public settings. Furthermore, the integration of LLM-based turn-taking mechanisms has the potential to markedly enhance the social robot's responsiveness by more accurately determining when a user has concluded their utterance. By leveraging the capabilities of large language models, the social robot can better predict the natural end of a user's utterance, allowing for quicker and more fluid transitions in conversation. This not only enhances the robot's ability to engage in real-time interactions but also reduces the likelihood of interrupting the user or causing awkward pauses. Further research could also explore more sophisticated prompting techniques for LLMs to improve the quality and coherence of responses, particularly in multi-turn dialogues where context may change rapidly. These improvements could help maintain the continuity of conversations, especially in complex scenarios. Conceptualization, C.B. and A.M.; methodology: A.M., C.B., O.C. and A.R. ; software, C.B. and O.C. ; validation, C.B., A.M. and O.C. ; formal analysis, C.B. and A.M.; investigation, C.B., A.M., O.C. and A.R. ; resources, C.B., A.M., O.C. and A.R; data curation, A.M. and C.B. ; writing--original draft, C.B., A.M. and O.C. ; writing--review and editing, A.M. and A.R. ; visualization, A.M. and C.B. ; supervision A.R. ; project administration A.M. and A.R. ; funding acquisition, A.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the Federal Ministry of Education and Research of Germany in the framework FH-Kooperativ 2-2019 (project number 13 FH504 KX9). The R&D activities were reviewed and approved by the Ethics Research Committee of TH Koln--University of Applied Sciences (application no. THK-2023-0004) on 29 th of June 2023. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in this work are available on request from the corresponding author. We thank our collaboration partners DB Systel GmbH and Kreisstadt Bergheim. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
A1 Artificial intelligence  
API Application programming interface  
ASR Automatic speech recognition  
CNN Convolutional neural network