Recent advances in large language models (LLMs) such as GPT-4o offer the potential for enhancing AI-driven robotic interactions, but their effectiveness in mobile tour guidance remains unexplored. This study investigates the differences between human-operated and AI-driven guidance at an aquarium using Teleco, a teleoperated mobile robot, in a real-world field experiment. A total of 277 guidance sessions were collected under two modes: human-operated, where the operator controlled all dialogue, actions, and movement, and AI-driven, where GPT-4o generated responses while the operator only controlled the robot’s actions and movement. Our results indicate that human-operated guidance places greater emphasis on visitor movement, spatial positioning during observation guidance, and empathetic expressions, whereas AI-driven guidance promotes conversational engagement by frequently prompting visitors to ask questions. In addition, we found that user behaviors, including users’ gaze patterns and vocabulary richness, also serve as valuable indicators reflecting their overall experience during guidance interactions. Furthermore, empathetic expression is recognized as the key differentiating factor between the two guidance modes, significantly influencing users’ overall experience.

Developing mobile robots that can provide guidance with high hospitality remains challenging, as it requires the coordination of spoken interaction, physical navigation, and user engagement. To gain insights that contribute to the development of such robots, we conducted a Wizard-of-Oz (WOZ) study using Teleco, a teleoperated humanoid robot, to explore the factors influencing hospitality in mobile robot guidance. Specifically, we enrolled 30 participants as visitors and two trained operators, who teleoperated the Teleco robot to provide mobile guidance to the participants. A total of 120 dialogue sessions were collected, along with evaluations from both the participants and the operators regarding the hospitality of each interaction. To identify the factors that influence hospitality in mobile guidance, we analyzed the collected dialogues from two perspectives: linguistic usage and multimodal robot behaviors. We first clustered system utterances and analyzed the frequency of categories in high- and low-satisfaction dialogues. The results showed that short responses appeared more frequently in high-satisfaction dialogues. Moreover, we observed a general increase in participant satisfaction over successive sessions, along with shifts in linguistic usage, suggesting a mutual adaptation effect between operators and participants. We also conducted a time-series analysis of multimodal robot behaviors to explore behavioral patterns potentially linked to hospitable interactions.