@inproceedings{guo-etal-2025-comparative,
title = "A Comparative Study of Human-operated and {AI}-driven Guidance with a Teleoperated Mobile Robot",
author = "Guo, Ao and
Mochizuki, Shota and
Yamashita, Sanae and
Kenya, Hoshimure and
Baba, Jun and
Higashinaka, Ryuichiro",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-long.87/",
pages = "1615--1627",
ISBN = "979-8-89176-298-5",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T A Comparative Study of Human-operated and AI-driven Guidance with a Teleoperated Mobile Robot
%A Guo, Ao
%A Mochizuki, Shota
%A Yamashita, Sanae
%A Kenya, Hoshimure
%A Baba, Jun
%A Higashinaka, Ryuichiro
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-298-5
%F guo-etal-2025-comparative
%X 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.
%U https://aclanthology.org/2025.ijcnlp-long.87/
%P 1615-1627
Markdown (Informal)
[A Comparative Study of Human-operated and AI-driven Guidance with a Teleoperated Mobile Robot](https://aclanthology.org/2025.ijcnlp-long.87/) (Guo et al., IJCNLP-AACL 2025)
ACL
- Ao Guo, Shota Mochizuki, Sanae Yamashita, Hoshimure Kenya, Jun Baba, and Ryuichiro Higashinaka. 2025. A Comparative Study of Human-operated and AI-driven Guidance with a Teleoperated Mobile Robot. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 1615–1627, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.