@inproceedings{c0d69d2d84c74baa8f7702c8c04e3542,
title = "Social Robots as Creative Partners: Comparing Large Language Models with Wizard-of-Oz in Human-Robot Brainstorming",
abstract = "Social robots can enhance brainstorming. The frequent reliance on Wizard-of-Oz (WoZ) methods hinders the development of autonomous human-robot brainstorming interactions. Large Language Models (LLM) may help address this issue. To compare WoZ- and LLM-controlled robots, a mixed methods experiment (within-subjects, n = 27) was conducted, in which human participants brainstormed with WoZ- and LLM-controlled Furhat robots. Quantitative analysis showed substantial evidence for equality between the two conditions regarding perceived robot creativity and social intelligence; and very strong evidence for a positive relationship between participants{\textquoteright} self-rated creativity and perceived robot creativity and social intelligence, but only when brainstorming with the LLM-controlled robot. Qualitative analysis supported these findings and contributed areas of improvement, most notably, regarding utilizing conversational turn-taking, adaptability, and non-verbal behavior. The findings highlight the potential of LLMs to advance social robots as autonomous creative partners in real-world applications.",
author = "Ethel Pruss and Anita Vrins and Caterina Ceccato and Jos Prinsen and Maryam Alimardani and \{de Wit\}, Jan and \{de Rooij\}, Alwin",
year = "2026",
doi = "10.1007/978-981-95-2398-6\_24",
language = "English",
isbn = "9789819523979",
volume = "3",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "342--355",
editor = "Mariacarla Staffa and John-John Cabibihan and Bruno Siciliano and Silvia Rossi and \{Sam Ge\}, Shuzhi and Leon Bodenhagen and Adriana Tapus and Filippo Cavallo and Laura Fiorini and Marco Matarese and Hongsheng He",
booktitle = "Social Robotics + AI - 17th International Conference, ICSR+AI 2025, Proceedings, Part 3",
}