ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Human-Robot Interaction
Volume 12 - 2025 | doi: 10.3389/frobt.2025.1585589
This article is part of the Research TopicInnovative Methods in Social Robot Behavior GenerationView all 4 articles
Exploring LLM-Powered Multi-Session Human-Robot Interactions with University Students
Provisionally accepted
- 1Robotics Research Lab, RPTU Kaiserslautern-Landau, Department of Computer Science, Kaiserslautern, Rhineland-Palatinate, Germany
- 2Center for Cognitive Science, RPTU Kaiserslautern-Landau, Department of Social Science, Kaiserslautern, Germany
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This exploratory study investigates how open-domain, multi-session interactions with a large language model (LLM)-powered social humanoid robot (SHR), EMAH, affect user perceptions and willingness for adoption in a university setting. Thirteen students (5 female, 8 male) engaged with EMAH across four weekly sessions, utilizing a compact open-source LLM (Flan-T5-Large) to facilitate multi-turn conversations. Mixed-method measures were employed, including subjective ratings, behavioral observations, and conversational analyses. Results revealed that perceptions of robot's sociability, agency, and engagement remained stable over time, with engagement sustained despite repeated exposure. While perceived animacy increased with familiarity, disturbance ratings did not significantly decline, suggesting enhanced lifelikeness of SHR without reducing discomfort. Observational data showed a mid-study drop in conversation length and turn-taking, corresponding with technical challenges such as slower response generation and speech recognition errors. Although prior experience with robots weakly correlated with rapport, it did not significantly predict adoption willingness. Overall, the findings highlight the potential for LLM-powered robots to maintain open-domain interactions over time, but also underscore the need for improving technical robustness, adapting conversation strategies by personalization, and managing user expectations to foster long-term social engagement. This work provides actionable insights for advancing humanoid robot deployment in educational environments.
Keywords: Social Robots, human-robot interaction, Large-Language Models, Generative AI, user studies
Received: 04 Mar 2025; Accepted: 16 May 2025.
Copyright: © 2025 Mauliana, Ashok, Czernochowski and Berns. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ashita Ashok, Robotics Research Lab, RPTU Kaiserslautern-Landau, Department of Computer Science, Kaiserslautern, Rhineland-Palatinate, Germany
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