Exploring LLM-powered multi-session human-robot interactions with university students

Abstract

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.